[{"access":[{"file_format":"PARQUET","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/668580b4d34e8a8b016cd4e6"}],"access_details":null,"bbox":{"east":-64.3359,"north":48.691,"south":22.5937,"west":-127.9688},"citation":"Stagnitta, T.J., King, T.V., Meyer, M.F., and Wakefield, B.F., 2025, Water temperature of lakes in the Conterminous U.S. using the Landsat 8 analysis ready dataset raster images (updated 2025-02-25): U.S. Geological Survey data release, https://doi.org/10.5066/P13DZ7MP.","creator":[{"creator_email":"tstagnitta@usgs.gov","creator_name":"Tim Stagnitta"}],"creator_project":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"date_created":"3/18/2025","date_updated":"6/3/2026","description":"This data release contains lake and reservoir water surface temperature summary statistics calculated from Landsat 8 Analysis Ready Dataset (ARD) images available within the Conterminous United States (CONUS). All zip files within this data release contain nested directories using .parquet files to store the data. The file example_script_for_using_parquet.R contains example code for using the R [arrow](https://github.com/apache/arrow/) package to open and query the nested .parquet files.","doi_url":"https://doi.org/10.5066/P13DZ7MP","domain":["Hydrology","Water Quality"],"draft":false,"id":"00ccfa87-066d-4c19-be9c-9fc62ea7444a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/668580b4d34e8a8b016cd4e6?f=__disk__ca%2F83%2F58%2Fca8358c711c1be8eaa36496876f0131ac1417795\u0026allowOpen=true"}],"name":"Water Temperature of Lakes in the Conterminous U.S. Using the Landsat 8 Analysis Ready Dataset Raster Images (updated 2025-02-25)","permalink":"/catalog/datasets/00ccfa87-066d-4c19-be9c-9fc62ea7444a/","project_use_history":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"project_using":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"varies","temporal_coverage":"2013 - Present","temporal_frequency":"16 days","update_detail":"append","update_frequency":"21 days","update_type":"Dynamic","variables":["Temperature"],"vars":"Temperature","weight":1},{"access":[{"file_format":"GPKG","name":"portal.edirepository.org","url":"https://portal.edirepository.org/nis/mapbrowse?packageid=edi.854.1"}],"access_details":null,"bbox":{"east":-67,"north":49,"south":25,"west":-125},"citation":"Smith, N.J., Webster,  K.E. , Rodriguez, L.K., Cheruvelil, K.S., Soranno, P.A.,  2021, LAGOS-US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S. ver 1. Environmental Data Initiative, Accessed [YYYY-MM-DD], https://doi.org/10.6073/pasta/e5c2fb8d77467d3f03de4667ac2173ca.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data package, LAGOS-US LOCUS v1.0, is one of the core data modules of the LAGOS-US platform that provides an extensible research-ready platform to study the 479,950 lakes and reservoirs larger than or equal to 1 hectare in the conterminous US. This data module contains information on the location, identifiers, and physical characteristics of lakes and their watersheds. The characteristics in this module include: variables that can be obtained from GIS data such as location and geometry; variables that can be derived using GIS processing such as lake watersheds and their geometry, lake glaciation history, and lake connectivity; and commonly used identifiers from GIS and other data products useful for linking with LAGOS-US. LOCUS is based on a snapshot of the high-resolution National Hydrography Dataset product available at the initiation of the project that provided the basis for locating, identifying, and characterizing the geometry of all lakes in LAGOS-US. The database design that supports the LAGOS-US research platform was created based on several important design features. Lakes are the fundamental unit of consideration, all lakes in the spatial extent must be represented (above a minimum size) and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other 2 core data modules that are part of the LAGOS-US platform: GEO (which includes geospatial ecological context at multiple spatial and temporal scales for lakes and their watersheds) and LIMNO (in situ lake surface-water physical, chemical, and biological measurements through time) that are each found in their own data packages.","doi_url":null,"domain":["Hydrology"],"draft":false,"id":"011115fb-d24c-4b76-9a8d-6537296195c5","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://portal.edirepository.org/nis/metadataviewer?packageid=edi.854.1"}],"name":"LAGOS-US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S.","permalink":"/catalog/datasets/011115fb-d24c-4b76-9a8d-6537296195c5/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"CONUS","spatial_resolution":"1:24,000; 1:100,000; 10 meter","temporal_coverage":"1925 - 2021","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Lakes and lake watersheds"],"vars":"Lakes and lake watersheds","weight":1},{"access":[{"file_format":"SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/63140610d34e36012efa385d"}],"access_details":null,"bbox":{"east":-66.9396,"north":49.3856,"south":24.5183,"west":-124.7559},"citation":"U.S. Geological Survey, 2003, Principal Aquifers of the 48 Conterminous United States, Hawaii, Puerto Rico, and the U.S. Virgin Islands, Hydrologic Atlas, USGS HA-730, U.S. Geological Survey data release, https://doi.org/10.5066/P9Y2HOUJ ","creator":[],"creator_project":[{"id":"DJ60TRJ","name":"NHGF: National Hydrologic Geospatial Fabric"}],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This map layer contains the shallowest principal aquifers of the conterminous United States, Hawaii, Puerto Rico, and the U.S. Virgin Islands, portrayed as polygons.  The map layer was developed as part of the effort to produce the maps published at 1:2,500,000 in the printed series \"Ground Water Atlas of the United States\". The published maps contain base and cultural features not included in these data.  This is a replacement for the July 1998 map layer called Principal Aquifers of the 48 Conterminous United States.","doi_url":"https://doi.org/10.5066/P9Y2HOUJ","domain":["Hydrogeology"],"draft":false,"id":"02a925a6-5266-4040-b0e6-b8d2de56b7ed","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/63140610d34e36012efa385d?f=__disk__77%2F4c%2F7d%2F774c7d2a6f3083f01530581c537ed1c1e9ae4a70\u0026allowOpen=true"}],"name":"Principal Aquifers of the 48 Conterminous United States, Hawaii, Puerto Rico, and the U.S. Virgin Islands, Hydrologic Atlas, USGS HA-730","permalink":"/catalog/datasets/02a925a6-5266-4040-b0e6-b8d2de56b7ed/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:2,500,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Rock_name","Rock_type","Aq_name","Aq_code"],"vars":"Rock_name; Rock_type; Aq_name; Aq_code","weight":1},{"access":[{"file_format":"HDF","name":"nsidc.org","url":"https://nsidc.org/data/mod10c1/versions/61"},{"file_format":"HDF","name":"modis-snow-ice.gsfc.nasa.gov","url":"https://modis-snow-ice.gsfc.nasa.gov/?c=MOD10C1"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Hall, D.K. and Riggs, G.A., 2021, MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG, Version 61 [Data Set Used], Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed [YYYY-MM-DD] at https://doi.org/10.5067/MODIS/MOD10C1.061.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This global Level-3 (L3) data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05 degree (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'MODIS/Terra Snow Cover Daily L3 Global 500m Grid' data set (https://doi.org/10.5067/MODIS/MOD10A1.061). The terms \"Version 61\" and \"Collection 6.1\" are used interchangeably in reference to this release of MODIS data.","doi_url":null,"domain":["Hydrology","Snow"],"draft":false,"id":"03f2803d-7ad8-4306-918c-92b4d3a72af3","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://nsidc.org/sites/default/files/mod10c1-v061-userguide_0.pdf"}],"name":"MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG, Version 6.1","permalink":"/catalog/datasets/03f2803d-7ad8-4306-918c-92b4d3a72af3/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"5 kilometer","temporal_coverage":"2000 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Snow cover"],"vars":"Snow cover","weight":1},{"access":[{"file_format":"TIF","name":"hydro.iis.u-tokyo.ac.jp","url":"http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/index.html"}],"access_details":"Please fill out the \u003ca href='https://goo.gl/forms/6VwfnasNjkYm0Wqu2' target='_blank'\u003eGoogle Form\u003c/a\u003e for registration \u0026 license agreement. The password for downloading is emailed after registration, or please contact the developer (yamadai [at] iis.u-tokyo.ac.jp) to get access.","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Yamazaki, D., Ikeshima,D., Sosa, J., Bates, P.D., Allen, G.H., Pavelsky, T.M., 2019, MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets Water Resources Research, vol.55, pp.5053-5073, accessed [YYYY-MM-DD] at https://doi.org/10.1029/2019WR024873","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"High-resolution raster hydrography maps are a fundamental data source for many geoscience applications. MERIT Hydro is a new global flow direction map at 3 arc-second resolution (~90 m at the equator) derived from the latest elevation data (MERIT DEM) and water body datasets (G1WBM, GSWO, and OpenStreetMap). This dataset uses a new algorithm to extract river networks near-automatically by separating actual inland basins from dummy depressions caused by the errors in input elevation data. After a minimum amount of hand-editing, the constructed hydrography map shows good agreement with existing quality-controlled river network datasets in terms of flow accumulation area and river basin shape. The location of river streamlines was realistically aligned with existing satellite-based global river channel data. Relative error in the drainage area was smaller than 0.05 for 90% of GRDC gauges, confirming the accuracy of the delineated global river networks. Discrepancies in flow accumulation area were found mostly in arid river basins containing depressions that are occasionally connected at high water levels and thus resulting in uncertain watershed boundaries. MERIT Hydro improves on existing global hydrography datasets in terms of spatial coverage (between N90 and S60) and representation of small streams, mainly due to increased availability of high-quality baseline geospatial datasets. The new flow direction and flow accumulation maps, along with accompanying supplementary layers on hydrologically adjusted elevation and channel width, will advance geoscience studies related to river hydrology at both global and local scales.","doi_url":null,"domain":["Hydrology"],"draft":false,"id":"05199160-2947-404d-bac7-ba6aed53a96e","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR024873"}],"name":"MERIT Hydro: Global Hydrography Datasets","permalink":"/catalog/datasets/05199160-2947-404d-bac7-ba6aed53a96e/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"Global","spatial_resolution":"3 arcsecond","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Elevation","Flow direction","Hydrosheds","River-width","Upstream drainage area"],"vars":"Elevation; Flow direction; Hydrosheds; River-width; Upstream drainage area","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5f8e091d82ce3241879215a7"}],"access_details":null,"bbox":{"east":-65,"north":50,"south":23,"west":-127},"citation":"Schmadel, N.M., and Harvey, J.W., 2020, NHD-RC: Extension of NHDPlus Version 2.1 with high-resolution river corridor attributes: U.S. Geological Survey data release, https://doi.org/10.5066/P9TCH5J7.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This hybrid medium-resolution national hydrography dataset with river corridor attributes (NHD-RC) for the conterminous United States (CONUS) was created by merging lentic and lotic attributes from the [high-resolution NHDPlus](/datasets/fd9b5188-5336-4cfd-9c7b-70680001eef0) into the [medium-resolution NHDPlus Version 2.1](/datasets/8a60b6b4-d785-4265-af99-cd1870ea7928). NHD-RC includes attributes from an additional 5.4 million small pond features and 5 million kilometers of small streams beyond the approximately 123,000 lentic waterbodies and 4 million kilometers of larger streams and rivers accounted for NHDPlus Version 2.1. This hybrid approach permitted the use of the many attributes that have been linked to NHD by others, including land cover and dam inventories, to provide four distinct classes of medium- and high-resolution lentic waterbodies: (1) lakes, (2) reservoirs, (3) historic small ponds that were not intensively managed during the past century, and (4) managed small ponds that were constructed for water supply, farm use, or another management purpose. Small ponds located in upland positions without a defined and mapped flowline are also included. This hybrid dataset further advances the basis for improved and more comprehensive integrated modeling and analyses of river corridors.","doi_url":null,"domain":["Stream Characteristics","Hydrology"],"draft":false,"id":"055eadd5-54dc-425f-a8d8-8e0424bdeb1a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5f8e091d82ce3241879215a7?f=__disk__d8%2F13%2F63%2Fd81363ad06acfe575c21ca87e5b1c04d5f07f0c8\u0026transform=1\u0026allowOpen=true"}],"name":"NHD-RC: Extension of NHDPlus Version 2.1 with high-resolution river corridor attributes","permalink":"/catalog/datasets/055eadd5-54dc-425f-a8d8-8e0424bdeb1a/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:24,000; 1:100,000","temporal_coverage":"2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Lentic and lotic attributes for NHDPlus Version 2.1 Flowlines"],"vars":"Lentic and lotic attributes for NHDPlus Version 2.1 Flowlines","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6414b3f9d34eb496d1ceb5ae"}],"bbox":{"east":-63.1667,"north":54.8088,"south":21.0821,"west":-129.7712},"citation":"Wieczorek, M.E., Staub, L.E., and Wnuk, K.C., Hafen, K.C., 2023, Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portions of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins (ver. 2.0, July 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P98IG8LO.","creator":[{"creator_email":"mewieczo@usgs.gov","creator_name":"Michael E Wieczorek"}],"creator_project":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"date_created":"2/25/2026","date_updated":"6/3/2026","description":"These tabular data sets represent daily climate metrics processed from 4 kilometer GridMET data (Abatzoglou, 2013) for the period of record 1980 through 2020 and compiled for three spatial components: select United States Geological Survey stream gage basins (Staub and Wieczorek, 2023), 2) individual reach flowline catchments of the Upper and Lower Colorado (ucol) portions of the Geospatial Fabric for the National Hydrologic Model, version 1.1 (nhgfv11, Bock and others, 2020 ), and 3) the upstream watersheds of each individual nhgfv11 flowline catchments. Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021). Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values were computed using the published python software package Xstrm (Wieferich and others). The following daily climate metrics were processed: minimum and maximum temperature (Celsius), precipitation (millimeters), potential evapotranspiration (millimeters), reference evapotranspiration (millimeters), and 5 day standardized precipitation evapotranspiration index (unitless).","doi_url":"https://doi.org/10.5066/P98IG8LO","domain":["Climate","Hydrology"],"draft":false,"id":"0699d46a-c087-4ab8-b79d-1d4c40b66522","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6414b3f9d34eb496d1ceb5ae?f=__disk__e8%2F0a%2Fd0%2Fe80ad0e2fc3ea16519eacd0e6dc51a9ade25545f\u0026allowOpen=true"}],"name":"Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portions of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins: Daily Climate Metrics Derived from gridMET, 1980 - 2020","permalink":"/catalog/datasets/0699d46a-c087-4ab8-b79d-1d4c40b66522/","project_use_history":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"project_using":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1980 - 2020","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Date","station_no","maximum temperature","minimum temperature","precipitation"],"vars":"Date; station_no; maximum temperature; minimum temperature; precipitation","weight":1},{"access":[{"file_format":"NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/640f60d7d34e254fd352e1e7"}],"access_details":null,"bbox":{"east":-74.5567153058843,"north":40.2255329654062,"south":38.5788844202186,"west":-75.6329061075375},"citation":"Cook, S.E. and Warner, J.C., 2023, U.S. Geological Survey simulations of 3D-hydrodynamics in Delaware Bay (2016, 2018, 2021) to improve understanding of the mechanisms driving salinity intrusion: U.S. Geological Survey data release, https://doi.org/10.5066/P9ANH82L","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST Warner and others, 2019; Warner and others, 2010) model was used to simulate three-dimensional hydrodynamics and waves to study salinity intrusion in the Delaware Bay estuary for 2016, 2018, 2021. Salinity intrusion in coastal systems is due in part to extreme events like drought or low-pressure storms and longer-term sea level rise, threatening economic infrastructure and ecological health. Along the eastern seaboard of the United States, approximately 13 million people rely on the water resources of the Delaware River basin, which is actively managed to suppress the salt front (or ~0.52 daily averaged psu line) through river discharge targets. However, river discharge is only part of the story. The other mechanisms controlling salinity intrusion include tidal motions on daily and spring-neap cycles, bathymetric and topographic features, and meteorological events. It is the interaction of these mechanisms that ultimately determines the distribution of salt in an estuary, particularly during periods of low discharge. The purpose of this study is to examine the mechanisms controlling the location of the salt front in the Delaware Bay estuary using a calibrated three-dimensional hydrodynamic model, the Coupled Ocean Atmosphere Wave and Sediment Transport (COAWST; v. 3.6) modeling system. The model was forced with tides, subtidal water levels, bulk atmospheric conditions for each year 2016, 2018, and 2021.","doi_url":"https://doi.org/10.5066/P9ANH82L","domain":["Water Quality"],"draft":false,"id":"06fd0d2a-820a-4fa6-be10-8d9f6ccfb02e","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/640f645cd34e254fd352e202?f=__disk__55%2F14%2Ffc%2F5514fc02a8d13a9ba35b04bbcba84769c572ea65\u0026allowOpen=true"}],"name":"U.S. Geological Survey simulations of 3D-hydrodynamics in Delaware Bay (2016, 2018, 2021) to improve understanding of the mechanisms driving salinity intrusion","permalink":"/catalog/datasets/06fd0d2a-820a-4fa6-be10-8d9f6ccfb02e/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Delaware Bay","spatial_resolution":"varies","temporal_coverage":"2016; 2018; 2021","temporal_frequency":"hourly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["COAWST model inputs and outputs"],"vars":"COAWST model inputs and outputs","weight":1},{"access":[{"file_format":"HDF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-myd13q1-006"}],"access_details":"Dataset Deprecated: This dataset is no longer available.\u003cbr\u003eThe MYD13Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD13Q1 Version 6.1 (https://www.earthdata.nasa.gov/data/catalog/lpcloud-myd13q1-061)\u003cbr\u003e\u003cbr\u003eAn Earthdata Login is required before users can download data or use selected tools that comprise NASA's Earth Observing System Data and Information System (EOSDIS).","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Didan, K., 2015, MYD13Q1 MODIS/Aqua Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 [Data set used]. NASA EOSDIS Land Processes DAAC. Accessed 2021-09-05 at https://doi.org/10.5067/MODIS/MYD13Q1.006","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The MYD13Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD13Q1 Version 6.1\u003cbr\u003e\u003cbr\u003eThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.","doi_url":null,"domain":["Land Cover"],"draft":false,"id":"086f2e3e-e889-4d69-96f4-be9ac1bbfe80","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf"}],"name":"Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6","permalink":"/catalog/datasets/086f2e3e-e889-4d69-96f4-be9ac1bbfe80/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"250 meter","temporal_coverage":"2002 - Present","temporal_frequency":"16 days","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["EVI/NDVI"],"vars":"EVI/NDVI","weight":1},{"access":[{"file_format":"CSV","name":"epa.gov","url":"https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys"}],"access_details":null,"bbox":{"east":-64,"north":71.8,"south":-14,"west":-179},"citation":"U.S. Environmental Protection Agency, [insert the year the survey report was published], National Aquatic Resource Surveys, [insert the survey name and survey year] (data and metadata files). Available from U.S. EPA web page, accessed [YYYY-MM-DD] at https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys. ","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The National Rivers and Streams Assessment (NRSA) is a collaborative survey that provides information on the ecological condition of the nation's rivers and streams and the key stressors that affect them, both on a national and an ecoregional scale. ","doi_url":null,"domain":["Stream Characteristics","Hydrology"],"draft":false,"id":"08a642c9-bd70-4586-aa65-ddd4ff22898a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"National Aquatic Resource Surveys","permalink":"/catalog/datasets/08a642c9-bd70-4586-aa65-ddd4ff22898a/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"EPA","spatial_extent":"United States and territories","spatial_resolution":"unknown","temporal_coverage":"2004 - 2019","temporal_frequency":"5 years","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Site data","Morphology","Water quality","Stream velocity, Habitat","Biology","Sediment","Bank stability","Macroinvertebrate"],"vars":"Site data; Morphology; Water quality; Stream velocity, Habitat; Biology; Sediment; Bank stability; Macroinvertebrate","weight":1},{"access":[{"file_format":"TXT; SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5cbf5150e4b09b8c0b700df3"}],"access_details":null,"bbox":{"east":-72.5108,"north":50.1537,"south":27.6603,"west":-114.8483},"citation":"Saad, D.A., and Robertson, D.M., 2020, SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Midwestern United States, 2012 Base Year: U.S. Geological Survey data release, https://doi.org/10.5066/P93QMXC9.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The U.S. Geological Survey's (USGS) SPAtially Referenced Regression On Watershed attributes (SPARROW) model was used to aid in the interpretation of monitoring data and simulate streamflow and water-quality conditions in streams across the Midwest Region of the United States. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based water-quality load estimates. This USGS data release includes input and output files associated with 2012 SPARROW simulations of streamflow, total nitrogen, total phosphorus and suspended-sediment load in streams of the Midwest. Model construction, calibration and results are described in Robertson and Saad (2019, https://doi.org/10.3133/sir20195114).","doi_url":"https://doi.org/10.5066/P93QMXC9","domain":["Hydrology"],"draft":false,"id":"09e67ebf-a31f-418f-9f71-06e0912526a4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[{"id":"8e649d48-f8fc-4cc6-a147-5645b54693eb","rel_type":"IsSourceOf"}],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5cbf5150e4b09b8c0b700df3?f=__disk__a6%2Ffb%2Fd6%2Fa6fbd6f6bcce874109d2e989d1d4d5a67c33cd49\u0026transform=1\u0026allowOpen=true"},{"name":"Documentation","url":"https://pubs.usgs.gov/publication/sir20195114"}],"name":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Midwestern United States, 2012 Base Year","permalink":"/catalog/datasets/09e67ebf-a31f-418f-9f71-06e0912526a4/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Midwestern United States","spatial_resolution":"1:100,000","temporal_coverage":"1999 - 2014","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Streamflow","Total nitrogen loads","Total phosphorus loads","Suspended-sediment loads"],"vars":"Streamflow; Total nitrogen loads; Total phosphorus loads; Suspended-sediment loads","weight":1},{"access":[{"file_format":"NC","name":"gleam.eu","url":"https://www.gleam.eu/"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"[1] Martens, B., Miralles, D.G., Lievens, H., van der Schalie, R., de Jeu, R.A.M., Fernandez-Prieto, D., Beck, H.E., Dorigo, W.A., and Verhoest, 2017, N.E.C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geoscientific Model Development, 10, 1903-1925, https://doi.org/10.5194/gmd-10-1903-2017\u003cbr\u003e[2] Miralles, D.G., Holmes, T.R.H., de Jeu, R.A.M., Gash, J.H., Meesters, A.G.C.A., Dolman, A.J., 2011, Global land-surface evaporation estimated from satellite-based observations, Hydrology and Earth System Sciences, 15, 453-469, https://doi.org/10.5194/hess-15-453-2011","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms that separately estimate the different components of land evaporation (often referred to as 'evapotranspiration'): transpiration, bare-soil evaporation, interception loss, open-water evaporation and sublimation. Additionally, GLEAM provides surface and root-zone soil moisture, potential evaporation and evaporative stress conditions. The rationale of the method is to maximize the recovery of information on evaporation contained in current satellite observations of climatic and environmental variables.\u003cbr\u003eThe Priestley and Taylor equation in GLEAM calculates potential evaporation based on observations of surface net radiation and near-surface air temperature. Estimates of potential evaporation for the land fractions of bare soil, tall canopy and short canopy are converted into actual evaporation using a multiplicative evaporative stress factor based on observations of microwave Vegetation Optical Depth (VOD) and estimates of root-zone soil moisture. The latter are calculated using a multi-layer running-water balance. To try to correct for random forcing errors, observations of surface soil moisture are also assimilated into the soil profile. Interception loss is calculated separately in GLEAM using a Gash analytical model. Finally, estimates of actual evaporation for water bodies and regions covered by ice and/or snow are based on a modified Priestley and Taylor equation.","doi_url":null,"domain":["Hydrology"],"draft":false,"id":"0a5230fc-3c2f-47bf-8017-646c605052d8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://gmd.copernicus.org/articles/10/1903/2017/"}],"name":"Global Land Evaporation Amsterdam Model (GLEAM)","permalink":"/catalog/datasets/0a5230fc-3c2f-47bf-8017-646c605052d8/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"ESA; Academic Institution(s)","spatial_extent":"Global","spatial_resolution":"0.25 degrees","temporal_coverage":"1980 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"annual","update_type":"Dynamic","variables":["Actual Evaporation","Soil Evaporation","Interception Loss","Potential Evaporation","Snow Sublimation","Transpiration","Open-Water Evaporation","Evaporative Stress","Root-Zone Soil Moisture","Surface Soil Moisture"],"vars":"Actual Evaporation; Soil Evaporation; Interception Loss; Potential Evaporation; Snow Sublimation; Transpiration; Open-Water Evaporation; Evaporative Stress; Root-Zone Soil Moisture; Surface Soil Moisture","weight":1},{"access":[{"file_format":"GDB","name":"fs.usda.gov","url":"https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0030"}],"access_details":null,"bbox":{"east":-65.3233,"north":51.6218,"south":23.2281,"west":-127.9887},"citation":"Abood, S. A., Spencer, L., Wieczorek, M., 2022, U.S. Forest Service national riparian areas base map for the conterminous United States in 2019: Forest Service Research Data Archive, Accessed [YYYY-MM-DD], https://doi.org/10.2737/RDS-2019-0030","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This metadata record describes 10-meter raster riparian areas for 50-year flood heights for the conterminous United States in 2019. Fifty-year flood heights were estimated using U.S. Geological Survey (USGS) stream gage information. NHDPlus version 2.1 was used as the hydrologic framework to delineate riparian areas. The U.S. Fish and Wildlife Service's National Wetland Inventory and USGS 10-meter digital elevation models were also used in processing these data. \u003cbr\u003e These data were created to estimate 50-year flood height riparian areas to support statistical analysis, map display, and model parameterization.","doi_url":"https://doi.org/10.2737/RDS-2019-0030","domain":["Hydrology"],"draft":false,"id":"0b24ffc7-68b3-40de-ac30-d5135e7b9fe8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.fs.usda.gov/rds/archive/products/RDS-2019-0030/_metadata_RDS-2019-0030.html"}],"name":"National riparian areas base map for the conterminous United States in 2019","permalink":"/catalog/datasets/0b24ffc7-68b3-40de-ac30-d5135e7b9fe8/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USDA; USFS","spatial_extent":"CONUS","spatial_resolution":"10 meter","temporal_coverage":"2019","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Riparian area"],"vars":"Riparian area","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/644ff709d34e45f6ddcf00d0"}],"bbox":{"east":-63.1667,"north":54.8088,"south":21.0821,"west":-129.7712},"citation":"Wieczorek, M.E., Staub, L.E., and Wnuk, K.C., Hafen, K.C., 2023, Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portions of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins (ver. 2.0, July 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P98IG8LO.","creator":[{"creator_email":"mewieczo@usgs.gov","creator_name":"Michael E Wieczorek"}],"creator_project":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"date_created":"2/25/2026","date_updated":"6/3/2026","description":"These tabular data sets represent daily climate metrics processed from 4 kilometer snow water equivalent (SWE) raster data in millimeters (Broxton and others, 2019) for the period of record 10-01-1981 through 09-30-2020 and compiled for three spatial components: select United States Geological Survey stream gage basins (Staub and Wieczorek, 2023), 2) individual reach flowline catchments of the Upper and Lower Colorado (ucol) portions of the Geospatial Fabric for the National Hydrologic Model, version 1.1 (nhgfv11, Bock and others, 2020 ), and 3) the upstream watersheds of each individual nhgfv11 flowline catchments. Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021). Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values were computed using the published python software package Xstrm (Wieferich and others).","doi_url":"https://doi.org/10.5066/P98IG8LO","domain":["Climate","Hydrology"],"draft":false,"id":"0b5551ef-a6a0-4ca3-a2cc-9d52ec0122be","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/644ff709d34e45f6ddcf00d0?f=__disk__8a%2Ff5%2Fa9%2F8af5a9697d01530dfae18b83862a1820667c204f\u0026allowOpen=true"}],"name":"Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portions of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins: Daily Snow Water Equivalent, 1981 - 2020","permalink":"/catalog/datasets/0b5551ef-a6a0-4ca3-a2cc-9d52ec0122be/","project_use_history":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"project_using":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1981 - 2020","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Date","station_no","snow water equivalent"],"vars":"Date; station_no; snow water equivalent","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/66833671d34e57e93663d8a5"}],"bbox":{"east":-64.1602,"north":75.8021,"south":17.1408,"west":-178.418},"citation":"Martinez, A.J., Reddy, J.E., and Padilla, J.A., 2025, Crosswalk table between 12-digit hydrologic unit code (HUC12) and hydrologic region boundaries: U.S. Geological Survey data release, https://doi.org/10.5066/P13XTCTM.","creator":[],"creator_project":[],"date_created":"1/15/2026","date_updated":"6/3/2026","description":"This data release contains a crosswalk between subwatersheds (12-digit hydrologic unit codes; hereafter, HUC12s) and hydrologic regions (sometimes called \"Van Metre regions\"). This crosswalk allows for data at the HUC12 scale to be summarized regionally. Hydrologic regions are boundaries of hydrologically distinct areas modified from hydrologic subregions (4-digit Hydrologic units; HUC4s) defined by Qi and Mason (2023; https://doi.org/10.5066/P98194QR) for use in Van Meter et al. (2020; https://doi.org/10.1007/s10661-020-08403-1). These hydrologic regions should not be confused with 2-digit hydrologic unit codes (HUC2 or HU2), also referred to as \"hydroregions\" or \"HydroRegions.\" Although they are similar in number and size, they represent different concepts: HUC2s denote drainage basins of major rivers, while the hydrologic regions defined by Van Metre et al. (2020) are areas with similar hydrology and water availability concerns that were originally developed to help inform selection of basins for more in-depth sampling, analysis, and modeling. For comparative purposes, we further grouped the hydrologic regions into four CONUS aggregated hydrologic regions based on location and shared water-availability characteristics and challenges (Northeast through Midwest, Southeast, High Plains, and Western). The HUC12 boundaries used are those made available in the Mainstems data release (https://doi.org/10.5066/P92U7ZUT), which are modified from the stable NHDPlusV2 snapshot of the Watershed Boundary Dataset.","doi_url":"https://doi.org/10.5066/P13XTCTM","domain":["Hydrology"],"draft":false,"id":"0bbcf7eb-9538-41c3-832f-1d669d31c9dd","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/66833671d34e57e93663d8a5?f=__disk__94%2F71%2Fa4%2F9471a483818a040e05f294db52a6e95fbf16bc82\u0026allowOpen=true"}],"name":"Crosswalk table between 12-digit hydrologic unit code (HUC12) and hydrologic region boundaries","permalink":"/catalog/datasets/0bbcf7eb-9538-41c3-832f-1d669d31c9dd/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"1:100,000","temporal_coverage":"2025","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["HUC12","HUC8","Region","Region_nam","AggRegion","AggRegion_nam","Area_sqkm"],"vars":"HUC12; HUC8; Region; Region_nam; AggRegion; AggRegion_nam; Area_sqkm","weight":1},{"access":[{"file_format":"NC","name":"gmao.gsfc.nasa.gov","url":"https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/"},{"file_format":"NC","name":"disc.gsfc.nasa.gov","url":"https://disc.gsfc.nasa.gov/datasets?project=MERRA-2"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Gelaro, R., McCarty, W., Suarez, M.J., Todling, R., Molod, A., Takacs, L., Randles, C.A., Darmenov, A., Bosilovich, M.G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, C., Buchard, V., Conaty, A., da Silva, A.M., Gu, W., Kim, G., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J.E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S.D., Sienkiewicz, M., and Zhao, B., 2017, The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Journal of Climate 30, 14: 5419-5454, https://doi.org/10.1175/JCLI-D-16-0758.1","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA's Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA's terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams and converged to a single near-real-time stream in mid-2015.","doi_url":"https://doi.org/10.1175/JCLI-D-16-0758.1","domain":["Climate"],"draft":false,"id":"0c4399b2-3324-495e-8aff-b56986e21375","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"MERRA-2: The second Modern-Era Retrospective analysis for Research and Applications","permalink":"/catalog/datasets/0c4399b2-3324-495e-8aff-b56986e21375/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"50 kilometer","temporal_coverage":"1980 - Present","temporal_frequency":"hourly","update_detail":"append","update_frequency":"monthly","update_type":"Dynamic","variables":["MERRA2 climate forcings"],"vars":"MERRA2 climate forcings","weight":1},{"access":[{"file_format":"CSV","name":"epa.gov","url":"https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys"}],"access_details":null,"bbox":{"east":-65.3233,"north":51.6218,"south":23.2281,"west":-127.9887},"citation":"U.S. Environmental Protection Agency, [insert the year the survey report was published], National Aquatic Resource Surveys, [insert the survey name and survey year] (data and metadata files). Available from U.S. EPA web page, accessed [YYYY-MM-DD] at https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Wadeable Streams Assessment (WSA) is a first-ever statistically-valid survey of the biological condition of small streams throughout the U.S. EPA worked with the states to conduct the assessment in 2004-2005. 1,392 sites were selected at random to represent the condition of all streams in regions that share similar ecological characteristics. Participants used the same standardized methods at all sites, to ensure results that are comparable across the nation.","doi_url":null,"domain":["Stream Characteristics"],"draft":false,"id":"0e261121-ab61-4447-b88d-b3a022ee12f0","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.epa.gov/sites/default/files/2014-10/documents/2007_5_16_streamsurvey_wsa_assessment_may2007.pdf"}],"name":"Wadeable Streams Assessment","permalink":"/catalog/datasets/0e261121-ab61-4447-b88d-b3a022ee12f0/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"EPA","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1999 - 2022","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Site data","Morphology","Water quality, Velocity","Habitat","Biology","Sediment","Bank stability","cover","Macroinvertebrate","monitoring"],"vars":"Site data; Morphology; Water quality, Velocity; Habitat; Biology; Sediment; Bank stability; cover; Macroinvertebrate; monitoring","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6852ef7fd4be023cfee776ee"}],"bbox":{"east":-63.1667,"north":54.8088,"south":21.0821,"west":-129.7712},"citation":"Wieczorek, M.E., Staub, L.E., and Wnuk, K.C., Hafen, K.C., 2023, Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portions of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins (ver. 2.0, July 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P98IG8LO.","creator":[{"creator_email":"mewieczo@usgs.gov","creator_name":"Michael E Wieczorek"}],"creator_project":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"date_created":"2/25/2026","date_updated":"6/3/2026","description":"These tabular datasets represent retrospective forecasts of average minimum temperature (degrees Celsius), maximum temperature (degrees Celsius), and total precipitation (millimeters) within three-hour forecasting periods derived from the Global Ensemble Forecast System (GEFS) reforecast dataset (Hamill and others, 2013). Data are averaged across 7 day forecast horizons for each day within the period of record spanning 2000 through 2019. The data were compiled for two spatial components: 1) select United States Geological Survey streamgage basins (Staub and others, 2023), 2) individual reach flowline catchments of the Upper Colorado (ucol) portion of the Geospatial Fabric for the National Hydrologic Model, version 1.1 (Bock and others, 2020). Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021).","doi_url":"https://doi.org/10.5066/P98IG8LO","domain":["Climate","Hydrology"],"draft":false,"id":"10fd96b5-bcf3-4711-b5d5-67b055b3ca24","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6852ef7fd4be023cfee776ee?f=__disk__6f%2Fc0%2Fd3%2F6fc0d3a130522aa75c5f77a031a73b8d8db4eb37\u0026allowOpen=true"}],"name":"Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portion of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins: Daily Meteorological Forecast Metrics Derived from the Global Ensemble Forecast System (GEFS), 2000 - 2019","permalink":"/catalog/datasets/10fd96b5-bcf3-4711-b5d5-67b055b3ca24/","project_use_history":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"project_using":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"2000 - 2019","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Date","station_no","mean maximum temperature","mean minimum temperature","mean total precipitation"],"vars":"Date; station_no; mean maximum temperature; mean minimum temperature; mean total precipitation","weight":1},{"access":[{"file_format":"SHP","name":"zenodo.org","url":"https://zenodo.org/records/18624542"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Allen, G.H. and Pavelsky, T.M., 2018,Global extent of rivers and streams, Science, v. 361, pp. 585-588, https://doi.org/10.1126/science.aat0636","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The GRWL Database was built to characterize the global coverage of rivers and streams. The GRWL Database is the first global compilation of river planform geometry at a constant-frequency discharge. A global database of 3,693 gages were used to determine the months when the rivers were commly near mean discharge; 7,376 Landsat THM, ETM+, and OLI scenes were capture during those months. Image processing techniques were used to classify rivers, measure their location, width, and braiding index. The dataset was validated using in situ river width measurements collected by USGS and the Water Survey of Canada.","doi_url":"https://doi.org/10.5281/zenodo.18624542","domain":["Hydrology","Stream Characteristics"],"draft":false,"id":"120270a9-e0b6-42d8-9b1f-17db852fd2b4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.1126/science.aat0636"}],"name":"Global River Widths from Landsat (GRWL) Database","permalink":"/catalog/datasets/120270a9-e0b6-42d8-9b1f-17db852fd2b4/","project_use_history":[],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"Global","spatial_resolution":"30 meter","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["River centerlines","River width","Braiding index","Elevation"],"vars":"width_min; width_med; width_mean; width_max; width_sd; lakeflag; nSegPx; Shape_Length","weight":1},{"access":[{"file_format":"SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/4f4e4773e4b07f02db47e241"}],"access_details":null,"bbox":{"east":-63,"north":74,"south":17,"west":-180},"citation":"Crawford, S., Whelan, G., Infante, D.M., Blackhart, K., Daniel, W.M., Fuller, P.L., Birdsong, T., Wieferich, D.J., McClees-Funinan, R., Stedman, S.M., Herreman, K., and Ruhl, P.,  2016, Through a Fish's Eye: The Status of Fish Habitats in the United States 2015. National Fish Habitat Partnership. accessed [YYYY-MM-DD] at http://assessment.fishhabitat.org/","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"These data summarize the results of a continuing unprecedented nationwide assessment of human effects on fish habitat in the rivers and estuaries of the United States and provides a basis for comparing fish habitat condition on a national scale. This national assessment assigns watersheds and estuaries a risk of current habitat degradation score ranging from very low to very high. These results allow comparison of aquatic habitats across the nation and within 14 sub-regions. Data on stream fishes were provided by many federal and state agencies and organizations from around the country. Besides fish data, many different human landscape factors were assembled and used to characterize habitat condition. These factors include: urban and agricultural land use; intensity of different types of mining activities; impervious surfaces; estimates of nutrient loading to streams; estimates of water withdrawals; major point sources of water pollution; and measures describing fragmentation of rivers by dams. After acquiring data, variables were attributed to a national stream coverage (NDH+v1) for use in assessment. Landscape information was aggregated throughout network catchments and buffers, resulting in data available in four spatial scales for use in assessment.","doi_url":null,"domain":["Stream Characteristics"],"draft":false,"id":"1274a2a4-0d78-4885-a496-3ded503f8cdb","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/4f4e4773e4b07f02db47e241?f=__disk__e0%2F56%2F4c%2Fe0564c2d986a4ebf50410128496fe1eb3ef85433\u0026allowOpen=true"}],"name":"National Fish Habitat Partnership Data System","permalink":"/catalog/datasets/1274a2a4-0d78-4885-a496-3ded503f8cdb/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; AK; HI","spatial_resolution":"1:100,000","temporal_coverage":"2010; 2015","temporal_frequency":"5 years","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Fish habitat"],"vars":"Fish habitat","weight":1},{"access":[{"file_format":"GPKG; CSV; GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/63cb38b2d34e06fef14f40ad"}],"access_details":null,"bbox":{"east":-65.2148,"north":49.838,"south":24.2069,"west":-126.2109},"citation":"Blodgett, D.L., 2023, Mainstem Rivers of the Conterminous United States (ver. 2.0, February 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P92U7ZUT.","creator":[{"creator_email":"dblodgett@usgs.gov","creator_name":"David Blodgett"}],"creator_project":[{"id":"DJ60TRJ","name":"NHGF: National Hydrologic Geospatial Fabric"}],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Mainstem rivers are the backbone of a connected network of hydrologic units that cover the landscape. A mainstem connects a headwater source area to an outlet. This data release identifies the same mainstem paths in hydrographic datasets for the conterminous US.\u003cbr\u003eThe Mainstems dataset includes cross walks between mainstem identifiers and several hydrographic datasets. These cross walk tables do not include geometry. It also includes a summary of all mainstems with headwater and outlet identifiers from other datasets for those mainstems that could be matched. The summary table has highly simplified geometry and should be used for visualization purposes only. Any analysis should rely on source dataset geometry.\u003cbr\u003eAs a convenience, a geodatabase of 12-digit hydrologic unit code watershed outlets is included as points with attributes that indicate the \"event\" linear reference along the NHDPlusV2 network as well as how the 12 digit hydrologic units nest within the 10, 8, 6, 4, and 2 digit hydrologic unit system. This file is based on the stable NHDPlusV2 snapshot of the Watershed Boundary Dataset.\u003cbr\u003e\u003cbr\u003e","doi_url":"https://doi.org/10.5066/P92U7ZUT","domain":["Hydrology"],"draft":false,"id":"127f2846-2167-41e7-a16a-cee5e0a03e0f","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[{"id":"6389d98c-7de8-4dc7-938c-f98da1063455","rel_type":"IsSourceOf"},{"id":"c7f2109c-f75e-4c98-9b1b-765923a48978","rel_type":"IsSourceOf"}],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/63cb38b2d34e06fef14f40ad?f=__disk__d2%2Ffa%2F14%2Fd2fa1412c05a27d9eca078190dc5909a3b2510b3\u0026transform=1\u0026allowOpen=true"}],"name":"Mainstem Rivers of the Conterminous United States (ver. 2.0, February 2023)","permalink":"/catalog/datasets/127f2846-2167-41e7-a16a-cee5e0a03e0f/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2023","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["ERF1_2","LevelPathI","HUC12","TOHUC","intersected_LevelPathI","corrected_LevelPathI","head_HUC12","outlet_HUC12","trib_intersect","trib_no_intersect","headwater_error","outlet_GNIS_ID","outlet_GNIS_NAME","head_nhdpv2_COMID","outlet_nhdpv2_COMID","head_2020HUC12","outlet_2020HUC12","outlet_nhdpv2HUC12","head_nhdpv2_HUC12","outlet_rf1ID","head_rf1ID","outlet_nhdpv1_COMID","head_nhdpv1_COMID","totdasqkm","level","length","v1_comid","COMID","REACHCODE","REACH_meas","offset","HUC2","toHUC2","HUC4","toHUC4","HUC6","toHUC6","HUC8","toHUC8","HUC10","toHUC10"],"vars":"ERF1_2; LevelPathI; HUC12; TOHUC; intersected_LevelPathI; corrected_LevelPathI; head_HUC12; outlet_HUC12; trib_intersect; trib_no_intersect; headwater_error; outlet_GNIS_ID; outlet_GNIS_NAME; head_nhdpv2_COMID; outlet_nhdpv2_COMID; head_2020HUC12; outlet_2020HUC12; outlet_nhdpv2HUC12; head_nhdpv2_HUC12; outlet_rf1ID; head_rf1ID; outlet_nhdpv1_COMID; head_nhdpv1_COMID; totdasqkm; level; length; v1_comid; COMID; REACHCODE; REACH_meas; offset; HUC2; toHUC2; HUC4; toHUC4; HUC6; toHUC6; HUC8; toHUC8; HUC10; toHUC10","weight":1},{"access":[{"file_format":"NC","name":"disc.gsfc.nasa.gov","url":"https://disc.gsfc.nasa.gov/datasets/NLDAS_VIC0125_H_2.0/summary"}],"access_details":null,"bbox":{"east":-67,"north":53,"south":25,"west":-125},"citation":"NLDAS project (2021), NLDAS VIC Land Surface Model L4 Hourly 0.125 x 0.125 degree V2.0, Edited by David M. Mocko, NASA/GSFC/HSL, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/45T7K120BJ2S","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Variable Infiltration Capacity (VIC) land surface model output driven by the NLDAS, Phase 2. A semi-distributed, grid-based hydrologic model estimating surface and subsurface fluxes and stores. The VIC model was developed at the University of Washington and Princeton University as a macroscale, semi-distributed, grid-based, hydrologic model (Liang and others, 1994; Wood and others, 1997). The full water and energy balance modes of VIC were used for NLDAS-2. VIC uses three soil layers, with thicknesses that vary spatially. The root zone depends on the vegetation type and its root distribution, and can span all three soil layers. The VIC model includes a two-layer energy balance snow model (Cherkauer and others, 2003). Details about the NLDAS-2 configuration of the VIC LSM can be found in Xia and others (2012). The version of the VIC model for the NLDAS-2 VIC data available from the NASA GES DISC is VIC-4.0.3; this version of the VIC model is the same as used in Sheffield and others (2003).","doi_url":"https://doi.org/10.5067/45T7K120BJ2S","domain":["Climate","Snow","Land Cover","Hydrology"],"draft":false,"id":"1310a3fd-a66b-4602-b220-b94569acecbf","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.1029/2011JD016048"}],"name":"NLDAS VIC Land Surface Model L4 Hourly 0.125 x 0.125 degree V2.0 (NLDAS_VIC0125_H)","permalink":"/catalog/datasets/1310a3fd-a66b-4602-b220-b94569acecbf/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"CONUS","spatial_resolution":"0.125 degrees","temporal_coverage":"1979 - Present","temporal_frequency":"hourly","update_detail":"append and modify","update_frequency":"4 days","update_type":"Dynamic","variables":["Total evapotranspiration","Water equivalent of accumulated snow depth","Snow depth","Albedo","Soil temperature","Soil moisture content","Vegetation","Snow melt","Net shortwave radiation flux (surface)","Net longwave radiation flux (surface)","Latent heat flux","Sensible heat flux","Potential latent heat flux (potential evaporation)","Average surface skin temperature","Liquid soil moisture content (non-frozen)","Ground heat flux","Frozen precipitation (for example, snowfall)","Liquid precipitation (rainfall)","Aerodynamic conductance","Canopy conductance","Leaf area index","Sublimation (evaporation from snow)","Direct evaporation from bare soil","Canopy water evaporation","Minimal stomatal resistance","Downward shortwave radiation flux","Downward longwave radiation flux","Moisture availability","Transpiration","Plant canopy surface water","Snow phase-change heat flux","Subsurface runoff (baseflow)","Surface runoff (non-infiltrating)","Snow cover","Solar parameter in canopy conductance","Temperature parameter in canopy conductance","Humidity parameter in canopy conductance","Soil moisture parameter in canopy conductance","Root zone soil moisture content","Relative soil moisture availability control factor"],"vars":"Total evapotranspiration; Water equivalent of accumulated snow depth; Snow depth; Albedo; Soil temperature; Soil moisture content; Vegetation; Snow melt; Net shortwave radiation flux (surface); Net longwave radiation flux (surface); Latent heat flux; Sensible heat flux; Potential latent heat flux (potential evaporation); Average surface skin temperature; Liquid soil moisture content (non-frozen); Ground heat flux; Frozen precipitation (for example, snowfall); Liquid precipitation (rainfall); Aerodynamic conductance; Canopy conductance; Leaf area index; Sublimation (evaporation from snow); Direct evaporation from bare soil; Canopy water evaporation; Minimal stomatal resistance; Downward shortwave radiation flux; Downward longwave radiation flux; Moisture availability; Transpiration; Plant canopy surface water; Snow phase-change heat flux; Subsurface runoff (baseflow); Surface runoff (non-infiltrating); Snow cover; Solar parameter in canopy conductance; Temperature parameter in canopy conductance; Humidity parameter in canopy conductance; Soil moisture parameter in canopy conductance; Root zone soil moisture content; Relative soil moisture availability control factor","weight":1},{"access":[{"file_format":"GDB","name":"nrcs.usda.gov","url":"https://www.nrcs.usda.gov/resources/data-and-reports/gridded-soil-survey-geographic-gssurgo-database"}],"bbox":{"east":-65,"north":50,"south":24,"west":-127},"citation":"Soil Survey Staff. Gridded Soil Survey Geographic (gSSURGO) Database for the Conterminous United States: United States Department of Agriculture, Natural Resources Conservation Service, accessed [YYYY-MM-DD] at https://gdg.sc.egov.usda.gov/.","creator":[{"creator_email":"Soils-Webmaster@usda.gov","creator_name":"Soils Webmaster"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Gridded SSURGO (gSSURGO) is similar to the standard USDA-NRCS Soil Survey Geographic (SSURGO) Database product but in the format of an Environmental Systems Research Institute, Inc. (ESRI®) file geodatabase. A file geodatabase has the capacity to store much more data and thus greater spatial extents than the traditional SSURGO product. This makes it possible to offer these data in statewide or even conterminous United States (CONUS) tiles. gSSURGO contains all of the original soil attribute tables in SSURGO. All spatial data are stored within the geodatabase instead of externally as separate shapefiles. Both SSURGO and gSSURGO are considered products of the National Cooperative Soil Survey (NCSS) partnership.\u003cbr\u003eThe gridded SSURGO (gSSURGO) dataset was created for use in national, regional, and statewide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, including the National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer (CDL), and the National Elevation Dataset (NED).\u003cbr\u003eThe gSSURGO Database is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO generally has the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging the traditional vector-based SSURGO digital map data and tabular data into statewide extents, adding a statewide gridded map layer derived from the vector layer, and adding a new value-added look up table (Valu1) containing “ready to map” attributes. The gridded map layer is a file geodatabase raster in an ArcGIS file geodatabase. The raster and vector map data have a statewide extent. The raster map data have a 10-meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link the raster cells and polygons to attribute tables. **Due to file size, the raster layer for the conterminous United States is only available in a 30-meter resolution.**","doi_url":null,"domain":["Soils"],"draft":false,"id":"13ff01c3-ab45-4523-a972-4fadd73d7839","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.nrcs.usda.gov/resources/data-and-reports/ssurgo/stats2go-metadata"}],"name":"Gridded Soil Survey Geographic (gSSURGO) Database for the Conterminous United States","permalink":"/catalog/datasets/13ff01c3-ab45-4523-a972-4fadd73d7839/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NRCS","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Soil characteristics"],"vars":"Soil characteristics","weight":1},{"access":[{"file_format":"HDF","name":"lpdaac.usgs.gov","url":"https://lpdaac.usgs.gov/products/vnp43ia4v001/"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Schaaf, C., Wang, Z., Zhang, X., Strahler, A., 2018, VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 500m SIN Grid V001 [Data set]: NASA EOSDIS Land Processes Distributed Active Archive Center, accessed [YYYY-MM-DD] at https://doi.org/10.5067/VIIRS/VNP43IA4.001","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Forward processing for VIIRS Version 1 Bidirectional Reflectance Distribution Function (BRDF) and Albedo products (VNP43s) was paused on June 17, 2024.  It resumed on June 25, 2024 and will continue until Version 2 reprocessing is fully complete over the historical mission period.\u003cbr\u003eThe NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\u003cbr\u003eThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).\u003cbr\u003eThe VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.","doi_url":"https://doi.org/10.5067/VIIRS/VNP43IA4.001","domain":["Climate"],"draft":false,"id":"145fd6ff-f934-400c-a4ae-b5ac05daaa85","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/products/vnp43ia4v001/"}],"name":"VNP43IA4 v001: VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid","permalink":"/catalog/datasets/145fd6ff-f934-400c-a4ae-b5ac05daaa85/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"500 meter","temporal_coverage":"2012 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Albedo","Nadir reflectance"],"vars":"Albedo; Nadir reflectance","weight":1},{"access":[{"file_format":"GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5b4e34dfe4b06a6dd180272e"}],"access_details":null,"bbox":{"east":-65.3748244399,"north":51.5120922457,"south":23.2443912389,"west":-127.857240873},"citation":"Moore, R., Belitz, K., Arnold, T.L., Sharpe, J.B., and Starn, J.J., 2019, National Multi Order Hydrologic Position (MOHP) Predictor Data for Groundwater and Groundwater-Quality Modeling: U.S. Geological Survey data release, https://doi.org/10.5066/P9HLU4YY.","creator":[],"creator_project":[],"date_created":"5/5/2024","date_updated":"6/3/2026","description":"Multi Order Hydrologic Position (MOHP) raster datasets: Distance from Stream to Divide (DSD) and Lateral Position (LP) have been produced nationally for the 48 contiguous United States at 30-meter and 90-meter cell resolution for stream orders 1 through 9.  These data are available for testing as predictor variables for various regional and national groundwater-flow and groundwater-quality statistical models.  For quicker downloads, these data are available here nationally at a 90-meter cell resolution, as well as on the National Spatial Data Infrastructure (NSDI) Node at the higher 30-meter cell resolution","doi_url":"https://doi.org/10.5066/P9HLU4YY","domain":["Hydrogeology","Hydrology","Stream Characteristics"],"draft":false,"id":"14f3b8c3-52fe-469d-a96d-48407a0873e7","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5b4e34dfe4b06a6dd180272e?f=__disk__eb%2F66%2Fd9%2Feb66d9dcf53ae8c7728f020576174583b1e2d79b\u0026allowOpen=true"}],"name":"National Multi Order Hydrologic Position (MOHP) Predictor Data for Groundwater and Groundwater-Quality Modeling","permalink":"/catalog/datasets/14f3b8c3-52fe-469d-a96d-48407a0873e7/","project_use_history":[{"id":"DJ50U93","name":"NEHF: National Extent Hydrogeologic Framework"}],"project_using":[{"id":"DJ50U93","name":"NEHF: National Extent Hydrogeologic Framework"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"90 meter","temporal_coverage":"2018","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Lateral Position (LP)","Distance-from-stream-to-divide (DSD)","COMID","REACHCODE","FROMMEAS","TOMEAS","BURNLENKM","InRPU","GridCode","Catchment","Burn","StreamCalc","Hydroseq","LevelPathI","Pathlength","TerminalPa","DnLevel","UpLevelPat","UpHydroseq","DnLevelPat","DnMinorHyd","DnDrainCou","DnHydroseq","NEW_ORDER","LEV_ORD","HYDSEQSTRM","MIN_HYDORD","DNLEV_ORD","Country","LevelPathI","COMID","AREASQKM","FTYPE","FCODE","ONOFFNET"],"vars":"Lateral Position (LP); Distance-from-stream-to-divide (DSD); COMID; REACHCODE; FROMMEAS; TOMEAS; BURNLENKM; InRPU; GridCode; Catchment; Burn; StreamCalc; Hydroseq; LevelPathI; Pathlength; TerminalPa; DnLevel; UpLevelPat; UpHydroseq; DnLevelPat; DnMinorHyd; DnDrainCou; DnHydroseq; NEW_ORDER; LEV_ORD; HYDSEQSTRM; MIN_HYDORD; DNLEV_ORD; Country; LevelPathI; COMID; AREASQKM; FTYPE; FCODE; ONOFFNET","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/59d3c73de4b05fe04cc3d1d1"}],"access_details":null,"bbox":{"east":-66.269531247357,"north":50.099440986147,"south":24.577099743367,"west":-126.21093749496},"citation":"Sohl, T, Reker, R., Bouchard, M., Sayler, K., Dornbierer, J., Wika, S., Quenzer, R., and Friesz, A., 2018, Modeled historical land use and land cover for the conterminous United States: 1938-1992: U.S. Geological Survey data release, https://doi.org/10.5066/F7KK99RR.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Modeled Historical Land Use and Land Cover for the Conterminous United States: 1938-1992. Researchers at the US Geological Survey have used a wide range of historical data sources and a spatially explicit modeling framework to model spatially explicit historical LULC change in the conterminous United States from 1992 back to 1938. Annual LULC maps were produced at 250-m resolution, with 14 LULC classes.","doi_url":null,"domain":["Land Cover"],"draft":false,"id":"1727ad68-c7c0-45c9-bc45-4e0f1512c429","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/59d3c73de4b05fe04cc3d1d1?f=__disk__8f%2F07%2Faa%2F8f07aa05983db3421f7bcdae28c3471e6870a9c2\u0026transform=1\u0026allowOpen=true"}],"name":"Modeled Historical Land Use and Land Cover for the Conterminous United States: 1938-1992","permalink":"/catalog/datasets/1727ad68-c7c0-45c9-bc45-4e0f1512c429/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"250 meter","temporal_coverage":"1938 - 1992","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Open Water","Urban/Developed","Mining","Barren","Deciduous Forest","Evergreen Forest","Mixed Forest","Grassland","Shrubland","Cultivated Cropland","Hay/Pasture","Herbaceous Wetland","Woody Wetland","Perennial Ice/Snow"],"vars":"Open Water; Urban/Developed; Mining; Barren; Deciduous Forest; Evergreen Forest; Mixed Forest; Grassland; Shrubland; Cultivated Cropland; Hay/Pasture; Herbaceous Wetland; Woody Wetland; Perennial Ice/Snow","weight":1},{"access":[{"file_format":"GPKG; NC; SHP","name":"zenodo.org","url":"https://zenodo.org/records/14727521"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Elizabeth H. Altenau, Tamlin M. Pavelsky, Michael T. Durand, Xiao Yang, Renato P. d. M. Frasson, \u0026 Liam Bendezu. (2025). SWOT River Database (SWORD) (Version v17) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14727521","creator":[],"creator_project":[],"date_created":"5/2/2025","date_updated":"6/3/2026","description":"The Surface Water and Ocean Topography (SWOT) satellite mission, launched in 2022, will vastly expand observations of river water surface elevation (WSE), width, and slope. In order to facilitate a wide range of new analyses with flexibility, the SWOT mission will provide a range of relevant data products. One product the SWOT mission will provide are river vector products stored in shapefile format for each SWOT overpass (JPL Internal Document, 2020b). The SWOT vector data products will be most broadly useful if they allow multitemporal analysis of river nodes and reaches covering the same river areas. Doing so requires defining SWOT reaches and nodes a priori, so that SWOT data can be assigned to them. The SWOt River Database (SWORD) combines multiple global river- and satellite-related datasets to define the nodes and reaches that will constitute SWOT river vector data products. SWORD provides high-resolution river nodes (200 m) and reaches (~10 km) in shapefile and netCDF formats with attached hydrologic variables (WSE, width, slope, etc.) as well as a consistent topological system for global rivers 30 m wide and greater.","doi_url":"https://doi.org/10.5281/zenodo.14727521","domain":["Hydrology","Stream Characteristics"],"draft":false,"id":"17d38a28-419a-4615-a882-690fdc7e7fa8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.1029/2021WR030054"}],"name":"SWOT River Database (SWORD), v17","permalink":"/catalog/datasets/17d38a28-419a-4615-a882-690fdc7e7fa8/","project_use_history":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"project_using":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA; Academic institution(s)","spatial_extent":"Global","spatial_resolution":"30 meter","temporal_coverage":"2025","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Longitude","Latitude","Node ID","Node length","Reach ID","Reach length","Average water surface elevation","WSE variance","Average width","Width variance","Maximum width","Maximum flow accumulation","Maximum number of channels","Mode of the number of channels","Type of obstruction","GROD ID","HydroFALLS ID","Distance from river outlet","Type identifier","GRWL water body identifier","Manual addition flag for public GRWL centerlines","Length of the meander","Sinuosity","Reach average slope","Number of nodes","Number of upstream reaches","Number of downstream reaches","Reach IDs of upstream neighbors","Reach IDs of downstream neighbors","Maximum number of SWOT passes during orbit cycle","SWOT orbit tracks intersecting during orbit cycle","River names","Flag for type of update applied","Flag indicating if tributary is entering"],"vars":"Longitude; Latitude; Node ID; Node length; Reach ID; Reach length; Average water surface elevation; WSE variance; Average width; Width variance; Maximum width; Maximum flow accumulation; Maximum number of channels; Mode of the number of channels; Type of obstruction; GROD ID; HydroFALLS ID; Distance from river outlet; Type identifier; GRWL water body identifier; Manual addition flag for public GRWL centerlines; Length of the meander; Sinuosity; Reach average slope; Number of nodes; Number of upstream reaches; Number of downstream reaches; Reach IDs of upstream neighbors; Reach IDs of downstream neighbors; Maximum number of SWOT passes during orbit cycle; SWOT orbit tracks intersecting during orbit cycle; River names; Flag for type of update applied; Flag indicating if tributary is entering","weight":1},{"access":[{"file_format":"HDF","name":"nsidc.org","url":"https://nsidc.org/data/smap/tools"}],"access_details":null,"bbox":{"east":180,"north":85,"south":-85,"west":-180},"citation":"Reichle, R., De Lannoy, G., Koster, R.D., Crow, W.T., Kimball, J.S., Liu, Q., and Bechtold, M., 2022, SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 7 [Data Set]: NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed [YYYY-MM-DD] at https://doi.org/10.5067/EVKPQZ4AFC4D","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: 1) SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D), 2) SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3), 3) SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.","doi_url":null,"domain":["Soils","Hydrology"],"draft":false,"id":"1aca575e-c6a2-492d-8058-93d1d0c33e4a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://nsidc.org/api/dataset/metadata/v2/oai?verb=GetRecord\u0026metadataPrefix=dif\u0026identifier=SPL4SMGP.007"},{"name":"Documentation","url":"https://nsidc.org/sites/default/files/documents/user-guide/multi_spl4smau-v007-userguide.pdf"}],"name":"SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 7","permalink":"/catalog/datasets/1aca575e-c6a2-492d-8058-93d1d0c33e4a/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"9 kilomter","temporal_coverage":"2015 - Present","temporal_frequency":"3 hours","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Root zone soil moisture","Surface soil moisture"],"vars":"Root zone soil moisture; Surface soil moisture","weight":1},{"access":[{"file_format":"TIF; NC","name":"figshare.com","url":"https://figshare.com/articles/dataset/AgTile-US/11825742"}],"access_details":null,"bbox":{"east":-64,"north":50,"south":22,"west":-127},"citation":"Valayamkunnath, P., Barlage, M., Chen, F., Gochis, D.J., and Franz, K.J., 2020, Mapping of 30-meter resolution tile-drained croplands using a geospatial modeling approach: Scientific Data, v. 7, no. 1, https://doi.org/10.1038/s41597-020-00596-x","creator":[{"creator_email":"prasanth@ucar.edu","creator_name":"Prasanth Valayamkunnath"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This is a 30-m resolution tile drainage map of the most-likely tile-drained area of the CONUS (AgTile-US) from county-level tile drainage census using a geospatial model that uses soil drainage information and topographic slope as inputs. Validation of AgTile-US with 16000 ground truth points indicated 86.03% accuracy at the CONUS-scale. Over the heavily tile-drained midwestern regions of the U.S., the accuracy ranges from 82.7% to 93.6%. These data can be used to study and model the hydrologic and water quality responses of tile drainage and to enhance streamflow forecasting in tile drainage dominant regions.","doi_url":"https://doi.org/10.1038/s41597-020-00596-x","domain":["Hydrology","Land Cover"],"draft":false,"id":"1b683e73-9730-40f3-8b3c-c87093191748","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.1038/s41597-020-00596-x"}],"name":"Mapping of 30-meter resolution tile-drained croplands using a geospatial modeling approach","permalink":"/catalog/datasets/1b683e73-9730-40f3-8b3c-c87093191748/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NCAR","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"2017","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Tile-drained agricultural land","Undrained agricultural land"],"vars":"Tile-drained agricultural land; Undrained agricultural land","weight":1},{"access":[{"file_format":"CSV; NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/63750ed1d34ed907bf6ceb1a"}],"access_details":null,"bbox":{"east":-64.56485,"north":18.516167,"south":17.6741,"west":-67.271733},"citation":"Swain, E.D., 2022, PRMS simulator used to assess rainfall, runoff, and river flow for the National Hydrologic Model (NHM) Puerto Rico: U.S. Geological Survey data release, https://doi.org/10.5066/P9IMU17O.","creator":[],"creator_project":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The National Hydrologic Model (NHM) is a modeling framework which has been applied to the continental United States through the Precipitation Runoff Modeling System (PRMS). The PRMS model of Puerto Rico extends the NHM and allows the simulation of rainfall-driven hydrologic conditions in the Commonwealth. Calibration of the NHM Puerto Rico model involved an initial manual calibration to understand the important processes and develop a basic representation of the hydrology. This is followed by an automated calibration procedure using the Let Us CAlibrate (LUCA) multi-objective function model calibration tool. A four-step procedure is used in Luca to separately calibrate parameters for solar radiation, evapotranspiration, low flows, and peak flows. The calibrated model is used for comparison with field-estimated flows and to examine coastal flows during Hurricane Maria. This USGS data release contains all of the input and output files for the simulations described in the associated journal article (https://doi.org/10.3390/hydrology9110205).","doi_url":null,"domain":["Hydrology","Soils"],"draft":false,"id":"1ca0b2f4-e8e8-4ae3-b3e5-d73aa0194e6a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/63750ed1d34ed907bf6ceb1a?f=__disk__ea%2F8c%2Fab%2Fea8cabc0f2fdc1347be5eabb0063df4ef3006bd4\u0026allowOpen=true"},{"name":"Documentation","url":"https://doi.org/10.3390/hydrology9110205"}],"name":"NHM-PRMS PR (Daymet v3, GF v1.0): PRMS simulator used to assess rainfall, runoff, and river flow for the National Hydrologic Model (NHM) Puerto Rico","permalink":"/catalog/datasets/1ca0b2f4-e8e8-4ae3-b3e5-d73aa0194e6a/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"PR","spatial_resolution":"1:100,000","temporal_coverage":"1981 - 2017","temporal_frequency":"monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Actual evapotranspiration (potential evapotranspiration)","Groundwater recharge","Runoff","Precipitation","Snowpack or swe","Soil moisture","Air temperature","Groundwater runoff","Subsurface runoff","Surface runoff"],"vars":"Actual evapotranspiration (potential evapotranspiration); Groundwater recharge; Runoff; Precipitation; Snowpack or swe; Soil moisture; Air temperature; Groundwater runoff; Subsurface runoff; Surface runoff","weight":1},{"access":[{"file_format":"XLSX","name":"globaldamwatch.org","url":"https://www.globaldamwatch.org/directory"},{"file_format":"GDB; SHP","name":"figshare.com","url":"https://figshare.com/articles/dataset/Global_Dam_Watch_database_version_1_0/25988293"}],"access_details":"To access the latest data at globaldamwatch.org, click on the link to the GDW database. User has to submit an organization name and may continue without signing in to get to the Global Dam Watch database.","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Lehner, B., Liermann, C.R., Revenga, C., Vorosmarty, C., Fekete, B., Crouzet, P., Doll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rodel, R., Sindorf, N., and Wisser, D., 2011, High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management: Frontiers in Ecology and the Environment, v. 9, no. 9, p. 494-502,  https://doi.org/10.1890/100125","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Global Reservoir and Dam Database (GRanD) v1.1 is a product of the Global Water System Project, which initiated a collaborative international effort to collate existing dam and reservoir datasets with the aim of providing a single, geographically explicit and reliable database for the scientific community.\u003cbr\u003eThe initial version 1.1 of GRanD contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 km3. Data was assembled from numerous sources by eleven participating institutions, and the dataset is managed by McGill University. Though GRanD has undergone an update, GRanD v1.1 will remain available to support existing and future research.\u003cbr\u003eGRanD v1.3 augments v1.1 with an additional 458 reservoirs and associated dams to bring the total number of records to 7320. Most of the added reservoirs were constructed between 2000 and 2016; global reservoir storage is increased by 666.5 km3. Updates have also been made to attribute data originally developed for GRanD v1.1; this includes a new column to indicate whether and when a dam has been removed.  Access the data from our directory page (https://www.globaldamwatch.org/directory).","doi_url":"https://doi.org/10.1890/100125","domain":["Infrastructure"],"draft":false,"id":"1cc31a34-415e-4de0-a401-8e03a710498c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.globaldamwatch.org/"}],"name":"Global Reservoir and Dam Database v 1.3 (GRanD)","permalink":"/catalog/datasets/1cc31a34-415e-4de0-a401-8e03a710498c/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Global Dam Watch","spatial_extent":"Global","spatial_resolution":"30 meter","temporal_coverage":"1984 - 2024","temporal_frequency":"NA","update_detail":"append","update_frequency":"irregular","update_type":"Dynamic","variables":["GDW_ID","Res_name","Dam_name","Alt_name","Dam_type","Lake_ctrl","River","Alt_river","Main_basin","Sub_basin","Country","Sec_cntry","Admin_unit","Sec_admin","Near_city","Alt_city","Year_dam","Pre_year","Year_src","Alt_year","Rem_year","Timeline","Year_txt","Dam_hgt_m","Alt_hgt_m","Dam_len_m","Alt_len_m","Area_skm","Area_poly","Area_rep","Area_max","Area_min","Cap_mcm","Cap_max","Cap_rep","Cap_min","Depth_m","Dis_avg_ls","Dor_pc","Elev_masl","Catch_skm","Catch_rep","Power_mw","Data_info","Use_irri","Use_elec","Use_supp","Use_fcon","Use_recr","Use_navi","Use_fish","Use_pcon","Use_live","Use_othr","Main_use","Multi_dams","Comments","Url","Quality","Editor","Long_riv","Lat_riv","Long_dam","Lat_dam","Orig_src","Poly_src","GRanD_ID","Hyriv_ID","Instream","Hylak_ID","Hybas_L12"],"vars":"GDW_ID; Res_name; Dam_name; Alt_name; Dam_type; Lake_ctrl; River; Alt_river; Main_basin; Sub_basin; Country; Sec_cntry; Admin_unit; Sec_admin; Near_city; Alt_city; Year_dam; Pre_year; Year_src; Alt_year; Rem_year; Timeline; Year_txt; Dam_hgt_m; Alt_hgt_m; Dam_len_m; Alt_len_m; Area_skm; Area_poly; Area_rep; Area_max; Area_min; Cap_mcm; Cap_max; Cap_rep; Cap_min; Depth_m; Dis_avg_ls; Dor_pc; Elev_masl; Catch_skm; Catch_rep; Power_mw; Data_info; Use_irri; Use_elec; Use_supp; Use_fcon; Use_recr; Use_navi; Use_fish; Use_pcon; Use_live; Use_othr; Main_use; Multi_dams; Comments; Url; Quality; Editor; Long_riv; Lat_riv; Long_dam; Lat_dam; Orig_src; Poly_src; GRanD_ID; Hyriv_ID; Instream; Hylak_ID; Hybas_L12","weight":1},{"access":[{"file_format":"TIF","name":"cec.org","url":"http://www.cec.org/north-american-environmental-atlas/land-cover-30m-2020/"}],"access_details":null,"bbox":{"east":-50,"north":84,"south":14,"west":-175},"citation":"Commission for Environmental Cooperation (CEC), 2023, 2020 Land Cover of North America at 30 meters. North American Land Change Monitoring System, Canada Centre for Remote Sensing (CCRS), U.S. Geological Survey (USGS), Comision Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO), Comision Nacional Forestal (CONAFOR), Instituto Nacional de Estadistica y Geografia (INEGI) Edition 2.0, Raster digital data [30-m], accessed [YYYY-MM-DD], http://www.cec.org/north-american-environmental-atlas/land-cover-30m-2020/","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The 2020 North American Land Cover 30-meter dataset was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia), National Commission for the Knowledge and Use of the Biodiversity (Comision Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comision Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries.\u003cbr\u003eThe general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country's specific requirements.\u003cbr\u003eThis 30-meter dataset of North American Land Cover reflects land cover information for 2020 from Mexico and Canada, 2019 over the conterminous United States and 2021 over Alaska. Each country developed its own classification method to identify Land Cover classes and then provided an input layer to produce a continental Land Cover map across North America. Canada, Mexico, and the United States developed their own 30-meter land cover products; see specific sections on data generation below.\u003cbr\u003eThe main inputs for image classification were 30-meter Landsat 8 Collection 2 Level 1 data in the three countries (Canada, the United States and Mexico). Image selection processes and reduction to specific spectral bands varied among the countries due to study-site-specific requirements. While Canada selected most images from the year 2020 with a few from 2019 and 2021, the Conterminous United States employed mainly images from 2019, while Alaska land cover maps are mainly based on the use of images from 2021. The land cover map for Mexico was based on land cover change detection between 2015 and 2020 Mexico Landsat 8 mosaics.\u003cbr\u003eIn order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by CONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each country chose their own approaches, ancillary data, and land cover mapping methodologies to create national datasets. This North America dataset was produced by combining the national land cover datasets. The integration of the three national products merged four Land Cover map sections, Alaska, Canada, the conterminous United States and Mexico.","doi_url":null,"domain":["Land Cover"],"draft":false,"id":"1d0230b6-b4d9-4927-b62d-b9903d497d3b","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Land cover 2020 (Landsat)","permalink":"/catalog/datasets/1d0230b6-b4d9-4927-b62d-b9903d497d3b/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"CEC","spatial_extent":"North America","spatial_resolution":"30 meter","temporal_coverage":"2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Temperate or sub-polar needleleaf forest","Sub-polar taiga needleleaf forest","Tropical or sub-tropical broadleaf evergreen forest","Tropical or sub-tropical broadleaf deciduous forest","Temperate or sub-polar broadleaf deciduous forest","Mixed Forest","Tropical or sub-tropical shrubland","Temperate or sub-polar shrubland","Tropical or sub-tropical grassland","Temperate or sub-polar grassland","Sub-polar or polar shrubland-lichen-moss","Sub-polar or polar grassland-lichen-moss","Sub-polar or polar barren-lichen-moss","Wetland","Cropland","Barren lands","Urban","Water","Snow and Ice"],"vars":"Temperate or sub-polar needleleaf forest; Sub-polar taiga needleleaf forest; Tropical or sub-tropical broadleaf evergreen forest; Tropical or sub-tropical broadleaf deciduous forest; Temperate or sub-polar broadleaf deciduous forest; Mixed Forest; Tropical or sub-tropical shrubland; Temperate or sub-polar shrubland; Tropical or sub-tropical grassland; Temperate or sub-polar grassland; Sub-polar or polar shrubland-lichen-moss; Sub-polar or polar grassland-lichen-moss; Sub-polar or polar barren-lichen-moss; Wetland; Cropland; Barren lands; Urban; Water; Snow and Ice","weight":1},{"access":[{"file_format":"HDF","name":"nsidc.org","url":"https://nsidc.org/data/mod10a1f/versions/61"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Hall, D.K. and Riggs, G.A., 2020, MODIS/Terra CGF Snow Cover Daily L3 Global 500m SIN Grid, Version 61. [dataset used], Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed [YYYY-MM-DD] at https://doi.org/10.5067/MODIS/MOD10A1F.061","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This global Level-3 data set (MOD10A1F) provides daily cloud-free snow cover derived from the MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid data set (MOD10A1). Grid cells in MOD10A1 which are obscured by cloud cover are filled by retaining clear-sky views of the surface from previous days. A separate parameter is provided which tracks the number of days in each cell since the last clear-sky observation. Each data granule contains a 10 degree x 10 degree tile projected to the 500 m sinusoidal grid. MODIS Terra daily Normalized Daily Snow Index and Albedo.","doi_url":"https://doi.org/10.5067/MODIS/MOD10A1F.061","domain":["Snow"],"draft":false,"id":"1d439945-3cee-4cb3-b41b-3f0d045eba2a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://nsidc.org/sites/default/files/mod10a1f-v061-userguide_0.pdf"}],"name":"MOD10A1F v61: MODIS/Terra CGF Snow Cover Daily L3 Global 500m SIN Grid, Version 61","permalink":"/catalog/datasets/1d439945-3cee-4cb3-b41b-3f0d045eba2a/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NSIDC","spatial_extent":"Global","spatial_resolution":"500 meter","temporal_coverage":"2000 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Snow cover"],"vars":"Snow cover","weight":1},{"access":[{"file_format":"SHP; TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/631405c5d34e36012efa3190#stdorder"}],"access_details":null,"bbox":{"east":-65.38,"north":51.54,"south":23.24,"west":-127.86},"citation":"Zell, W.O. and Sanford, W.E., 2020, MODFLOW 6 models used to simulate the long-term average surficial groundwater system for the contiguous United States: U.S. Geological Survey data release, https://doi.org/10.5066/P91LFFN1","creator":[{"creator_email":"wzell@usgs.gov","creator_name":"Wes Zell"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Seventy-five steady-state two-dimensional groundwater flow (MODFLOW-6) models of the shallow groundwater system were developed to map depth to water and estimate effective surficial transmissivity for the contiguous United States (CONUS). The models were driven by spatially-distributed recharge estimated by Reitz and others (2017, https://doi.org/10.5066/F7PN93P0) using average water-budget information for 1985-2015 and calibrated against long-term average water levels in observation wells, as well as, water-level estimates derived from perennial first-order streams and wetlands.  The development of the model input and output files included in this data release, as well as post-processing used to derive additional water-budget components also included in this data release, are documented in the Water Resources Research article (https://doi.org/10.1029/2019WR026724).","doi_url":"https://doi.org/10.5066/P91LFFN1","domain":["Hydrogeology","Hydrology"],"draft":false,"id":"1e58b2a8-676e-45db-bdb3-07a0777ce4a6","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/631405c5d34e36012efa3190?f=__disk__52%2Fa7%2F43%2F52a74342bc57ae2a02d4833312eb8c15d60efc2e\u0026transform=1\u0026allowOpen=true"},{"name":"Documentation","url":"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR026724"}],"name":"MODFLOW 6 models used to simulate the long-term average surficial groundwater system for the contiguous United States","permalink":"/catalog/datasets/1e58b2a8-676e-45db-bdb3-07a0777ce4a6/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"250 meter","temporal_coverage":"1985 - 2015","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Recharge","Surficial effective transmissivity","Depth-to-the water table"],"vars":"Recharge; Surficial effective transmissivity; Depth-to-the water table","weight":1},{"access":[{"file_format":"NC","name":"ceres.larc.nasa.gov","url":"https://ceres.larc.nasa.gov/data/"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"\nKato, S., Rose, F.G., Rutan, D. A., Thorsen, T.E., Loeb, N.G., Doelling, D.R., Huang, X., Smith, W.L., Su, W., and Ham, S.H., 2018, Surface irradiances of Edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF): data product, Journal of Climate, v. 31, no. 11, p. 4501-4527,  https://doi.org/10.1175/JCLI-D-17-0523.1","creator":[{"creator_email":"seiji.kato@nasa.gov","creator_name":" Seiji Kato"}],"creator_project":[],"date_created":"5/2/2024","date_updated":"6/3/2026","description":"The algorithm to produce the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4.0 (Ed4) Energy Balanced and Filled (EBAF)-surface data product is explained. The algorithm forces computed top-of-atmosphere (TOA) irradiances to match with Ed4 EBAF-TOA irradiances by adjusting surface, cloud, and atmospheric properties. Surface irradiances are subsequently adjusted using radiative kernels. The adjustment process is composed of two parts: bias correction and Lagrange multiplier. The bias in temperature and specific humidity between 200 and 500 hPa used for the irradiance computation is corrected based on observations by Atmospheric Infrared Sounder (AIRS). Similarly, the bias in the cloud fraction is corrected based on observations by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat. Remaining errors in surface, cloud, and atmospheric properties are corrected in the Lagrange multiplier process. Ed4 global annual mean (January 2005 through December 2014) surface net shortwave (SW) and longwave (LW) irradiances increase by 1.3 W m^−2 and decrease by 0.2 W m^−2, respectively, compared to EBAF Edition 2.8 (Ed2.8) counterparts (the previous version), resulting in an increase in net SW + LW surface irradiance of 1.1 W m^−2. The uncertainty in surface irradiances over ocean, land, and polar regions at various spatial scales are estimated. The uncertainties in all-sky global annual mean upward and downward shortwave irradiance are 3 and 4 W m^−2, respectively, and the uncertainties in upward and downward longwave irradiance are 3 and 6 W m^−2, respectively. With an assumption of all errors being independent, the uncertainty in the global annual mean surface LW + SW net irradiance is 8 W m^−2.","doi_url":"https://doi.org/10.1175/JCLI-D-17-0523.1","domain":["Climate"],"draft":false,"id":"1f1df116-a793-4cc0-94d1-afdc7d1ee437","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://ceres.larc.nasa.gov/documents/DQ_summaries/CERES_EBAF_Ed4.2_DQS.pdf"}],"name":"CERES-EBAF Level 3: Surface Irradiances of Edition 4.0 Clouds and the Earths Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Data Product","permalink":"/catalog/datasets/1f1df116-a793-4cc0-94d1-afdc7d1ee437/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"CONUS","spatial_resolution":"1 degree","temporal_coverage":"2000 - 2022","temporal_frequency":"monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["TOA Fluxes (Net balanced)","Surface Fluxes (Net balanced)","Cloud Radiative Effect","Cloud Properties"],"vars":"TOA Fluxes (Net balanced); Surface Fluxes (Net balanced); Cloud Radiative Effect; Cloud Properties","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/674e09fbd34eaf9f550668d9"}],"bbox":{"east":-63,"north":52,"south":21,"west":-129},"citation":"U.S. Geological Survey, 2024, LCMAP Land Cover and Land Change Conterminous U.S. Collection 1.3: U.S. Geological Survey data release, https://doi.org/10.5066/P9C46NG0.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Land Change Monitoring Assessment and Projection (LCMAP) raster dataset is a suite of five annual land surface change and five annual land cover (and land cover derivative) products. The LCMAP approach is the foundation for an integrated land change science framework led by the U.S. Geological Survey (USGS). The data were calculated using the Continuous Change Detection and Classification (CCDC) algorithm developed by Zhu and Woodcock (2014) and are derived from a time series of satellite imagery consisting of all available cloud- and shadow-free pixels in the USGS Landsat Analysis Ready Data (ARD) archive (Dwyer and others, 2018). The CCDC methodology supports the continuous tracking and characterization of changes in land cover, and condition enabling assessments of current, historical, and future processes of change. Landsat ARD, as the source data for LCMAP, are standardized Landsat data pre-processed to ensure the data meet a minimum set of requirements and are organized into a form that allows immediate analysis with a minimum of additional user effort. ARD data are provided as tiled, georegistered, surface reflectance products defined in a common equal area projection and tiled to a common grid. ARD observations must be transformed into time series vectors before further calculations using the CCDC methodology. The CCDC methodology, initially developed at Boston University (Zhu and Woodcock, 2014), has been adopted and modified by USGS for LCMAP. CCDC involves harmonic modeling that characterizes the seasonality, trends, and breaks from those trends based on the time series spectral reflectance data from multiple Landsat bands (i.e., green, red, near-infrared, short-wave infrared). The CCDC approach involves two major components: change detection and classification. The change detection component utilizes available high-quality surface reflectance data in a pixel-based time series to calculate a mathematical model for the spectral response of each pixel and to estimate the dates at which the spectral time series data diverge from past responses or patterns. The basis of change detection is the comparison of clear satellite observations with model predictions. 'Divergence' (referred to as a model 'break') often is identified as the result of an abrupt change (e.g. wildfire, logging, mining, and urban development) but may also result from a gradual shift (e.g., forest regrowth, insect infestation, disease) in the spectral signal over time. Breaks are detected by CCDC by applying a criterion based on the root mean square error of the harmonic modeling. Time periods for established models are referred to as 'model segments.' After a break is identified in the time series, a new model can be established following the break provided there are enough clear observations going forward in time. The classification component of CCDC involves using the coefficients of time series models as the inputs for land cover classification. The CCDC method has the capability to generate land cover for any date in the time series; the USGS has selected an annual time step for land cover classification. The suite of land cover and change products are nominally identified at a central point in the year, July 1. Classification is performed using a boosted decision tree method based on training data developed from 2001 NLCD land cover classes (Homer and others, 2007). The land cover legend for the Primary and Secondary Land Cover products is comparable to an Anderson level 1 classifcation scheme.\u003cbr\u003e\n\u003cbr\u003e\nHighlight Map Detailed Description -\u0026nbsp;\u003cbr\u003e\n\u003cbr\u003e\n(A) U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (\u003ca href=\"https://doi.org/10.5066/F7QN651G\" style=\"box-sizing: inherit; color: rgb(0, 94, 162); font-family: \u0026quot;Merriweather Web\u0026quot;, Georgia, Cambria, \u0026quot;Times New Roman\u0026quot;, Times, serif; font-size: 16px;\"\u003eNAIP\u003c/a\u003e) aerial imagery from November 15, 2021 over recently disturbed locations near Newnan, Georgia, located southwest of Atlanta. Examples include what appear to be (1) forest clearing for urban development, (2) part of a tornado scar from an EF4 tornado that struck Newnan on March 26, 2021, (3) a combination of disturbances including reforestation, and (4) forest harvest.\u003cbr\u003e\n\u003cbr\u003e\n(B) LCMAP Collection 1.3 Annual Land Cover Change from 2021 identified all four disturbances, shown in purple as land cover classification changes.\u003cbr\u003e\n\u003cbr\u003e\n(C) The LCMAP Collection 1.3 Time of Spectral Change product from 2020 and 2021 shows when spectral changes occurred in 2020 and 2021, grouped by month, including the tornado scar showing changes in April 2021 after the late March tornado.\u003cbr\u003e\n\u003cbr\u003e\n(D) The LCMAP Collection 1.3 Spectral Change Magnitude product, which shows the intensity of a spectral model break, with more drastic changes such as forest clearing in shades of blue and more subtle changes like forest regrowth or tornado damage in shades of green.","doi_url":"https://doi.org/10.5066/P9C46NG0","domain":["Land Cover"],"draft":false,"id":"1f45f9ee-37ad-45e1-8813-53b6da0b3a25","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/674e09fbd34eaf9f550668d9?f=__disk__c3%2F0e%2Fea%2Fc30eeaa5142cb3e831f4b4b39c4fdb5e970a210e\u0026allowOpen=true"}],"name":"LCMAP Land Cover and Land Change Conterminous U.S. Collection 1.3","permalink":"/catalog/datasets/1f45f9ee-37ad-45e1-8813-53b6da0b3a25/","project_use_history":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"1985 - 2021","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Developed","Cropland","Grass/Shrub","Tree Cover","Water","Wetland","Ice/Snow","Barren"],"vars":"Developed; Cropland; Grass/Shrub; Tree Cover; Water; Wetland; Ice/Snow; Barren","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/66fd7083d34edc655a85f049"}],"bbox":{"east":-64.5127,"north":71.4396,"south":17.8309,"west":179.8567},"citation":"Martinez, A.J., Padilla, J.A., and Gorski, G., 2025, Monthly ensemble outputs from the National Hydrologic Model Precipitation-Runoff Modeling System and the Weather Research and Forecasting model hydrologic modeling system for the conterminous United States, Alaska, Hawaii, and Puerto Rico for water years 2010–2020: U.S. Geological Survey data release, https://doi.org/10.5066/P1RBMDUT.","creator":[],"creator_project":[],"date_created":"1/20/2026","date_updated":"6/3/2026","description":"The Integrated Water Availability Assessment uses an ensemble of outputs from two hydrologic models to assess water supply conditions. Results from the two models, the National Hydrologic Model Precipitation-Runoff Modeling System (NHM-PRMS) and Weather Research and Forecasting model hydrologic modeling system (WRF-Hydro) were averaged to create a single model ensemble. A unique instance of each model was developed specifically for this application (see the following data releases listed in the related external resources below: Sampson et al., 2024; Foks et al., 2025; Foks et al., 2024a-c). Each model was run at its native spatiotemporal resolution and converted to the subwatershed (12-digit hydrologic unit code; HUC12) spatial resolution and year-month temporal resolution for ensembling and assessment purposes. For NHM, the native spatiotemporal resolution is hydrologic response units (HRU), which have an average area of about 75 m2 at a daily time step. For WRF-Hydro, the native spatiotemporal resolution is a 250-m grid at an hourly timestep. The data in this release represent the mean between the results from of each model, by variable (hydrologic state or flux), at the HUC12, year-month resolution. The methods are described in the related primary publication (Gorski et al., 2025) and source code (Martinez et al., 2024), both listed in the related external resources below.","doi_url":"https://doi.org/10.5066/P1RBMDUT","domain":["Hydrology","Climate"],"draft":false,"id":"1f5407df-540e-414c-b9f9-29d101312fed","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/66fd7083d34edc655a85f049?f=__disk__e7%2F11%2Fa7%2Fe711a783762bc3b11918d4d8518e0145be7d8009\u0026allowOpen=true"},{"name":"Documentation","url":"https://doi.org/10.5194/hess-2024-262"}],"name":"Monthly ensemble outputs from the National Hydrologic Model Precipitation-Runoff Modeling System and the Weather Research and Forecasting model hydrologic modeling system for the conterminous United States, Alaska, Hawaii, and Puerto Rico for water years 2010--2020","permalink":"/catalog/datasets/1f5407df-540e-414c-b9f9-29d101312fed/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; AK; HI; PR","spatial_resolution":"1:100,000","temporal_coverage":"2009 - 2020","temporal_frequency":"monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["HUC","2009_10-2020_09","HUC","2009_10-2020_09","HUC","2009_10-2020_09","HUC","2009_10-2020_09","HUC","2009_10-2020_09","HUC","2009_10-2020_09","HUC","2009_10-2020_09"],"vars":"HUC; 2009_10-2020_09; HUC; 2009_10-2020_09; HUC; 2009_10-2020_09; HUC; 2009_10-2020_09; HUC; 2009_10-2020_09; HUC; 2009_10-2020_09; HUC; 2009_10-2020_09","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6239f05ad34e915b67cddc31"}],"access_details":null,"bbox":{"east":-65.2148,"north":49.838,"south":24.0465,"west":-125.1563},"citation":"Towler, E., Foks, S.S., Staub, L.E., Dickinson, J.E., Dugger, A.L., Essaid, H.I., Gochis, D., Hodson, T.O., Viger, R.J., and Zhang, Y., 2022, Daily streamflow performance benchmark defined by the standard statistical suite (v1.0) for the National Hydrologic Model application of the Precipitation-Runoff Modeling System (v1 byObs Muskingum) at benchmark streamflow locations in the conterminous United States (ver 3.0, March 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9DKA9KQ","creator":[],"creator_project":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"date_created":"5/2/2024","date_updated":"6/3/2026","description":"This data release contains the standard statistical suite (version 1.0) daily streamflow performance benchmark results for the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Modeling System (NHM-PRMS) version 1 \"byObs\" calibration with Muskingum routing computed at streamflow benchmark locations defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow versus observed daily mean streamflow using various statistics; the Nash-Sutcliffe efficiency (NSE), the Kling-Gupta efficiency (KGE), the logNSE, the Pearson correlation coefficient, the Spearman correlation coefficient, the ratio of the standard deviation, the percent bias, the percent bias in flow duration curve midsegment slope, the percent bias in the flow duration curve high-segment volume, and the percent bias in flow duration curve low-segment volume. Two climatological KGE benchmarks are included that are calculated using daily mean streamflow observations and interannual daily mean or median flows. Additionally, KGE uncertainty estimates have been added as a separate csv file including the standard error of jackknife, standard error of bootstrap, the 5th, 50th and 95th percentiles of the estimates, the jackknife score, the bias of jackknife, the bias of bootstrap, and the standard error of jackknife after bootstrap.","doi_url":"https://doi.org/10.5066/P9DKA9KQ","domain":["Hydrology"],"draft":false,"id":"2209e7b7-d702-4b92-a1f9-6c9d87725388","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6239f05ad34e915b67cddc31?f=__disk__30%2F6c%2Fab%2F306caba2d2132fd088451c16972d1e7b7b27211d\u0026allowOpen=true"}],"name":"Streamflow standard suite benchmark results (NHM v1.0): Daily streamflow performance benchmark defined by the standard statistical suite (v1.0) for the National Hydrologic Model application of the Precipitation-Runoff Modeling System (v1 byObs Muskingum) at benchmark streamflow locations in the conterminous United States (ver 3.0, March 2023)","permalink":"/catalog/datasets/2209e7b7-d702-4b92-a1f9-6c9d87725388/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"unknown","temporal_coverage":"1983 - 2016","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["site_no","KGE","NSE","logNSE","r","rSpearman","rSD","PBIAS","pbiasfdc","t_n","PBIAS_HF","t_n_HF","PBIAS_LF","t_n_LF","KGE_Avg_DOY","KGE_Med_DOY","camels","GOF_stat","seJack","seBoot","p05","p50","p95","score","biasJack","biasBoot","seJab"],"vars":"site_no; KGE; NSE; logNSE; r; rSpearman; rSD; PBIAS; pbiasfdc; t_n; PBIAS_HF; t_n_HF; PBIAS_LF; t_n_LF; KGE_Avg_DOY; KGE_Med_DOY; camels; GOF_stat; seJack; seBoot; p05; p50; p95; score; biasJack; biasBoot; seJab","weight":1},{"access":[{"file_format":"HGT","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-srtmgl1-003"}],"access_details":"An Earthdata Login is required before users can download data or use selected tools that comprise NASA's Earth Observing System Data and Information System (EOSDIS).","bbox":{"east":180,"north":60,"south":-56,"west":-180},"citation":"NASA JPL, 2013, NASA Shuttle Radar Topography Mission Global 1 arc second [Data set]: NASA EOSDIS Land Processes DAAC, Accessed [YYYY-MM-DD] at https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) version SRTM, which includes the global 1 arc second (~30 meter) product. \u003cbr\u003e\u003cbr\u003eNASA Shuttle Radar Topography Mission (SRTM) datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. The purpose of SRTM was to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. SRTM was a primary component of the payload on the Space Shuttle Endeavour during its STS-99 mission. Endeavour launched February 11, 2000 and flew for 11 days.\u003cbr\u003eSRTM collected data in swaths, which extend from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km). These swaths are ~225 km wide, and consisted of all land between 60 degrees N and 56 degrees S latitude. This accounts for about 80% of Earth's total landmass.\u003cbr\u003e Each SRTMGL1 data tile contains a mosaic and blending of elevations generated by averaging all \"data takes\" that fall within that tile. These elevation files use the extension \"HGT\", meaning height (such as N37W105.SRTMGL1.HGT). The primary goal of creating the Version 3 data was to eliminate voids that were present in earlier versions of SRTM data. In areas with limited data, existing topographical data were used to supplement the SRTM data to fill the voids. The source of each elevation pixel is identified in the corresponding SRTMGL1N product (such as N37W105.SRTMGL1N.NUM).\u003cbr\u003eThe global 1 arc second SRTM product is also available in NetCDF4 format as the SRTMGL1_NC dataset with the source of each elevation pixel in the corresponding SRTMGL1_NUMNC product.","doi_url":"https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003","domain":["Topography"],"draft":false,"id":"22c70bf3-57b2-4027-8f2c-7d1f6e808964","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf"}],"name":"NASA Shuttle Radar Topography Mission Global 1 arc second V003","permalink":"/catalog/datasets/22c70bf3-57b2-4027-8f2c-7d1f6e808964/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"30 meter","temporal_coverage":"2000","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Elevation"],"vars":"Elevation","weight":1},{"access":[{"file_format":"DAT; GDB; XYZ; TIN; SHP; KMZ","name":"arcgis.com","url":"https://www.arcgis.com/apps/dashboards/4b8f2ba307684cf597617bf1b6d2f85d"}],"access_details":"interactive online map for selecting channel data, metadata is bundled with each data download zip file","bbox":{"east":-65,"north":72,"south":14,"west":-179},"citation":null,"creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"National database of hydrographic surveys, which provide assistance in locating navigable channels, determining dredging requirements, verifying dredging accuracy, and maintaining harbors and rivers.","doi_url":null,"domain":["Stream Characteristics"],"draft":false,"id":"247a297a-39dc-4d1e-96bb-7bf684aefbd4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.sam.usace.army.mil/Missions/Spatial-Data-Branch/eHydro/"}],"name":"USACE Hydrographic Surveys (eHydro)","permalink":"/catalog/datasets/247a297a-39dc-4d1e-96bb-7bf684aefbd4/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USACE","spatial_extent":"CONUS; AK; HI","spatial_resolution":"varies","temporal_coverage":"2018 - Present","temporal_frequency":"varies","update_detail":"append","update_frequency":"irregular","update_type":"Dynamic","variables":["Bathymetry"],"vars":"Bathymetry","weight":1},{"access":[{"file_format":"CSV; NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/667b23c8d34e6151c9d6be10"}],"access_details":null,"bbox":{"east":-129.375,"north":47.5172,"south":71.7464,"west":-179.7363},"citation":"Foks, S.S., LaFontaine, J.H., McDonald, R.R., Snyder, A.M., Kolb, K.R., LaMotte, A.E., and Viger, R.J., 2025, Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Alaska, 1980-2021: U.S. Geological Survey data release, https://doi.org/10.5066/P149Z4G9.","creator":[{"creator_email":"sfoks@usgs.gov","creator_name":"Sydney S Foks"}],"creator_project":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"date_created":"5/3/2024","date_updated":"6/3/2026","description":"\u003cp\u003eThis data release contains 15\u0026nbsp;variables from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) modeling application forced with Daymet version 4 (Koczot and others, 2025) from 1980 through 2021\u0026nbsp;that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of Alaska.The following flux and storages are included: total monthly precipitation, evapotranspiration, lateral flow, surface runoff, interflow, recharge, groundwater flow, and the average monthly snow water equivalent, interflow storage, groundwater storage, total storage, and soil moisture. These data can be found in the “AK_huc12_monthly_nhmprms_daymet_1980_2021.nc” file.\u003cbr\u003e\n\u003cbr\u003e\nAdditionally, two supplementary files are also included in this data release. The first file (“AK_weights_hru_to_huc12_nhmprms_daymet.csv”) contains the spatial weights or fraction that is used to “weight” the modeling output in the area-weighting process. The second file (“AK_summed_weights_per_huc12_nhmprms_daymet.csv”) contains the total fractional area within each twelve-digit hydrologic unit code that is covered by the modeling output and is important for filtering results in the data file (where a fractional coverage may be less than one).\u003c/p\u003e\n","doi_url":"https://doi.org/10.5066/P149Z4G9","domain":["Hydrology"],"draft":false,"id":"25d35497-f186-4aa8-8dea-438cd79a92b9","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/667b23c8d34e6151c9d6be10?f=__disk__ea%2F71%2F49%2Fea7149a787e8483ce2e5414f5eb42efee44eae40\u0026allowOpen=true"}],"name":"Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Alaska, 1980-2021","permalink":"/catalog/datasets/25d35497-f186-4aa8-8dea-438cd79a92b9/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"AK","spatial_resolution":"unknown","temporal_coverage":"1980 - 2021","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["huc_id","time","nhm_ppt_post","nhm_actet_post","nhm_potet_post","nhm_lateral_flow_post","nhm_sroff_post","nhm_ssres_flow_post","nhm_gwres_flow_post","nhm_recharge_post","nhm_pkwater_equiv_post","nhm_gwres_stor_post","nhm_ssres_stor_post","nhm_storage_post","nhm_soil_moisture_total_depth_post","nhm_soil_moisture_depth_post","nhm_soil_moisture_fraction","yrmo","huc_id","hru_id","weight","huc_id","summed_weights"],"vars":"huc_id; time; nhm_ppt_post; nhm_actet_post; nhm_potet_post; nhm_lateral_flow_post; nhm_sroff_post; nhm_ssres_flow_post; nhm_gwres_flow_post; nhm_recharge_post; nhm_pkwater_equiv_post; nhm_gwres_stor_post; nhm_ssres_stor_post; nhm_storage_post; nhm_soil_moisture_total_depth_post; nhm_soil_moisture_depth_post; nhm_soil_moisture_fraction; yrmo; huc_id; hru_id; weight; huc_id; summed_weights","weight":1},{"access":[{"file_format":"SHP; GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/537a6a51e4b0efa8af081550"}],"access_details":null,"bbox":{"east":-63.21,"north":50.741871378,"south":17.1,"west":-161.31},"citation":"Viger, R.J., 2014, Geospatial Fabric Attribute Tables for PRMS Surface Depressions Parameters based on NHD high-res(Preliminary), U.S. Geological Survey; https://doi.org/10.5066/F70C4ST6","creator":[{"creator_email":"rviger@usgs.gov","creator_name":"Roland Viger"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"\u003cbr /\u003e \n\u003cstrong\u003e(\u003ca href=\"http://wwwbrr.cr.usgs.gov/projects/SW_MoWS/GeospatialFabric.html\"\u003eHyperlink to Official Landing Page for Geospatial Fabric products\u003c/a\u003e)\u003c/strong\u003e \n\u003cbr /\u003e \n\u003cbr /\u003eThis dataset contains a set of attributes describing the \u0026quot;nhru\u0026quot; GIS features (Hydrologic Response Units)in the Geospatial Fabric Features dataset(http://dx.doi.org/doi:10.5066/F7542KMD) that have been developed in support of the USGS PRMS watershed model. These tables are organized according to Geospatial Fabric Region; see the thumbnail of the Geospatial Fabric Features Regions (https://www.sciencebase.gov/catalog/item/535edb4ae4b08e65d60fc837). Each table contains a key field, \u0026quot;hru_id\u0026quot;, that can be used to relate to the nhru feature class in the Geospatial Fabric Feature dataset for the corresponding Region. The methodologies used to derive the individual attributes can be located in the Appendix of the GIS Weasel Users Manual by the name of the attribute, which is the same as the name of the corresponding PRMS parameter, in (Viger and Leavesley, 2007). The metadata for each table within the current container identifies any ancillary datasets used to produce the table fields.For nhru instances that are partially or entirely beyond the borders of the United States, supporting GIS data was generally lacking. Where the value for a field could not be determined, values derived at the border were spatially extended to these areas to support derivation of a value. Users may want to review and modify the field values for these HRUs.Viger, R.J., and Leavesley, G.H., 2007, The GIS Weasel user's manual: U.S. Geological Survey Techniques and Methods, book 6, chap. B4, 201 p.","doi_url":"https://doi.org/10.5066/F70C4ST6","domain":["Hydrology"],"draft":false,"id":"2627575d-ecd7-4ef7-8176-edcda6790fc7","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/537a6a51e4b0efa8af081550?f=__disk__3b%2Fff%2F32%2F3bff32b68959d4362e2851e15355ec905855db2b\u0026allowOpen=true"}],"name":"Geospatial Fabric Attribute Tables for PRMS Surface Depressions Parameters based on NHD high-res(Preliminary)","permalink":"/catalog/datasets/2627575d-ecd7-4ef7-8176-edcda6790fc7/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; HI; PR","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["hru_id","dprst_area","sro_to_dprst"],"vars":"hru_id; dprst_area; sro_to_dprst","weight":1},{"access":[{"file_format":"XLSX","name":"nj.gov","url":"https://www.nj.gov/drbc/library/documents/water-use/2060report_data-release_v2110.xlsx"},{"file_format":"SHP","name":"nj.gov","url":"https://www.nj.gov/drbc/library/documents/GIS/drb147.zip"}],"access_details":null,"bbox":{"east":-74.5,"north":42.4,"south":38.65,"west":-76.5},"citation":"Thompson, M.Y., and Pindar, C.E., 2021, Water Withdrawal and Consumptive Use Estimates for the Delaware River Basin (1990-2017) With Projections Through 2060: Delaware River Basin Commission Report number 2021-4, https://www.nj.gov/drbc/library/documents/water-use/DRBC_2021-4_Water2060_Final_101421.pdf","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"In October 2021, the Delaware River Basin Commission (DRBC) published a new report titled Water Withdrawal and Consumptive Use Estimates for the Delaware River Basin (1990-2017) with Projections through 2060. The report analyzes 30 years of historic withdrawal data and projects withdrawal demands to the year 2060.\u003cbr\u003eThis study compiles data on water withdrawals from the Delaware River Basin for the years 1990 through 2017, assumed to represent actual (or observed) conditions. Application of consumptive use ratios in a standardized approach emphasizing self-reported consumptive use data has helped to provide estimates of consumptive use for the years 1990 through 2017. Each sector of withdrawal and consumptive use data is then projected through the year 2060.\u003cbr\u003emain site (contains links to report, data, and sub-basin dataset): https://nj.gov/drbc/programs/supply/use-demand-projections2060.html","doi_url":null,"domain":["Water Use"],"draft":false,"id":"267dd340-9c03-4f2e-8050-822167b36a88","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.nj.gov/drbc/library/documents/water-use/DRBC_2021-4_Water2060_Final_101421.pdf"}],"name":"Water Withdrawal and Consumptive Use Estimates for the Delaware River Basin (1990-2017) With Projections Through 2060","permalink":"/catalog/datasets/267dd340-9c03-4f2e-8050-822167b36a88/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"DRBC","spatial_extent":"Delaware River basin","spatial_resolution":"unknown","temporal_coverage":"1990 - 2060","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Public water supply historical data for water withdrawals and consumptive use","Public water supply projected water withdrawal and consumptive use","Self-supplied domestic estimated water withdrawals and consumptive use","Thermoelectric historical data for water withdrawals and consumptive use","Thermoelectric projected water withdrawals and consumptive use","Hydroelectric historical data for water withdrawals and consumptive use","Hydroelectric projected water withdrawals and consumptive use","Industrial sector historical data for water withdrawals and consumptive use","Industrial sector projected water withdrawals and consumptive use","Mining sector historical data for water withdrawals and consumptive use","Mining sector projected water withdrawals and consumptive use","Irrigation historical data for water withdrawals and consumptive use","Irrigation projected water withdrawals and consumptive use","Other sector historical data for water withdrawals and consumptive use","Other sector projected water withdrawals and consumptive use"],"vars":"Public water supply historical data for water withdrawals and consumptive use; Public water supply projected water withdrawal and consumptive use; Self-supplied domestic estimated water withdrawals and consumptive use; Thermoelectric historical data for water withdrawals and consumptive use; Thermoelectric projected water withdrawals and consumptive use; Hydroelectric historical data for water withdrawals and consumptive use; Hydroelectric projected water withdrawals and consumptive use; Industrial sector historical data for water withdrawals and consumptive use; Industrial sector projected water withdrawals and consumptive use; Mining sector historical data for water withdrawals and consumptive use; Mining sector projected water withdrawals and consumptive use; Irrigation historical data for water withdrawals and consumptive use;  Irrigation projected water withdrawals and consumptive use; Other sector historical data for water withdrawals and consumptive use; Other sector projected water withdrawals and consumptive use","weight":1},{"access":[{"file_format":"CSV; NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6489d846d34ef77fcafe5bae"}],"access_details":null,"bbox":{"east":-64.0503,"north":19.4666,"south":17.1723,"west":-68.0273},"citation":"Foks, S.S., LaFontaine, J.H., McDonald, R.R., Snyder, A.M., Kolb, K.R., LaMotte, A.E., and Viger, R.J., 2024, Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Puerto Rico, 1950-2021 (ver. 2.0, June 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P1ERNH5C.","creator":[{"creator_email":"sfoks@usgs.gov","creator_name":"Sydney S Foks"}],"creator_project":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"date_created":"5/3/2024","date_updated":"6/3/2026","description":"This data release contains 16 variables from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) modeling application forced with Daymet version 4 (LaFontaine and others, 2024) from January 1950 through December 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of Puerto Rico and the U.S. Virgin Islands. The following fluxes and storages are included: total monthly precipitation, evapotranspiration, lateral flow, surface runoff, interflow, recharge, groundwater flow, and the average monthly snow water equivalent, interflow storage, groundwater storage, total storage, and soil moisture. These data can be found in the “PR_huc12_monthly_nhmprms_daymet_1950_2021.nc” file.\u003cbr\u003e\n\u003cbr\u003e\nAdditionally, two supplementary files are also included in this data release. The first file (“PR_weights_hru_to_huc12_nhmprms_daymet.csv”) contains the spatial weights or fraction that is used to “weight” the modeling output in the area-weighting process. The second file (“PR_summed_weights_per_huc12_nhmprms_daymet.csv”) contains the total fractional area within each twelve-digit hydrologic unit code that is covered by the modeling output and is important for filtering results in the data file (where a fractional coverage may be less than one).\u003cbr\u003e\n\u003cbr\u003e\nIn the version 2.0 data release update, a new variable was added to the “PR_huc12_monthly_nhmprms_daymet_1950_2021.nc” file and a new file, \"PR_huc12_daily_soil_moisture_fraction_nhmprms_paymet_1950_2021.nc\" was added that contains daily estimates of the soil moisture fraction at each twelve-digit hydrologic unit code for the spatial extent of Puerto Rico and the U.S. Virgin Islands. See the file, “revision_history_nhmprms_daymet_PR.txt” for a full description of revisions.\u003cbr\u003e\n\u0026nbsp;\n\u003cp\u003eFirst release: 2024\u003c/p\u003e\n\n\u003cp\u003eRevised: June\u0026nbsp;2025 (ver. 2.0)\u003c/p\u003e\n\n\u003cul\u003e\n\u003c/ul\u003e\n","doi_url":"https://doi.org/10.5066/P1ERNH5C","domain":["Hydrology"],"draft":false,"id":"26f67162-6d16-4322-9457-43f81499b370","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6489d846d34ef77fcafe5bae?f=__disk__83%2F23%2F08%2F8323089a6b4e59d3a59c883b01297ef0efbccdf8\u0026allowOpen=true"}],"name":"Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Puerto Rico, 1950-2021 (ver. 2.0, June 2025)","permalink":"/catalog/datasets/26f67162-6d16-4322-9457-43f81499b370/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"PR","spatial_resolution":"unknown","temporal_coverage":"1950 - 2021","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["huc_id","time","nhm_ppt_post","nhm_actet_post","nhm_potet_post","nhm_lateral_flow_post","nhm_sroff_post","nhm_ssres_flow_post","nhm_gwres_flow_post","nhm_recharge_post","nhm_pkwater_equiv_post","nhm_gwres_stor_post","nhm_ssres_stor_post","nhm_storage_post","nhm_soil_moisture_total_depth_post","nhm_soil_moisture_depth_post","nhm_soil_moisture_fraction","yrmo","nhm_quickflow","huc_id","hru_id","weight","huc_id","summed_weights","huc_id","time","nhm_soil_moisture_fraction"],"vars":"huc_id; time; nhm_ppt_post; nhm_actet_post; nhm_potet_post; nhm_lateral_flow_post; nhm_sroff_post; nhm_ssres_flow_post; nhm_gwres_flow_post; nhm_recharge_post; nhm_pkwater_equiv_post; nhm_gwres_stor_post; nhm_ssres_stor_post; nhm_storage_post; nhm_soil_moisture_total_depth_post; nhm_soil_moisture_depth_post; nhm_soil_moisture_fraction; yrmo; nhm_quickflow; huc_id; hru_id; weight; huc_id; summed_weights; huc_id; time; nhm_soil_moisture_fraction","weight":1},{"access":[{"file_format":"CSV; NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/667b2c75d34e6151c9d6bf6d"}],"access_details":null,"bbox":{"east":-154.303,"north":22.6039,"south":18.5942,"west":-160.8289},"citation":"Foks, S.S., LaFontaine, J.H., McDonald, R.R., Snyder, A.M., Kolb, K.R., LaMotte, A.E., and Viger, R.J., 2024, Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Hawaii, 1980-2021 (ver. 2.0, June 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P14NFNOJ.","creator":[{"creator_email":"sfoks@usgs.gov","creator_name":"Sydney S Foks"}],"creator_project":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"date_created":"5/3/2024","date_updated":"6/3/2026","description":"\u003cp\u003eThis data release contains 16 variables from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) modeling application forced with Daymet version 4 (Rosa and others, 2025) from 1980 through 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of Hawaii. The following fluxes and storages are included: total monthly precipitation, evapotranspiration, lateral flow, surface runoff, quickflow, interflow, recharge, groundwater flow, and the average monthly snow water equivalent, interflow storage, groundwater storage, total storage, and soil moisture. These data can be found in the “HI_huc12_monthly_nhmprms_daymet_1980_2021.nc” file.\u0026nbsp;\u003cbr\u003e\n\u003cbr\u003e\nAdditionally, two supplementary files are also included in this data release. The first file (“HI_weights_hru_to_huc12_nhmprms_daymet.csv”) contains the spatial weights or fraction that is used to “weight” the modeling output in the area-weighting process. The second file (“HI_summed_weights_per_huc12_nhmprms_daymet.csv”) contains the total fractional area within each twelve-digit hydrologic unit code that is covered by the modeling output and is important for filtering results in the data file (where a fractional coverage may be less than one).\u0026nbsp;\u003cbr\u003e\n\u003cbr\u003e\nIn the version 2.0 data release update, a new variable was added to the “HI_huc12_monthly_nhmprms_daymet_1980_2021.nc” file. Additionally, several new netCDF files were added that contain data summarizations from a different production run (output data referenced as \"byPOIobs\") within the model application data release by Rosa and others (2025). Two of the three files added contain daily estimates of soil moisture fraction (\"HI_byPOIobs_huc12_daily_soil_moisture_fraction_nhmprms_daymet_1980_2021.nc\") and daily estimates of snow water equivalent (\"HI_byPOIobs_huc12_daily_pkwater_equiv_nhmprms_daymet_1980_2021.nc\") at the twelve-digit hydrologic unit code spatial regions. The third file added contains monthly estimates of 16 variables from the \"byPOIobs\" modeling application production run (\"HI_byPOIobs_huc12_monthly_nhmprms_daymet_1980_2021.nc\"). See the file, “revision_history_nhmprms_daymet_PR.txt” for a full description of the revisions.\u003cbr\u003e\n\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003eFirst release: 2024\u003c/p\u003e\n\n\u003cp\u003eRevised: June\u0026nbsp;2025 (ver. 2.0)\u003c/p\u003e\n","doi_url":"https://doi.org/10.5066/P14NFNOJ","domain":["Hydrology"],"draft":false,"id":"27beb4a7-3975-4cac-8641-aad39a3c61e4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/667b2c75d34e6151c9d6bf6d?f=__disk__de%2F08%2F30%2Fde08301da28e17fe8339afc81974b739b2d96f34\u0026allowOpen=true"}],"name":"Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Hawaii, 1980-2021 (ver. 2.0, June 2025)","permalink":"/catalog/datasets/27beb4a7-3975-4cac-8641-aad39a3c61e4/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"HI","spatial_resolution":"unknown","temporal_coverage":"1980 - 2021","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["huc_id","time","nhm_ppt_post","nhm_actet_post","nhm_potet_post","nhm_lateral_flow_post","nhm_sroff_post","nhm_ssres_flow_post","nhm_gwres_flow_post","nhm_recharge_post","nhm_pkwater_equiv_post","nhm_gwres_stor_post","nhm_ssres_stor_post","nhm_storage_post","nhm_soil_moisture_total_depth_post","nhm_soil_moisture_depth_post","nhm_soil_moisture_fraction","yrmo","nhm_quickflow","huc_id","hru_id","weight","huc_id","summed_weights","huc_id","time","nhm_ppt_post","nhm_actet_post","nhm_potet_post","nhm_lateral_flow_post","nhm_sroff_post","nhm_ssres_flow_post","nhm_gwres_flow_post","nhm_recharge_post","nhm_pkwater_equiv_post","nhm_gwres_stor_post","nhm_ssres_stor_post","nhm_storage_post","nhm_soil_moisture_total_depth_post","nhm_soil_moisture_depth_post","nhm_soil_moisture_fraction","yrmo","nhm_quickflow","huc_id","time","nhm_soil_moisture_fraction","huc_id","time","nhm_pkwater_equiv_post"],"vars":"huc_id; time; nhm_ppt_post; nhm_actet_post; nhm_potet_post; nhm_lateral_flow_post; nhm_sroff_post; nhm_ssres_flow_post; nhm_gwres_flow_post; nhm_recharge_post; nhm_pkwater_equiv_post; nhm_gwres_stor_post; nhm_ssres_stor_post; nhm_storage_post; nhm_soil_moisture_total_depth_post; nhm_soil_moisture_depth_post; nhm_soil_moisture_fraction; yrmo; nhm_quickflow; huc_id; hru_id; weight; huc_id; summed_weights; huc_id; time; nhm_ppt_post; nhm_actet_post; nhm_potet_post; nhm_lateral_flow_post; nhm_sroff_post; nhm_ssres_flow_post; nhm_gwres_flow_post; nhm_recharge_post; nhm_pkwater_equiv_post; nhm_gwres_stor_post; nhm_ssres_stor_post; nhm_storage_post; nhm_soil_moisture_total_depth_post; nhm_soil_moisture_depth_post; nhm_soil_moisture_fraction; yrmo; nhm_quickflow; huc_id; time; nhm_soil_moisture_fraction; huc_id; time; nhm_pkwater_equiv_post","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5c66e72ae4b0fe48cb3ac8ac"}],"access_details":null,"bbox":{"east":-63.1667,"north":54.8088,"south":21.0821,"west":-129.7712},"citation":"Sabitov, T.Y., and Wieczorek, M.E., 2019, 30 year (1981 - 2010) annual average of daily intensity of precipitation for a rain event for the Conterminous United States and District of Columbia: U.S. Geological Survey data release, https://doi.org/10.5066/P9T3NSBB","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"A rain event is defined as a period when the number of consecutive days with precipitation equals or exceeds 1 millimeter. Daily precipitation intensity is defined as the amount of precipitation over the duration of a rain event divided by the number of days in a rain event. This dataset was derived from Daymet data.","doi_url":null,"domain":["Climate"],"draft":false,"id":"28d6e394-e539-4757-8085-0e78bd68266a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"30 year (1981-2010) annual average of daily intensity of precipitation for a rain event for the Conterminous United States and District of Columbia","permalink":"/catalog/datasets/28d6e394-e539-4757-8085-0e78bd68266a/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1 kilometer","temporal_coverage":"1981 - 2010","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Precipitation (daily intensity for a rain event)"],"vars":"Precipitation (daily intensity for a rain event)","weight":1},{"access":[{"name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/631405bbd34e36012efa304a"}],"access_details":null,"bbox":{"east":180,"north":90,"south":5.402082,"west":-180},"citation":"Falcone, J., 2011, GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow: U.S. Geological Survey data release, https://doi.org/10.5066/P96CPHOT.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"\u003cem\u003eThese data were released prior to the October 1, 2016 effective date for the USGS’s policy dictating the review, approval, and release of scientific data as referenced in \u003c/em\u003e\u003ca href=\"https://www.usgs.gov/about/organization/science-support/survey-manual/5028-fundamental-science-practices-review-and\" style=\"padding: 0px; user-select: text; -webkit-user-drag: none; -webkit-tap-highlight-color: transparent; font-family: Calibri, Calibri_MSFontService, sans-serif; font-size: 14.6667px; white-space: pre-wrap; color: inherit;\"\u003e\u003cstrong\u003e\u003cem\u003eUSGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release\u003c/em\u003e\u003c/strong\u003e.\u003c/a\u003e\u003cbr\u003e\n\u003cbr\u003e\n This dataset, termed \"GAGES II\", an acronym for Geospatial Attributes of Gages for Evaluating Streamflow, version II, provides geospatial data and classifications for 9,322 stream gages maintained by the U.S. Geological Survey (USGS). It is an update to the original GAGES, which was published as a Data Paper on the journal Ecology's website (Falcone and others, 2010b) in 2010. The GAGES II dataset consists of gages which have had either 20+ complete years (not necessarily continuous) of discharge record since 1950, or are currently active, as of water year 2009, and whose watersheds lie within the United States, including Alaska, Hawaii, and Puerto Rico. Reference gages were identified based on indicators that they were the least-disturbed watersheds within the framework of broad regions, based on 12 major ecoregions across the United States. Of the 9,322 total sites, 2,057 are classified as reference, and 7,265 as non-reference. Of the 2,057 reference sites, 1,633 have (through 2009) 20+ years of record since 1950. Some sites have very long flow records: a number of gages have been in continuous service since 1900 (at least), and have 110 years of complete record (1900-2009) to date. The geospatial data include several hundred watershed characteristics compiled from national data sources, including environmental features (e.g. climate – including historical precipitation, geology, soils, topography) and anthropogenic influences (e.g. land use, road density, presence of dams, canals, or power plants). The dataset also includes comments from local USGS Water Science Centers, based on Annual Data Reports, pertinent to hydrologic modifications and influences. The data posted also include watershed boundaries in GIS format. This overall dataset is different in nature to the USGS Hydro-Climatic Data Network (HCDN; Slack and Landwehr 1992), whose data evaluation ended with water year 1988. The HCDN identifies stream gages which at some point in their history had periods which represented natural flow, and the years in which those natural flows occurred were identified (i.e. not all HCDN sites were in reference condition even in 1988, for example, 02353500). The HCDN remains a valuable indication of historic natural streamflow data. However, the goal of this dataset was to identify watersheds which currently have near-natural flow conditions, and the 2,057 reference sites identified here were derived independently of the HCDN. A subset, however, noted in the BasinID worksheet as “HCDN-2009”, has been identified as an updated list of 743 sites for potential hydro-climatic study. The HCDN-2009 sites fulfill all of the following criteria: (a) have 20 years of complete and continuous flow record in the last 20 years (water years 1990-2009), and were thus also currently active as of 2009, (b) are identified as being in current reference condition according to the GAGES-II classification, (c) have less than 5 percent imperviousness as measured from the NLCD 2006, and (d) were not eliminated by a review from participating state Water Science Center evaluators. The data posted here consist of the following items:- This point shapefile, with summary data for the 9,322 gages.- A zip file containing basin characteristics, variable definitions, and a more detailed report.- A zip file containing shapefiles of basin boundaries, organized by classification and aggregated ecoregion.- A zip file containing mainstem stream lines (Arc line coverages) for each gage.","doi_url":"https://doi.org/10.5066/P96CPHOT","domain":["Hydrology"],"draft":false,"id":"2945b996-7636-4d71-89d2-137e984a8a77","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/631405bbd34e36012efa304a?f=__disk__e5%2F51%2F9c%2Fe5519c78a0075340f33d600568e42b64719ca668\u0026allowOpen=true"}],"name":"GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow","permalink":"/catalog/datasets/2945b996-7636-4d71-89d2-137e984a8a77/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"unknown","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["FID","Shape","STAID","STANAME","CLASS","AGGECOREGI","DRAIN_SQKM","HUC02","LAT_GAGE","LNG_GAGE","STATE","HCDN_2009","ACTIVE09","FLYRS1900","FLYRS1950","FLYRS1990"],"vars":"FID; Shape; STAID; STANAME; CLASS; AGGECOREGI; DRAIN_SQKM; HUC02; LAT_GAGE; LNG_GAGE; STATE; HCDN_2009; ACTIVE09; FLYRS1900; FLYRS1950; FLYRS1990","weight":1},{"access":[{"file_format":"NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/62e2ef03d34e394b65364f8f"}],"access_details":"For the ScienceBase access point, ISO metadata are available with each child item dataset on the page.","bbox":{"east":-74.55671530588432,"north":40.22553296540622,"south":38.57888442021855,"west":-75.63290610753754},"citation":"Cook, S.E., and Warner, J.C., 2023, U.S. Geological Survey simulations of 3D-hydrodynamics in Delaware Bay (2019) to improve understanding of the mechanisms driving salinity intrusion: U.S. Geological Survey data release, https://doi.org/10.5066/P9GU07FL","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST Warner and others, 2019; Warner and others, 2010) model was used to simulate three-dimensional hydrodynamics and waves to study salinity intrusion in the Delaware Bay estuary for 2019. Salinity intrusion in coastal systems is due in part to extreme events like drought or low-pressure storms and longer-term sea level rise, threatening economic infrastructure and ecological health. Along the eastern seaboard of the United States, approximately 13 million people rely on the water resources of the Delaware River basin, which is actively managed to suppress the salt front (or ~0.52 daily averaged psu line) through river discharge targets. However, river discharge is only part of the story. The other mechanisms controlling salinity intrusion include tidal motions on daily and spring-neap cycles, bathymetric and topographic features, and meteorological events. It is the interaction of these mechanisms that ultimately determines the distribution of salt in an estuary, particularly during periods of low discharge. The purpose of this study is to examine the mechanisms controlling the location of the salt front in the Delaware Bay estuary using a calibrated three-dimensional hydrodynamic model, the Coupled Ocean Atmosphere Wave and Sediment Transport (COAWST; v. 3.6) modeling system. The model was forced with tides, subtidal water levels, bulk atmospheric conditions, and waves. Compared with the observation-based location of the salt front line the model captured the major dynamics throughout the year and performed well during times of low discharge. The daily average salt front moved almost 16 km (10 mi) within a neap-spring tidal cycle, while low pressure storm systems were found to move the daily averaged salt front by 13-16 km in one event.","doi_url":"https://doi.org/10.5066/P9GU07FL","domain":["Water Quality"],"draft":false,"id":"2a09104b-8a4c-4f29-ae4e-ceccd93951d3","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.5066/P9GU07FL"}],"name":"U.S. Geological Survey simulations of 3D-hydrodynamics in Delaware Bay (2019) to improve understanding of the mechanisms driving salinity intrusion","permalink":"/catalog/datasets/2a09104b-8a4c-4f29-ae4e-ceccd93951d3/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Delaware Bay","spatial_resolution":"varies","temporal_coverage":"2019","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["ntimes","ndtfast","dt","dtfast","dstart","nHIS","ndefHIS","nRST","nSTA","Falpha","Fbeta","Fgamma","nl_tnu2","nl_visc2","LuvSponge","LtracerSponge","Akt_bak","Akv_bak","Akk_bak","Akp_bak","rdrg","rdrg2","Zob","Zos","gls_p","gls_m","gls_n","gls_cmu0","gls_c1","gls_c2","gls_c3m","gls_c3p","gls_sigk","gls_sigp","gls_Kmin","gls_Pmin","Charnok_alpha","Zos_hsig_alpha","sz_alpha","CrgBan_cw","wec_alpha","Znudg","M2nudg","M3nudg","Tnudg","Tnudg_SSS","FSobc_in","FSobc_out","M2obc_in","M2obc_out","M3obc_in","M3obc_out","rho0","R0","Tcoef","Scoef","gamma2","LuvSrc","LwSrc","LtracerSrc","LsshCLM","Lm2CLM","Lm3CLM","LtracerCLM","LnudgeM2CLM","LnudgeM3CLM","LnudgeTCLM","spherical","xl","el","Vtransform","Vstretching","theta_s","theta_b","Tcline","hc","grid","Cs_r","Cs_w","Ipos","Jpos","h","angle","s_rho","s_w","lon_rho","lat_rho","ocean_time"],"vars":"ntimes; ndtfast; dt; dtfast; dstart; nHIS; ndefHIS; nRST; nSTA; Falpha; Fbeta; Fgamma; nl_tnu2; nl_visc2; LuvSponge; LtracerSponge; Akt_bak; Akv_bak; Akk_bak; Akp_bak; rdrg; rdrg2; Zob; Zos; gls_p; gls_m; gls_n; gls_cmu0; gls_c1; gls_c2; gls_c3m; gls_c3p; gls_sigk; gls_sigp; gls_Kmin; gls_Pmin; Charnok_alpha; Zos_hsig_alpha; sz_alpha; CrgBan_cw; wec_alpha; Znudg; M2nudg; M3nudg; Tnudg; Tnudg_SSS; FSobc_in; FSobc_out; M2obc_in; M2obc_out; M3obc_in; M3obc_out; rho0; R0; Tcoef; Scoef; gamma2; LuvSrc; LwSrc; LtracerSrc; LsshCLM; Lm2CLM; Lm3CLM; LtracerCLM; LnudgeM2CLM; LnudgeM3CLM; LnudgeTCLM; spherical; xl; el; Vtransform; Vstretching; theta_s; theta_b; Tcline; hc; grid; Cs_r; Cs_w; Ipos; Jpos; h; angle; s_rho; s_w; lon_rho; lat_rho; ocean_time","weight":1},{"access":[{"file_format":"NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6494515fd34ef77fcb014eb0"}],"access_details":null,"bbox":{"east":-66.7969,"north":49.3251,"south":24.7668,"west":-125.8594},"citation":"Hammond, J.C., 2024, Daily time series of surface water input from rainfall, rain on snow, and snowmelt for the Conterminous United States from 1990 to 2023, as well as annual series of input seasonality, precipitation seasonality, and average rainfall, rain on snow, and snowmelt rates: U.S. Geological Survey data release, https://doi.org/10.5066/P9JWJPNC.","creator":[],"creator_project":[{"id":"DJ60U83","name":"Snow Hydrology (Phase 2)"}],"date_created":"5/22/2024","date_updated":"6/3/2026","description":"This data release contains daily gridded data reflecting surface water input from rainfall, rain on snow (mixed), and snowmelt for the conterminous United States for water years 1990 to 2023 (1990/10/01 to 2023/09/30). This release also contains annual estimates of gridded input seasonality (an index reflecting whether surface water input occurs within a concentrated period or is equally distributed throughout the year), precipitation seasonality, average snowmelt, rainfall and rain on snow rates, and finally, annual totals of each input type. Average snowmelt, rainfall and rain on snow rates were computed using days where values were greater than zero. Daily data were generated using precipitation input from the gridMET dataset (Abatzoglou, 2013) and the University of Arizona snow water equivalent product (Broxton et al., 2019).","doi_url":"https://doi.org/10.5066/P9JWJPNC","domain":["Hydrology","Snow","Climate"],"draft":false,"id":"2b41a4d3-b348-48ac-85e8-7680df21a74f","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6494515fd34ef77fcb014eb0?f=__disk__c6%2F05%2F49%2Fc6054956b7a77d73ca6cf3fc0bd82b67856c50b0\u0026allowOpen=true"}],"name":"Daily time series of surface water input from rainfall, rain on snow, and snowmelt for the Conterminous United States from 1990 to 2023, as well as annual series of input seasonality, precipitation seasonality, and average rainfall, rain on snow, and snowmelt rates","permalink":"/catalog/datasets/2b41a4d3-b348-48ac-85e8-7680df21a74f/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"4 kilometer","temporal_coverage":"1990 - 2023","temporal_frequency":"daily; 3 months; annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["rain_surface","mix_surface","melt_surface","input_seasonality","precipitation_seasonality","average_melt","average_mixed_rate","average_rain_rate","annual_mixed_sum","annual_rainfall_sum","annual_snowmelt_sum"],"vars":"rain_surface; mix_surface; melt_surface; input_seasonality; precipitation_seasonality; average_melt; average_mixed_rate; average_rain_rate; annual_mixed_sum; annual_rainfall_sum; annual_snowmelt_sum","weight":1},{"access":[{"file_format":"CSV","name":"usgs.gov","url":"https://www.usgs.gov/centers/national-minerals-information-center/mineral-commodity-summaries"}],"access_details":null,"bbox":{},"citation":null,"creator":[],"creator_project":[],"description":"Annual publication providing information on events, trends, and issues for nonfuel mineral commodities including salt. Covers domestic industry structure, government programs, tariffs, and salient statistics for over 90 individual minerals and materials. Used here as a source of national salt production and distribution data relevant to road de-icing estimates.","doi_url":null,"domain":["Water Quality"],"draft":false,"id":"2bc21afa-5360-4cbe-b45c-2d9e37995251","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"553e2bbb-00ea-4052-b472-5561943d5dc6","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[],"links":[],"name":"USGS Mineral Commodity Summaries","permalink":"/catalog/datasets/2bc21afa-5360-4cbe-b45c-2d9e37995251/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"NA","temporal_coverage":"1996 - Present","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":[],"vars":"salt production; salt sales; salt imports; salt exports","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5f3ab12082ce8df5b6c4076a"}],"access_details":null,"bbox":{"east":-66.5332,"north":49.5537,"south":23.1606,"west":-126.3867},"citation":"Barnhart, T.B., Schultz, A.R., Siefken, S.A., Thompson, F., Welborn, T., Sando, T.R., Rea, A.H., McCarthy, P.M., 2021, Flow-Conditioned Parameter Grids for the Contiguous United States: A Pilot, Seamless Basin Characteristic Dataset: U.S. Geological Survey data release, https://doi.org/10.5066/P9HUWM6Q","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Pre-processed using the NHDPlus medium resolution flow direction grids. CEC North America landcover class, Daymet.\u003cbr\u003eTo aid in parameterization of mechanistic, statistical, and machine learning models of hydrologic systems in the contiguous United States (CONUS), flow-conditioned parameter grids (FCPGs) have been generated describing upstream basin mean elevation, slope, land cover class, latitude, and 30-year climatologies of mean total annual precipitation, minimum daily air temperature, and maximum daily air temperature. Additional datasets of upstream basin area and binary stream presence-absence are provided to help validate queries against the flow-conditioned data. These data are provided as virtual raster tile (vrt) mosaics of cloud optimized GeoTIFFs to allow point queries of the data (see Distribution Information) without requiring downloading the whole dataset.","doi_url":null,"domain":["Hydrology","Climate","Land Cover","Topography","Snow"],"draft":false,"id":"2c13a6c8-9f70-4efc-a8e4-c046e87e819a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Flow-Conditioned Parameter Grids for the Contiguous United States: A Pilot, Seamless Basin Characteristic Dataset","permalink":"/catalog/datasets/2c13a6c8-9f70-4efc-a8e4-c046e87e819a/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"1989 - 2018","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Temperature, mean upstream maximum","Temperature, mean upstream minimum","Mean upstream air temperature, maximum","Mean upstream air temperature, minimum","Upstream annual precipitation","Precipitation, mean upstream","Mean upstream area (fac)","Mean upstream elevation (elev)","Mean upstream latitude (lat)","Mean upstream slope (slope)","Percent of upstream barren lands (lc16)","Percent of upstream cropland (lc15)","Percent of upstream mixed forest (lc6)","Percent of upstream snow and ice (lc19)","Percent of upstream sub-polar or polar barren-lichen-moss (lc13)","Percent of upstream sub-polar or polar grassland-lichen-moss (lc12)","Percent of upstream sub-polar or polar shrubland-lichen-moss (lc11)","Percent of upstream sub-polar taiga needleleaf forest (lc2)","Percent of upstream temperate or sub-polar broadleaf deciduous forest (lc5)","Percent of upstream temperate or sub-polar grassland (lc10)","Percent of upstream Temperate or sub-polar needleleaf forest (lc1)","Percent of upstream temperate or sub-polar shrubland (lc8)","Percent of upstream tropical or sub-tropical broadleaf deciduous forest (lc4)","Percent of upstream tropical or sub-tropical broadleaf evergreen forest (lc3)","Percent of upstream tropical or sub-tropical grassland (lc9)","Percent of upstream tropical or sub-tropical shrubland (lc7)","Percent of upstream urban and built-up (lc17)","Percent of upstream water (lc18)","Percent of upstream wetland (lc14)"],"vars":"Temperature, mean upstream maximum; Temperature, mean upstream minimum; Mean upstream air temperature, maximum; Mean upstream air temperature, minimum; Upstream annual precipitation; Precipitation, mean upstream; Mean upstream area (fac); Mean upstream elevation (elev); Mean upstream latitude (lat); Mean upstream slope (slope); Percent of upstream barren lands (lc16); Percent of upstream cropland (lc15); Percent of upstream mixed forest (lc6); Percent of upstream snow and ice (lc19); Percent of upstream sub-polar or polar barren-lichen-moss (lc13); Percent of upstream sub-polar or polar grassland-lichen-moss (lc12); Percent of upstream sub-polar or polar shrubland-lichen-moss (lc11); Percent of upstream sub-polar taiga needleleaf forest (lc2); Percent of upstream temperate or sub-polar broadleaf deciduous forest (lc5); Percent of upstream temperate or sub-polar grassland (lc10); Percent of upstream Temperate or sub-polar needleleaf forest (lc1); Percent of upstream temperate or sub-polar shrubland (lc8); Percent of upstream tropical or sub-tropical broadleaf deciduous forest (lc4); Percent of upstream tropical or sub-tropical broadleaf evergreen forest (lc3); Percent of upstream tropical or sub-tropical grassland (lc9); Percent of upstream tropical or sub-tropical shrubland (lc7); Percent of upstream urban and built-up (lc17); Percent of upstream water (lc18); Percent of upstream wetland (lc14)","weight":1},{"access":[{"name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/66564f56d34ef3137d35eae2"}],"bbox":{"east":-63.457,"north":49.4967,"south":23.8858,"west":-126.0352},"citation":"Foks, S.S., LaFontaine, J.H., McDonald, R.R., Snyder, A.M., Staub, L.E., Kolb, K.R., LaMotte, A.E., and Viger, R.J., 2025, Daily twelve-digit hydrologic unit code aggregations of snow water equivalent, soil moisture, and actual evapotranspiration estimates from the National Hydrologic Model Precipitation Runoff Modeling System forced with CONUS404-BA: U.S. Geological Survey data release, https://doi.org/10.5066/P13WDBTQ.","creator":[{"creator_email":"sfoks@usgs.gov","creator_name":"Sydney S Foks"}],"creator_project":[],"date_created":"1/20/2026","date_updated":"6/3/2026","description":"\u003cp\u003eThis data release contains three variables from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) version 1.1 modeling application forced with CONUS404-BA (Markstrom and others, 2024) from January 1st, 1980 through September 25th, 2021 that are summarized to a twelve-digit hydrologic unit code for the spatial extent of the conterminous United States at a daily timestep. The three variables presented here are snow water equivalent, actual evapotranspiration, and soil moisture fraction. There are three netCDF files of daily, modeled data; one for each of the following variables: actual evapotranspiration - \"huc12_daily_nhmprms-conus404ba_actet.nc\", soil moisture fraction - \"huc12_daily_nhmprms-conus404ba_soil_moisture_fraction.nc\",\u0026nbsp;and snow water equivalent - \"huc12_daily_nhmprms-conus404ba_pkwater_equiv.nc\".\u003c/p\u003e\nAdditionally, two supplementary files are also included in this data release. The first file (“weights_hru_to_huc12_nhmprms_conus404ba.csv”) contains the spatial weights or fraction that is used to “weight” the modeling output in the area-weighting process. The second file (“summed_weights_per_huc12_nhmprms_conus404ba.csv”) contains the total fractional area within each twelve-digit hydrologic unit code that is covered by the modeling output and is important for filtering results in the data file (where a fractional coverage may be less than one).","doi_url":"https://doi.org/10.5066/P13WDBTQ","domain":["Hydrology"],"draft":false,"id":"2c95bc77-cb0b-4965-ac7a-ef2cf44867bd","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"f930b17a-c6ea-4623-b89d-d7d85ac698aa","rel_type":"IsDerivedFrom"}],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/66564f56d34ef3137d35eae2?f=__disk__c3%2F9a%2Faf%2Fc39aaf2e78269b647f56b421ebb8bc526a4bc1ec\u0026allowOpen=true"}],"name":"Daily twelve-digit hydrologic unit code aggregations of snow water equivalent, soil moisture, and actual evapotranspiration estimates from the National Hydrologic Model Precipitation Runoff Modeling System forced with CONUS404-BA","permalink":"/catalog/datasets/2c95bc77-cb0b-4965-ac7a-ef2cf44867bd/","project_use_history":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1980 - 2021","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["huc_id","nhru_v1_1","weight","huc_id","summed_weight","huc_id","time","nhm_actet","huc_id","time","nhm_pkwater_equiv","huc_id","time","nhm_soil_moisture_fraction"],"vars":"huc_id; nhru_v1_1; weight; huc_id; summed_weight; huc_id; time; nhm_actet; huc_id; time; nhm_pkwater_equiv; huc_id; time; nhm_soil_moisture_fraction","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6961456bd4be02298529c5b0"}],"access_details":"The ScienceBase access url is not yet published, so you will need to be added as a viewer by Stacey Archfield to view the dataset currently.","citation":"Archfield., S.A., Blodgett, D.L., Terziotti, S., and Aichele, S.S., 2026, Probabilities of stream permanence for the contiguous United States based on basin drainage area, U.S. Geological Survey data release, https://doi.org/10.5066/P1BNCITF.","creator":[{"creator_email":"sarch@usgs.gov","creator_name":"Stacey Archfield"}],"creator_project":[{"id":"XXXXXXX","name":"External Creator"}],"date_created":"3/10/2026","date_updated":"6/3/2026","description":"This dataset contains estimated probabilities of detecting at least x days of zero streamflow, where x equals 1, 3, 7, and 30 days, for river locations across the contiguous United States as a function of drainage area. Also included are the 95-percent confidence intervals around the probability estimates. Probabilities are estimated by survival regression models, which are simple, non-parametric, empirically-based models that assume a decreasing probability of observing at least n days of zero streamflow as drainage area increases.","doi_url":"https://doi.org/10.5066/P1BNCITF","domain":["Hydrology"],"draft":false,"id":"2e5c17d8-da95-4721-8f6b-345c063aa15a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[{"id":"29c67c9b-7a32-4844-a711-672a8dee2fb4","rel_type":"IsSourceOf"}],"links":[],"name":"Probabilities of stream permanence for the contiguous United States based on drainage area","permalink":"/catalog/datasets/2e5c17d8-da95-4721-8f6b-345c063aa15a/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1900 - 2022","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Number of observations in the training dataset","Drainage area","Probability of observing at least x day(s) of zero flow","Standard error of the estimate of the probability of observing at least x day(s) of zero flow","Lower 95% confidence interval of the probability of observing at least x day(s) of zero flow","Upper 95% confidence interval of the probability of observing at least 1 day of zero flow"],"vars":"Number of observations in the training dataset; Drainage area; Probability of observing at least x day(s) of zero flow; Standard error of the estimate of the probability of observing at least x day(s) of zero flow; Lower 95% confidence interval of the probability of observing at least x day(s) of zero flow; Upper 95% confidence interval of the probability of observing at least 1 day of zero flow","weight":1},{"access":[{"file_format":"NC","name":"cesm.ucar.edu","url":"https://www.cesm.ucar.edu/community-projects/lens2/data-sets"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Rodgers, K.B., Lee, S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., Edwards, J., Kim, J., Simpson, I.R., Stein, K., Stuecker, M.F., Yamaguchi, R., Bodai, T., Chung, E., Huang, L., Kim, W.M., Lamarque, J., Lombardozzi, D.L., Wieder, W.R, and Yeager, S.G., 2021, Ubiquity of human-induced changes in climate variability: European Geosciences Union, v. 12, no. 4, p. 1393-1411, https://doi.org/10.5194/esd-12-1393-2021","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The CESM2 Large Ensemble (LENS2) consists of 100 members at 1-degree spatial resolution covering the period 1850-2100 under CMIP6 historical and SSP370 future radiative forcing scenarios. Data sets from this ensemble are available via the Climate Data Gateway.\u003cbr\u003eUnlike the CESM1 Large Ensemble (LENS1), LENS2 uses a combination of different oceanic and atmospheric initial states to create ensemble spread as follows.\u003cbr\u003e1. Members 1-10: These begin from years 1001, 1021, 1041, 1061, 1081, 1101, 1121, 1141, 1161, and 1181 of the 1400-year pre-industrial control simulation. This segment of the control simulation was chosen to minimize drift.\u003cbr\u003e2. Members 11-90: These begin from 4 pre-selected years of the pre-industrial control simulation based on the phase of the Atlantic meridional overturning circulation (AMOC). For each of the 4 initial states, there are 20 ensemble members created by randomly perturbing the atmospheric potential temperature field by order 10^-14K. The chosen start dates (model years 1231, 1251, 1281, and 1301) sample AMOC and sea surface height (SSH) in the Labrador Sea at their maximum, minimum, and transition states.\u003cbr\u003e3. Members 91-100: These begin from years 1011, 1031, 1051, 1071, 1091, 1111, 1131, 1151, 1171, and 1191 of the 1400-year pre-industrial control simulation. This group includes an extensive \"MOAR\" output (mother of all runs) which can be used to drive regional climate models.\u003cbr\u003eThis initialization design is intended to enable an assessment of oceanic (AMOC) and atmospheric contributions to ensemble spread, and the impact of AMOC initial-condition memory on the global Earth system.","doi_url":null,"domain":["Climate"],"draft":false,"id":"2ee06419-428c-4163-a8b7-00a4a9c2edd9","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.cesm.ucar.edu/community-projects/lens2"}],"name":"CESM2 Large Ensemble Community Project (LENS2) climate data","permalink":"/catalog/datasets/2ee06419-428c-4163-a8b7-00a4a9c2edd9/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NCAR; ICCP","spatial_extent":"Global","spatial_resolution":"1 degree","temporal_coverage":"1850 - 2100","temporal_frequency":"3 hours; 6 hours; daily; monthly; annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["net solar flux at surface","surface latent heat flux","Vertical velocity (pressure)","convective precipitation rate (liq + ice)","large-scale (stable) precipitation rate (liq + ice)","Total (convective and large-scale) precipitation rate (liq + ice)","surface pressure","sea level pressure","Specific humidity","Relative humidity","surface sensible heat flux","sea surface temperature","temperature","reference height temperature","surface temperature (radiative)","zonal wind","meridional wind","geopotential height (above sea level)","total ecosystem respiration (autotrophic + heterotrophic)","gross primary production","net biome production","net primary production","total projected leaf area index","soil temperature (vegetated landunits only)","DIC Surface Gas Flux","mixed-layer depth","Surface pH","salinity","Windstress in grid-x direction","Windstress in grid-y direction","potential temperature","ice area","grid cell mean ice thickness"],"vars":"net solar flux at surface; surface latent heat flux; Vertical velocity (pressure); convective precipitation rate (liq + ice); large-scale (stable) precipitation rate (liq + ice); Total (convective and large-scale) precipitation rate (liq + ice); surface pressure; sea level pressure; Specific humidity; Relative humidity; surface sensible heat flux; sea surface temperature; temperature; reference height temperature; surface temperature (radiative); zonal wind; meridional wind; geopotential height (above sea level); total ecosystem respiration (autotrophic + heterotrophic); gross primary production; net biome production; net primary production; total projected leaf area index; soil temperature (vegetated landunits only); DIC Surface Gas Flux; mixed-layer depth; Surface pH; salinity; Windstress in grid-x direction; Windstress in grid-y direction; potential temperature; ice area; grid cell mean ice thickness","weight":1},{"access":[{"file_format":"TIF; BIL","name":"prism.oregonstate.edu","url":"https://prism.oregonstate.edu/downloads/"},{"file_format":"ZARR","name":"WMA STAC","url":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/PRISM_v2"}],"access_details":null,"bbox":{"east":-66.52082824707031,"north":49.937503814697266,"south":24.10416603088379,"west":-125.02083587646484},"citation":"PRISM Climate Group, Oregon State University, http://prism.oregonstate.edu, created \u003cdate\u003e    Citation for methods: Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J. and Pasteris, P.P., 2008, Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States, International Journal of  Climatology, vol 28, issue 15, pp 2031-2064, https://doi.org/10.1002/joc.1688","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The PRISM Climate Group at Oregon State University produces data for a suite of climate variables: precipitation, daily temperature minimum/maximum/mean, dewpoint temperature, and vapor pressure deficit minimum/maximum. These data represent a combination of modeled climate dynamics and various observational data sets. Higher resolution (800 meter) is available for a fee.","doi_url":"https://doi.org/10.1002/joc.1688","domain":["Climate"],"draft":false,"id":"2f3d0798-45b7-4b54-9f47-042cbfc657b3","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[{"id":"d71a50f4-4014-4c6d-93c0-b3c728c477b4","rel_type":"IsSourceOf"},{"id":"a6b01211-f71f-4e7a-a02a-4fe7ede14f2b","rel_type":"IsSourceOf"},{"id":"78a93a34-3c9e-4859-b967-b68998fd7f9e","rel_type":"IsSourceOf"}],"linked_usecases":[{"id":"148a7db7-1175-47aa-8437-5ae39f62b1ce","rel_type":"IsSourceOf"}],"links":[{"name":"Documentation","url":"https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.1688"}],"name":"PRISM: 4km Monthly Parameter-elevation Regressions on Independent Slopes Model Monthly Climate Data for the Continental United States.","permalink":"/catalog/datasets/2f3d0798-45b7-4b54-9f47-042cbfc657b3/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"CONUS","spatial_resolution":"800 meter; 4 kilometer","temporal_coverage":"1895 - 2024","temporal_frequency":"daily; monthly; annual","update_detail":"append","update_frequency":"6 months","update_type":"Dynamic","variables":["Precipitation","Temperature","Vapor pressure"],"vars":"Precipitation; Temperature; Vapor pressure","weight":1},{"access":[{"file_format":"NC","name":"catalogue.ceda.ac.uk","url":"https://catalogue.ceda.ac.uk/uuid/28935552223242ca97953a8db99c2821/"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Dorigo, W., Preimesberger, W., Moesinger, L., Pasik, A., Scanlon, T., Hahn, S., Van der Schalie, R., Van der Vliet, M., De Jeu, R., Kidd, R., Rodriguez-Fernandez, N., Hirschi, M., 2021, ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): [ACTIVE/PASSIVE/COMBINED] product, Version 06.1. NERC EDS Centre for Environmental Data Analysis, Accessed [YYYY-MM-DD], https://doi.org/10.5285/28935552223242ca97953a8db99c2821","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Soil Moisture CCI datasets include three datasets (ACTIVE, PASSIVE, and COMBINED) that were created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. These products, provided as global daily images in NetCDF-4 classic file format, present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees.\u003cbr\u003eThe ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. It is provided in percent of saturation [%] and covers the period 1991-08-05 to 2020-12-31.\u003cbr\u003eThe PASSIVE product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. It is provided in volumetric units [m3 m-3] and covers the period 1978-11-01 to 2020-12-31.\u003cbr\u003eThe COMBINED product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. It is provided in volumetric units [m3 m-3] and covers the period 1978-11-01 to 2020-12-31.\u003cbr\u003eFor information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.","doi_url":"https://doi.org/10.5285/28935552223242ca97953a8db99c2821","domain":["Soils"],"draft":false,"id":"2f3fd543-15a7-4031-8ad2-e158931f1905","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci)","permalink":"/catalog/datasets/2f3fd543-15a7-4031-8ad2-e158931f1905/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"ESA","spatial_extent":"Global","spatial_resolution":"0.25 degrees","temporal_coverage":"1991-2020 (active); 1978-2020 (passive, combined)","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Soil moisture"],"vars":"Soil moisture","weight":1},{"access":[{"file_format":"NC","name":"nsidc.org","url":"https://nsidc.org/data/NSIDC-0668"}],"access_details":null,"bbox":{"east":180,"north":90,"south":0,"west":-180},"citation":"Mudryk, L. R. and Derksen, C., 2017, CanSISE Observation-Based Ensemble of Northern Hemisphere Terrestrial Snow Water Equivalent, Version 2. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. Accessed [YYYY-MM-DD] at https://doi.org/10.5067/96ltniikJ7vd","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Canadian Sea Ice and Snow Evolution Network (CanSISE) estimates snow water equivalent (SWE) by merging five observation-based estimates through an adapted ensemble mean methodology. Merged products are (1) GlobSnow-combined SWE (merges ex-situ passive microwave and in-situ weather station observations), (2) ERA-Interim reanalysis, (3) MERRA reanalysis, (4) Crocus SWE data set: output from the Crocus snowpack model, driven by ERA-Interim meteorology, and (5) NASA Global Land Data Assimilation System (GLDAS) reanalysis. Resolutions are daily at 1 degree from 1981 to 2010 for the Northern Hemisphere.","doi_url":"https://doi.org/10.5067/96ltniikJ7vd","domain":["Snow"],"draft":false,"id":"31920748-d91d-44b6-a378-8d1e9e639684","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://nsidc.org/sites/default/files/nsidc-0668-v002-userguide_1.pdf"}],"name":"CanSISE Observation-Based Ensemble of Northern Hemisphere Terrestrial Snow Water Equivalent, Version 2","permalink":"/catalog/datasets/31920748-d91d-44b6-a378-8d1e9e639684/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NSIDC","spatial_extent":"North America","spatial_resolution":"1 degree","temporal_coverage":"1981 - 2010","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Snow water equivalent"],"vars":"Snow water equivalent","weight":1},{"access":[{"file_format":"TXT","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6662fbd3d34e4d5e8484139c"}],"access_details":null,"bbox":{"east":-88.25,"north":41.7713,"south":37.9962,"west":-92.0215},"citation":"Schmadel, N.M., Miller, O.M., Schwarz, G.E., Ator, S.W., Sekellick, A.J., and Saad, D.A., 2024, Illinois River basin seasonally dynamic total nitrogen and phosphorus SPARROW model inputs and outputs, 2000 through 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P1TFJT2W.","creator":[],"creator_project":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"date_created":"1/23/2025","date_updated":"6/3/2026","description":"This data release contains seasonal source-specific estimates of total nitrogen (TN) and total phosphorus (TP) loading to streams across the Illinois River basin (25,620 reaches) for 21 years from 2000 through 2020 using a dynamic SPARROW (Spatially Referenced Regressions on Watershed attributes) model. Input data including calibration loads, output predictions, model control files, and model source code are provided to fully reproduce results. The modeled period was from December 1999 through November 2020 in seasonal timesteps, or 84 periods, and the following seasonal definitions were applied: winter includes December, January, and February; spring includes March, April, and May; summer includes June, July, and August; and fall includes September, October, and November. See Schmadel and others (2024) for further details","doi_url":"https://doi.org/10.5066/P1TFJT2W","domain":["Hydrology","Water Quality"],"draft":false,"id":"3194bc0c-c5b6-41fd-bc8e-7ea7f27be5de","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6662fbd3d34e4d5e8484139c?f=__disk__01%2F34%2F7a%2F01347af3d6de66e91a8b5bcfe6f23b474838de49\u0026allowOpen=true"}],"name":"Illinois River basin seasonally dynamic total nitrogen and phosphorus SPARROW model inputs and outputs, 2000 through 2020","permalink":"/catalog/datasets/3194bc0c-c5b6-41fd-bc8e-7ea7f27be5de/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Illinois River basin","spatial_resolution":"1:100,000","temporal_coverage":"2000 - 2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["COMID","wyear","quarter","period","rchtot","vel_fps","q_cfs","rhload","tn_fluxm","tn_fluxm_se","lat_tn","lon_tn","tn_load","tn_wrtds","station_id_tn","flag_fluxm_tn","tp_fluxm","tp_fluxm_se","lat_tp","lon_tp","tp_load","tp_wrtds","station_id_tp","flag_fluxm_tp","npdesTN_kg","npdesTP_kg","TN_atm","fcmaq","prefcmaq","manun","mancn","manup","mancp","fertn","fertp","nonfarmn","nonfarmp","urb","fix1","fix2","Run","preRun","PPT","prePPT","TAV","preTAV","AET","preAET","tile_hr","kfact_up","rockdep","bd","clay","LENGTHKM","Hydroseq","TermFlag","IncAreaKm2","CumAreaKm2","DivFrac","cfromnode","ctonode","HR_SS_areakm2","HR_UWB_areakm2","CAT_PMAP","time_comid","time_hydroseq","time_cfromnode","time_ctonode","flow_cfs","cumarea","incarea","comid","iftran","ls_weight","STAID","LOAD","PLOAD_TOTAL","PLOAD_WWTP1_KG","PLOAD_FERT_KG1","PLOAD_MAN_KG1","PLOAD_ATM_KG","PLOAD_URB_KM2","PLOAD_NFIX_KM2","PLOAD_STORAGE","PLOAD_TOTAL_ND","PLOAD_INC_TOTAL","PLOAD_INC_WWTP1_KG","PLOAD_INC_FERT_KG1","PLOAD_INC_MAN_KG1","PLOAD_INC_ATM_KG","PLOAD_INC_URB_KM2","PLOAD_INC_NFIX_KM2","PLOAD_INC_STORAGE","PLOAD_INC_TOTAL_ND","SE_PLOAD_TOTAL","ci_lo_PLOAD_TOTAL","ci_hi_PLOAD_TOTAL","SE_PLOAD_WWTP1_KG","ci_lo_PLOAD_WWTP1_KG","ci_hi_PLOAD_WWTP1_KG","SE_PLOAD_FERT_KG1","ci_lo_PLOAD_FERT_KG1","ci_hi_PLOAD_FERT_KG1","SE_PLOAD_MAN_KG1","ci_lo_PLOAD_MAN_KG1","ci_hi_PLOAD_MAN_KG1","SE_PLOAD_ATM_KG","ci_lo_PLOAD_ATM_KG","ci_hi_PLOAD_ATM_KG","SE_PLOAD_URB_KM2","ci_lo_PLOAD_URB_KM2","ci_hi_PLOAD_URB_KM2","SE_PLOAD_NFIX_KM2","ci_lo_PLOAD_NFIX_KM2","ci_hi_PLOAD_NFIX_KM2","SE_PLOAD_STORAGE","ci_lo_PLOAD_STORAGE","ci_hi_PLOAD_STORAGE","SE_PLOAD_TOTAL_ND","ci_lo_PLOAD_TOTAL_ND","ci_hi_PLOAD_TOTAL_ND","SE_PLOAD_INC_TOTAL","ci_lo_PLOAD_INC_TOTAL","ci_hi_PLOAD_INC_TOTAL","SE_PLOAD_INC_WWTP1_KG","ci_lo_PLOAD_INC_WWTP1_KG","ci_hi_PLOAD_INC_WWTP1_KG","SE_PLOAD_INC_FERT_KG1","ci_lo_PLOAD_INC_FERT_KG1","ci_hi_PLOAD_INC_FERT_KG1","SE_PLOAD_INC_MAN_KG1","ci_lo_PLOAD_INC_MAN_KG1","ci_hi_PLOAD_INC_MAN_KG1","SE_PLOAD_INC_ATM_KG","ci_lo_PLOAD_INC_ATM_KG","ci_hi_PLOAD_INC_ATM_KG","SE_PLOAD_INC_URB_KM2","ci_lo_PLOAD_INC_URB_KM2","ci_hi_PLOAD_INC_URB_KM2","SE_PLOAD_INC_NFIX_KM2","ci_lo_PLOAD_INC_NFIX_KM2","ci_hi_PLOAD_INC_NFIX_KM2","SE_PLOAD_INC_STORAGE","ci_lo_PLOAD_INC_STORAGE","ci_hi_PLOAD_INC_STORAGE","SE_PLOAD_INC_TOTAL_ND","ci_lo_PLOAD_INC_TOTAL_ND","ci_hi_PLOAD_INC_TOTAL_ND","total_yield","inc_total_yield","concentration","sh_wwtp1_kg","sh_fert_kg1","sh_man_kg1","sh_atm_kg","sh_urb_km2","sh_nfix_km2","sh_storage","PLOAD_NATP_MT","PLOAD_INC_NATP_MT","SE_PLOAD_NATP_MT","ci_lo_PLOAD_NATP_MT","ci_hi_PLOAD_NATP_MT","SE_PLOAD_INC_NATP_MT","ci_lo_PLOAD_INC_NATP_MT","ci_hi_PLOAD_INC_NATP_MT","sh_natp_mt","method_tn","method_tp"],"vars":"COMID; wyear; quarter; period; rchtot; vel_fps; q_cfs; rhload; tn_fluxm; tn_fluxm_se; lat_tn; lon_tn; tn_load; tn_wrtds; station_id_tn; flag_fluxm_tn; tp_fluxm; tp_fluxm_se; lat_tp; lon_tp; tp_load; tp_wrtds; station_id_tp; flag_fluxm_tp; npdesTN_kg; npdesTP_kg; TN_atm; fcmaq; prefcmaq; manun; mancn; manup; mancp; fertn; fertp; nonfarmn; nonfarmp; urb; fix1; fix2; Run; preRun; PPT; prePPT; TAV; preTAV; AET; preAET; tile_hr; kfact_up; rockdep; bd; clay; LENGTHKM; Hydroseq; TermFlag; IncAreaKm2; CumAreaKm2; DivFrac; cfromnode; ctonode; HR_SS_areakm2; HR_UWB_areakm2; CAT_PMAP; time_comid; time_hydroseq; time_cfromnode; time_ctonode; flow_cfs; cumarea; incarea; comid; iftran; ls_weight; STAID; LOAD; PLOAD_TOTAL; PLOAD_WWTP1_KG; PLOAD_FERT_KG1; PLOAD_MAN_KG1; PLOAD_ATM_KG; PLOAD_URB_KM2; PLOAD_NFIX_KM2; PLOAD_STORAGE; PLOAD_TOTAL_ND; PLOAD_INC_TOTAL; PLOAD_INC_WWTP1_KG; PLOAD_INC_FERT_KG1; PLOAD_INC_MAN_KG1; PLOAD_INC_ATM_KG; PLOAD_INC_URB_KM2; PLOAD_INC_NFIX_KM2; PLOAD_INC_STORAGE; PLOAD_INC_TOTAL_ND; SE_PLOAD_TOTAL; ci_lo_PLOAD_TOTAL; ci_hi_PLOAD_TOTAL; SE_PLOAD_WWTP1_KG; ci_lo_PLOAD_WWTP1_KG; ci_hi_PLOAD_WWTP1_KG; SE_PLOAD_FERT_KG1; ci_lo_PLOAD_FERT_KG1; ci_hi_PLOAD_FERT_KG1; SE_PLOAD_MAN_KG1; ci_lo_PLOAD_MAN_KG1; ci_hi_PLOAD_MAN_KG1; SE_PLOAD_ATM_KG; ci_lo_PLOAD_ATM_KG; ci_hi_PLOAD_ATM_KG; SE_PLOAD_URB_KM2; ci_lo_PLOAD_URB_KM2; ci_hi_PLOAD_URB_KM2; SE_PLOAD_NFIX_KM2; ci_lo_PLOAD_NFIX_KM2; ci_hi_PLOAD_NFIX_KM2; SE_PLOAD_STORAGE; ci_lo_PLOAD_STORAGE; ci_hi_PLOAD_STORAGE; SE_PLOAD_TOTAL_ND; ci_lo_PLOAD_TOTAL_ND; ci_hi_PLOAD_TOTAL_ND; SE_PLOAD_INC_TOTAL; ci_lo_PLOAD_INC_TOTAL; ci_hi_PLOAD_INC_TOTAL; SE_PLOAD_INC_WWTP1_KG; ci_lo_PLOAD_INC_WWTP1_KG; ci_hi_PLOAD_INC_WWTP1_KG; SE_PLOAD_INC_FERT_KG1; ci_lo_PLOAD_INC_FERT_KG1; ci_hi_PLOAD_INC_FERT_KG1; SE_PLOAD_INC_MAN_KG1; ci_lo_PLOAD_INC_MAN_KG1; ci_hi_PLOAD_INC_MAN_KG1; SE_PLOAD_INC_ATM_KG; ci_lo_PLOAD_INC_ATM_KG; ci_hi_PLOAD_INC_ATM_KG; SE_PLOAD_INC_URB_KM2; ci_lo_PLOAD_INC_URB_KM2; ci_hi_PLOAD_INC_URB_KM2; SE_PLOAD_INC_NFIX_KM2; ci_lo_PLOAD_INC_NFIX_KM2; ci_hi_PLOAD_INC_NFIX_KM2; SE_PLOAD_INC_STORAGE; ci_lo_PLOAD_INC_STORAGE; ci_hi_PLOAD_INC_STORAGE; SE_PLOAD_INC_TOTAL_ND; ci_lo_PLOAD_INC_TOTAL_ND; ci_hi_PLOAD_INC_TOTAL_ND; total_yield; inc_total_yield; concentration; sh_wwtp1_kg; sh_fert_kg1; sh_man_kg1; sh_atm_kg; sh_urb_km2; sh_nfix_km2; sh_storage; PLOAD_NATP_MT; PLOAD_INC_NATP_MT; SE_PLOAD_NATP_MT; ci_lo_PLOAD_NATP_MT; ci_hi_PLOAD_NATP_MT; SE_PLOAD_INC_NATP_MT; ci_lo_PLOAD_INC_NATP_MT; ci_hi_PLOAD_INC_NATP_MT; sh_natp_mt; method_tn; method_tp","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/656e22acd34e7ca10833f955"}],"access_details":null,"bbox":{"east":-154.4019,"north":22.5329,"south":18.5421,"west":-160.4004},"citation":"Rosa, S.N., Markstrom, S.L., McDonald, R.R., Norton, P.A., Dickinson, J.E., Regan, R.S., Bock, A.R., Santiago, M., Wieczorek, M.E., and LaFontaine, J.H., 2025, Hawai'i National Hydrologic Model (NHM) application,1980–2021: U.S. Geological Survey data release, https://doi.org/10.5066/P9NP24AS.","creator":[{"creator_email":"snrosa@usgs.gov","creator_name":"Sarah N Rosa"}],"creator_project":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data release contains inputs for and outputs from hydrologic simulations for the Hawai‘i (HI) domain using the Precipitation Runoff Modeling System (PRMS) version 5.2.1.1 for the precalibration, by Hydrologic Response Unit (byHRU) release, and by Point Of Interest Observation (byPOIobs) release using the USGS National Hydrologic Model infrastructure (NHM; Regan and others, 2018). These simulations were developed to provide estimates of the water budget for the calendar-year period 1980 to 2021, where the first two years are used for model initialization. Specific file types include: 1) input atmospheric forcings of minimum air temperature, maximum air temperature, and daily precipitation accumulation derived from Daymet Version 4 gridded estimates of daily weather parameters (Thornton and others, 2020) and input parameter and control files for each release (Markstrom and others, 2015), 2) monthly calibration target baselines derived from Global Circulation Model (GCM) simulations (Koczot and others, 2025) that were used in addition to USGS measured streamflow, 3) output files of simulated water budget components for each hydrologic response unit and stream segment and 4) performance statistics at selected streamgage locations. Figure 1 shows the calibration methodology that was used for the model application (see Hay and others, 2023 for additional information). Figure 2 shows all the HRUSs in the geospatial fabric for the HI domain (Bock and others, 2024). Table 1 lists the streamgages that are included in the model application. The first two years of the simulations are considered 'model initialization' and should not be included in any subsequent analysis. The executable used for these simulations may be downloaded from https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms (version 5.2.1.1). A batch file to run the model has also been included.","doi_url":"https://doi.org/10.5066/P9NP24AS","domain":["Hydrology","Snow","Soils"],"draft":false,"id":"331db98f-dd3b-4d3d-a9e1-15524e84edba","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/656e22acd34e7ca10833f955?f=__disk__25%2F0d%2F8f%2F250d8f7c21ca834e994b9cd68bedd4b7df0e6079\u0026allowOpen=true"}],"name":"Hawai'i National Hydrologic Model (NHM) application, 1980-2021","permalink":"/catalog/datasets/331db98f-dd3b-4d3d-a9e1-15524e84edba/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"HI","spatial_resolution":"1:100,000","temporal_coverage":"1980 - 2021","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["variable_name","datatype","description","default","parameter_name","datatype","units","description","valid_minimum","valid_maximum","default","dimension","modules","variable_name","datatype","description","units","dimension","poi_id","ns","nslog","mon_ns","mon_nslog","bias","mon_bias","da_actual_obs","drainage_area_model","falcone_class","latitude","longitude","variable_name","datatype","long_name","units","dimensions","Site ID number","Site name","Altitude (feet)","NWIS Drainage Area (square miles)","Modeled Drainage Area (square miles)","Warm-up Period","Calibration Period","Used for byPOIobs Calibration","Mean Annual Actual Evapotranspiration (in)","Mean Annual Precipitation (in)"],"vars":"variable_name; datatype; description; default; parameter_name; datatype; units; description; valid_minimum; valid_maximum; default; dimension; modules; variable_name; datatype; description; units; dimension; poi_id; ns; nslog; mon_ns; mon_nslog; bias; mon_bias; da_actual_obs; drainage_area_model; falcone_class; latitude; longitude; variable_name; datatype; long_name; units; dimensions; Site ID number; Site name; Altitude (feet); NWIS Drainage Area (square miles); Modeled Drainage Area (square miles); Warm-up Period; Calibration Period; Used for byPOIobs Calibration; Mean Annual Actual Evapotranspiration (in); Mean Annual Precipitation (in)","weight":1},{"access":[{"file_format":"CSV; TSV","name":"waterdata.usgs.gov","url":"https://waterdata.usgs.gov/nwis"}],"access_details":null,"bbox":{"east":-63,"north":73,"south":16,"west":-179},"citation":"U.S. Geological Survey, 2001, National Water Information System data available on the World Wide Web (Water Data for the Nation), accessed [YYYY-MM-DD] at https://doi.org/10.5066/F7P55KJN","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This database provides access to water-resources data collected at approximately 1.9 million sites in all 50 States, the District of Columbia, Puerto Rico, the Virgin Islands, Guam, American Samoa and the Commonwealth of the Northern Mariana Islands.","doi_url":"https://doi.org/10.5066/F7P55KJN","domain":["Hydrology"],"draft":false,"id":"343736de-5bf0-405f-823c-c830748e0245","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"NWIS","permalink":"/catalog/datasets/343736de-5bf0-405f-823c-c830748e0245/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"NA","temporal_coverage":"1889 - Present","temporal_frequency":"15 minutes; daily; monthly; annual","update_detail":"append and modify","update_frequency":"hourly","update_type":"Dynamic","variables":["Groundwater parameters","Surface water parameters","Water-quality parameters","Water-use parameters"],"vars":"Groundwater parameters; Surface water parameters; Water-quality parameters; Water-use parameters","weight":1},{"access":[{"file_format":"NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/61f1d006d34e42a0e1fa0646"}],"access_details":null,"bbox":{"east":-66,"north":51,"south":24,"west":-125},"citation":"Wieczorek, M.E., Signell, R.P., McCabe, G.J., and Wolock, D.M., 2022, USGS monthly water balance model inputs and outputs for the conterminous United States, 1895-2020, based on ClimGrid data: U.S. Geological Survey data release, https://doi.org/10.5066/P9JTV1T6.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Monthly inputs and outputs from a United States Geological Survey water-balance model (McCabe and Wolock, 2011) for the conterminous United States for the period 1895-01-01 to 2020-12-31. The source data used to run the water balance model is based on the National Oceanic and Atmospheric Administration's (NOAA, 2020) ClimGrid data for precipitation and temperature. This NetCDF contains the following monthly inputs: temperature (degrees Celsius) and precipitation (millimeters, mm) and the following outputs (all in mm): runoff, soil moisture storage, actual evapotranspiration, potential evapotranspiration, snow water equivalent, and snowfall. The spatial reference for this data set is ESPG 4326.","doi_url":"https://doi.org/10.5066/P9JTV1T6","domain":["Hydrology"],"draft":false,"id":"3472d776-7276-4214-816d-2f85964c031c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/61f1d006d34e42a0e1fa0646?f=__disk__c6%2F03%2F2c%2Fc6032ca5a8a713c8dc4d8d624179f35a63416aa0\u0026allowOpen=true"}],"name":"USGS monthly water balance model inputs and outputs for the conterminous United States, 1895-2020, based on ClimGrid data","permalink":"/catalog/datasets/3472d776-7276-4214-816d-2f85964c031c/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"unknown","temporal_coverage":"1895 - 2020","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["actual evapotranspiration","potential evapotranspiration","total precipitation amount including snow and rain","total precipitation amount for rain","runoff amount","snowfall amount","liquid water content of soil layer","liquid water equivalent of the snowpack","mean temperature"],"vars":"actual evapotranspiration; potential evapotranspiration; total precipitation amount including snow and rain; total precipitation amount for rain; runoff amount; snowfall amount; liquid water content of soil layer; liquid water equivalent of the snowpack; mean temperature","weight":1},{"access":[{"file_format":"GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5ece996d82ce30fd9808527b"}],"access_details":null,"bbox":{"east":-77.6953,"north":49.153,"south":24.287,"west":-125.0684},"citation":"Brandt, J.T., Caldwell, R.R., Haynes, J.V., Painter, J.A., and Read, A.L., 2021, Verified Irrigated Agricultural Lands for the United States, 2002–17: U.S. Geological Survey data release, https://doi.org/10.5066/P9NAWU1U.","creator":[],"creator_project":[],"date_created":"10/31/2024","date_updated":"6/3/2026","description":"The spatial extents of verified irrigated lands were compiled from various federal and state sources across the nation and combined into a single Geographic Information System (GIS) geodatabase for the purpose of model training and validation. In cooperation with U.S. Geological Survey (USGS), researchers at the University of Wisconsin (UW) generated a nation-wide map of irrigated lands using remote-sensing techniques that will be incorporated into future irrigation water-use models. The verified spatial data varies in scope, accuracy, and time period represented, but in general represents GIS coverages (polygons) of agricultural land irrigated for at least some period during 2002–17. Data from 14 states were provided to UW (Arizona, California, Colorado, Florida, Georgia, Idaho, Illinois, Mississippi, Montana, New Mexico, Texas, Utah, Washington, and Wyoming). It is important to validate that the remote sensing techniques correctly identify both irrigated and non-irrigated land. Varying data sources prevent this approach from being applied throughout the United States, but most datasets used for validation include at least some “non irrigated” land identification.","doi_url":"https://doi.org/10.5066/P9NAWU1U","domain":["Hydrology","Land Cover","Water Use"],"draft":false,"id":"35701fec-2efa-4d46-8808-15ab32d9278b","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5ece996d82ce30fd9808527b?f=__disk__49%2Fd9%2F40%2F49d940676e59cb16dcaaec46884bc4c7061a7a8d\u0026allowOpen=true"}],"name":"Verified Irrigated Agricultural Lands for the United States, 2002-17","permalink":"/catalog/datasets/35701fec-2efa-4d46-8808-15ab32d9278b/","project_use_history":[{"id":"DJ50UY1","name":"Water Use Model Development"}],"project_using":[{"id":"DJ50UY1","name":"Water Use Model Development"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Arizona; California; Colorado; Florida; Georgia; Idaho; Illinois; Mississippi; Montana; New Mexico; Texas; Utah; Washington; Wyoming","spatial_resolution":"varies","temporal_coverage":"2002 - 2017","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["IRG_STATUS","IRG_SYS","CROP_TYPE","DATA_DATE","DATA_YEAR"],"vars":"IRG_STATUS; IRG_SYS; CROP_TYPE; DATA_DATE; DATA_YEAR","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/638dfdffd34ed907bf7bc715"}],"access_details":null,"bbox":{"east":-72,"north":46,"south":36,"west":-110},"citation":"Medalie, L., Eng, K., Skinner, K.D., Ivahnenko, T., and Heilman, J. A., 2024, Data for modeling interbasin transfers of water in Colorado and the Northeast Region, United States: Geological Survey data release, https://doi.org/10.5066/P9309BYJ.","creator":[{"creator_email":"keng@usgs.gov","creator_name":"Ken Eng"}],"creator_project":[{"id":"DJ60UX2","name":"RIMBE: Regional IWAAs Integrated Methods for Base Evaluation"}],"date_created":"5/1/2025","date_updated":"6/3/2026","description":"Data used to predict flow characteristics of transfers of water between hydrologic basins at the hydrologic unit code 8 (HUC8 scale) using tree-based ensemble models—random forest models for Colorado and M5 cubist models for the Northeast Region (parts of Pennsylvania, New Jersey, and New York)—are presented and documented in this data release. This data release contains all input files necessary to reproduce the results of the flow prediction models described in the associated journal paper ([Eng and others, 2024](https://onlinelibrary.wiley.com/doi/10.1111/1752-1688.13250)). Nine CSV files of five different types are presented: interbasin flow information from reported data sources, HUC8 basins for validation runs of the classification models, HUC8 basins for validation runs of the flow prediction models, model predictor variables, and a data dictionary. Interbasin transfer flows are given in millions of gallons.","doi_url":"https://doi.org/10.5066/P9309BYJ","domain":["Hydrology","Infrastructure","Water Use"],"draft":false,"id":"35d72f7f-f017-46f7-99f6-7c1587c01971","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://onlinelibrary.wiley.com/doi/10.1111/1752-1688.13250"},{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/638dfdffd34ed907bf7bc715?f=__disk__dd%2F65%2F4c%2Fdd654c0d62d31bfa43442d99d2c661496cc9d7d7\u0026allowOpen=true"}],"name":"Data for modeling interbasin transfers of water in Colorado and the Northeast Region, United States","permalink":"/catalog/datasets/35d72f7f-f017-46f7-99f6-7c1587c01971/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"PA; NJ; NY; CO","spatial_resolution":"NA","temporal_coverage":"1985 - 2021","temporal_frequency":"monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["HUC8","IBT_Direction","DataSource","mean monthly interbasin flows","mean seasonal interbasin flows","max monthly interbasin flows","Bootstrap classification number","ModelType","precipitation","soil moisture","evapotranspiration","average annual runoff","snowfall","temperature","hydroelectric water use","industrial water use","irrigation water use","livestock water use","mining water use","HUC8 Population","Total Withdrawals"],"vars":"HUC8; IBT_Direction; DataSource; mean monthly interbasin flows; mean seasonal interbasin flows; max monthly interbasin flows; Bootstrap classification number; ModelType; precipitation; soil moisture; evapotranspiration; average annual runoff; snowfall; temperature; hydroelectric water use; industrial water use; irrigation water use; livestock water use; mining water use; HUC8 Population; Total Withdrawals","weight":1},{"access":[{"file_format":"TIF","name":"usgs.gov","url":"https://www.usgs.gov/landsat-missions/landsat-collection-2-level-3-dynamic-surface-water-extent-science-product"}],"access_details":null,"bbox":{"east":-65,"north":72,"south":17,"west":-178},"citation":"Jones, J.W., 2019. Improved Automated Detection of Subpixel-Scale Inundation-Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests: Remote Sensing, v 11, no 4, https://doi.org/10.3390/rs11040374","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This operational data set provides (1) dynamic mapping of CONUS non-ocean surface water or wetland presence through time, with associated confidence levels, (2) time-varying storage estimates for CONUS non-ocean surface water, and (3) average values over various timescales, of extent of CONUS non-ocean surface water.","doi_url":"https://doi.org/10.3390/rs11040374","domain":["Hydrology"],"draft":false,"id":"37510f13-bd61-499f-b5f6-5084214e53ba","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Landsat Collection 2 Level-3 Dynamic Surface Water Extent (DSWE)","permalink":"/catalog/datasets/37510f13-bd61-499f-b5f6-5084214e53ba/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; AK; HI","spatial_resolution":"30 meter","temporal_coverage":"1984 - Present","temporal_frequency":"8 days","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Dynamic Surface Water Extent","Inundation frequency","Depression storage"],"vars":"Dynamic Surface Water Extent; Inundation frequency; Depression storage","weight":1},{"access":[{"file_format":"SHP; GRD","name":"nhdplus.com","url":"https://nhdplus.com/NHDPlus/NHDPlusV1_data.php"}],"access_details":null,"bbox":{"east":-65,"north":52,"south":17,"west":-179},"citation":"U.S. Environmental Protection Agency (USEPA) and U.S. Geological Survey (USGS), 2005, National Hydrography Dataset Plus - NHDPlus version 1, https://nhdplus.com/NHDPlus/NHDPlusV1_home.php","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Wording from Horizon Systems Corporation website: NHDPlus Version 1 (Archive)\u003cbr\u003e\u003cbr\u003ePlease note that NHDPlusV1 is replaced in its entirety by NHDPlusV2. NHDPlusV1 data will remain available through this archive, however, users are encouraged to migrate existing applications and develop new applications using the far superior NHDPlusV2.\u003cbr\u003e First released in 2006, the NHDPlusV1 consists of ten components: 1) 2006 version of the 1:100K National Hydrography Dataset (NHD), 2) 2004 version of the 30 meter National Elevation Dataset (NED), 3) A set of value added attributes to enhance stream network navigation, analysis and display, 4) An elevation-based catchment for each flowline in the stream network, 5) Catchment characteristics, 6) Headwater node areas, 7) Cumulative drainage area characteristics, 8) Flow direction and flow accumulation grids, 9) Flowline min/max elevations and slopes, and 10) Flow volume \u0026 velocity estimates for each flowline in the stream network.","doi_url":null,"domain":["Hydrology"],"draft":false,"id":"37b8ac9c-5461-4031-847d-196e75693d83","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://nhdplus.com/NHDPlus/NHDPlusV1_documentation.php"}],"name":"NHDPlus v1","permalink":"/catalog/datasets/37b8ac9c-5461-4031-847d-196e75693d83/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS; EPA","spatial_extent":"CONUS; HI; PR; VI","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Flowlines","Catchments"],"vars":"Flowlines; Catchments","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/61df4601d34ed79294021f80"}],"access_details":null,"bbox":{"east":-65.2148,"north":49.838,"south":24.0465,"west":-125.1563},"citation":"Hodson, T.O., Foks, S.S., Dunne, K.A., Miles, K.A., Over, T.M., Penn, C.A., Saxe, S.W., Simeone, C.E., Towler, E., Dickinson, J.E., and Viger, R.J., 2022, Daily streamflow performance benchmark defined by D-score (v0.1) for the National Hydrologic Model application of the Precipitation-Runoff Modeling System (v1 byObs Muskingum) at benchmark streamflow locations: U.S. Geological Survey data release, https://doi.org/10.5066/P9PZLHYZ","creator":[],"creator_project":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"date_created":"5/2/2024","date_updated":"6/3/2026","description":"This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Modeling System (NHM-PRMS) version 1 \"byObs\" calibration with Muskingum routing (Hay and LaFontaine, 2020) computed at streamflow benchmark locations (version 1.0) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow versus observed daily mean streamflow. Using those errors, the D-score performance benchmark computes the mean squared logarithmic error (MSLE), then decomposes the overall MSLE into orthogonal components such as bias, distribution, and sequence (Hodson and others, 2021). For easier interpretation, the MSLE components can be passed through a scoring function as described in Hodson and others (2021).","doi_url":"https://doi.org/10.5066/P9PZLHYZ","domain":["Hydrology"],"draft":false,"id":"37d7835b-1130-4246-808b-584800bb28a7","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/61df4601d34ed79294021f80?f=__disk__b4%2Fdd%2F7c%2Fb4dd7c0aa9deae4f4115269d54267938552b4682\u0026allowOpen=true"}],"name":"Streamflow decomposition suite benchmark results (NHM v1.0): Daily streamflow performance benchmark defined by D-score (v0.1) for the National Hydrologic Model application of the Precipitation-Runoff Modeling System (v1 byObs Muskingum) at benchmark streamflow locations","permalink":"/catalog/datasets/37d7835b-1130-4246-808b-584800bb28a7/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"unknown","temporal_coverage":"1983 - 2016","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["site_no","overall","trend","seasonality","variability","bias","distribution","sequence","winter","spring","summer","fall","low","below_avg","above_avg","high"],"vars":"site_no; overall; trend; seasonality; variability; bias; distribution; sequence; winter; spring; summer; fall; low; below_avg; above_avg; high","weight":1},{"access":[{"file_format":"HDF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-mod44b-006"}],"access_details":"","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"DiMiceli, C., Carroll, M., Sohlberg, R., Kim, D., Kelly, M., Townshend, J., 2015, MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006 [Insert dataset name], NASA EOSDIS Land Processes DAAC. Accessed [YYYY-MM-DD] at https://doi.org/10.5067/MODIS/MOD44B.006","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Deprecated: This dataset is no longer available.\u003cbr\u003eThe MOD44B Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MOD44B Version 6.1 data product.\u003cbr\u003e\u003cbr\u003eThe MOD44B Version 6 Vegetation Continuous Fields (VCF) yearly product is a global representation of surface vegetation cover as gradations of three ground cover components: percent tree cover, percent non-tree cover, and percent non-vegetated (bare). VCF products provide a continuous, quantitative portrayal of land surface cover at 250 meter (m) pixel resolution, with a sub-pixel depiction of percent cover in reference to the three ground cover components. The sub-pixel mixture of ground cover estimates represents a revolutionary approach to the characterization of vegetative land cover that can be used to enhance inputs to environmental modeling and monitoring applications. The MOD44B data product layers include percent tree cover, percent non-tree cover, percent non-vegetated, cloud cover, and quality indicators. The start date of the annual period for this product begins with day of year (DOY) 65 (March 5).","doi_url":"https://doi.org/10.5067/MODIS/MOD44B.006","domain":["Land Cover"],"draft":false,"id":"38eff456-23d9-49ef-b2a0-edc77e44767c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"MOD44B v006: MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250 m SIN Grid","permalink":"/catalog/datasets/38eff456-23d9-49ef-b2a0-edc77e44767c/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"250 meter","temporal_coverage":"2000 - 2021","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Tree canopy"],"vars":"Tree canopy","weight":1},{"access":[{"file_format":"TXT; SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5d4192aee4b01d82ce8da477"}],"access_details":null,"bbox":{"east":-66,"north":48.1,"south":36.65,"west":-80.55},"citation":"Ator, S.W., 2020, SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Northeastern United States, 2012 Base Year: U.S. Geological Survey data release, https://doi.org/10.5066/P9NKNVQO.","creator":[{"creator_email":"swator@usgs.gov","creator_name":"Scott Ator"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The U.S. Geological Survey's (USGS) SPAtially Referenced Regression On Watershed attributes (SPARROW) model was used to aid in the interpretation of monitoring data and simulate streamflow and water-quality conditions in streams across the Northeast Region of the United States. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based water-quality load estimates. This USGS data release includes input and output files associated with 2012 SPARROW simulations of streamflow, total nitrogen, total phosphorus and suspended-sediment load in streams of the Northeast region. Model construction, calibration and results are described in Ator (2019, https://doi.org/10.3133/sir20195118).","doi_url":"https://doi.org/10.5066/P9NKNVQO","domain":["Hydrology","Water Quality"],"draft":false,"id":"3a0eabe4-e4ba-4b7c-b548-dc0af5faec5a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[{"id":"8e649d48-f8fc-4cc6-a147-5645b54693eb","rel_type":"IsSourceOf"}],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088\u0026allowOpen=true"},{"name":"Documentation","url":"https://doi.org/10.3133/sir20195118"}],"name":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Northeastern United States, 2012 Base Year","permalink":"/catalog/datasets/3a0eabe4-e4ba-4b7c-b548-dc0af5faec5a/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Northeastern United States","spatial_resolution":"1:100,000","temporal_coverage":"1999 - 2014","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Streamflow","Total nitrogen loads","Total phosphorus loads","Suspended-sediment loads"],"vars":"Streamflow; Total nitrogen loads; Total phosphorus loads; Suspended-sediment loads","weight":1},{"access":[{"file_format":"NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/661039a6d34e6334665050f4"}],"access_details":null,"bbox":{"east":-63.1184,"north":52.898,"south":20.1149,"west":-131.1649},"citation":"Rafieeinasab, A., Gochis, D., Srivastava, I., Dugger, A., Sampson, K., Omani, N., Mazrooei, A., Zhang, Y., Casali, M., and LaFontaine, J., 2024, Application of the WRF-Hydro Modeling System for the Conterminous United States at the NHDPlus version 2 Spatial Resolution Using the Bias Adjusted Version of the CONUS404 Atmospheric Forcings (CONUS404BA), Water Years 2010-2021: U.S. Geological Survey data release, https://doi.org/10.5066/P1KZGLU2.","creator":[{"creator_email":"jlafonta@usgs.gov","creator_name":"Jacob LaFontaine"}],"creator_project":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"date_created":"9/16/2024","date_updated":"6/8/2026","description":"This data release contains inputs for and outputs from a hydrologic simulation for the conterminous United States (CONUS) using the WRF-Hydro modeling system version 5.2.0 (Gochis and others, 2020) at the NHDPlus version 2 spatial resolution. This simulation was developed to provide water budget estimates for the period 10/1/2009 to 9/30/2021 using the bias adjusted version of the CONUS404 (CONUS404BA) atmospheric forcings dataset (Zhang and others, 2024). The WRF-Hydro model input files are included within this data release and consist of two configuration files, two simulation restart files, nine parameter files, and five types of output files. Each output type has a file for each timestep of the model application simulation. All model files are archived on the U.S. Geological Survey's Black Pearl tape drive system. The data can be accessed through a Globus endpoint here: https://app.globus.org/file-manager/collections/bf01ea19-425e-4434-809f-4e44ac550b1c/overview. The Entity and Attribute element of the metadata file contains the data descriptions for all the variables in each of the five types of output files. Please refer to the Supplemental Information element of this metadata record for further information on this model application.","doi_url":"https://doi.org/10.5066/P1KZGLU2","domain":["Climate","Hydrology"],"draft":false,"id":"3a2ece15-f510-473e-82e5-7b23d4b37fed","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"f930b17a-c6ea-4623-b89d-d7d85ac698aa","rel_type":"IsDerivedFrom"}],"linked_tools":[{"id":"a69ed792-02ac-4709-9f33-b61848d73ae8","rel_type":"IsDerivedFrom"}],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/661039a6d34e6334665050f4?f=__disk__37%2F3a%2Fe9%2F373ae95ba8d40e90d741592b01bf305aaa3b4d81\u0026allowOpen=true"}],"name":"Application of the WRF-Hydro Modeling System for the Conterminous United States at the NHDPlus version 2 Spatial Resolution Using the Bias Adjusted Version of the CONUS404 Atmospheric Forcings (CONUS404BA), Water Years 2010-2021","permalink":"/catalog/datasets/3a2ece15-f510-473e-82e5-7b23d4b37fed/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS; NCAR","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2010 - 2021","temporal_frequency":"hourly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["crs","elevation","feature_id","latitude","longitude","order","reference_time","streamflow","time","qBtmVertRunoff","qBucket","qSfcLatRunoff","q_lateral","velocity","depth","inflow","outflow","ACCET","ACSNOM","ACSNOW","ALBEDO","ALBSND","ALBSNI","COSZ","EDIR","FIRA","FSA","FSNO","HFX","LH","QRAIN","QSNOW","SNEQV","SNOWH","SOIL_M","SOIL_W","TRAD","UGDRNOFF","x","y","sfcheadsubrt","zwattablrt"],"vars":"crs; elevation; feature_id; latitude; longitude; order; reference_time; streamflow; time; qBtmVertRunoff; qBucket; qSfcLatRunoff; q_lateral; velocity; depth; inflow; outflow; ACCET; ACSNOM; ACSNOW; ALBEDO; ALBSND; ALBSNI; COSZ; EDIR; FIRA; FSA; FSNO; HFX; LH; QRAIN; QSNOW; SNEQV; SNOWH; SOIL_M; SOIL_W; TRAD; UGDRNOFF; x; y; sfcheadsubrt; zwattablrt","weight":1},{"access":[{"file_format":"NC; TSV","name":"doi.pangaea.de","url":"https://doi.pangaea.de/10.1594/PANGAEA.918447"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Hannes, M.S., Caceres, D., Eisner, S., Florke, M., Herbert, C., Niemann, C., Peiris, T.A., Popat, E., Portmann, F.T., Reinecke, R., Shadkam, S., Trautmann, T., Doll, P., 2020, The global water resources and use model WaterGAP v2.2d - Standard model output, PANGAEA, https://doi.org/10.1594/PANGAEA.918447","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The global water use and water availability model WaterGAP is in development since 1996 and serves a range of applications and topics as such as Life Cycle Assessments, a better understanding of terrestrial water storage variations (for example, jointly with satellite observations), water (over)use and consequently depletion of water resources, as well as model evaluation and model development. In the paper connected to this dataset, the newest model version, WaterGAP 2.2d is described by providing the water balance equations, insights to input data used and typical model applications. The most important and requested model outputs (total water storage variations, streamflow and water use) are evaluated against observation data. Standard model output is described and the reader is guided to the location where those data can be downloaded. Limitations of specific output data and an overview of model applications as well as an outlook of future model development lines are presented as well.","doi_url":"https://doi.org/10.1594/PANGAEA.918447","domain":["Hydrology"],"draft":false,"id":"3aa27e66-778d-476d-946f-cf3a532d2cea","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://gmd.copernicus.org/articles/14/1037/2021/"}],"name":"The global water resources and use model WaterGAP v2.2d - Standard model output","permalink":"/catalog/datasets/3aa27e66-778d-476d-946f-cf3a532d2cea/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"Global","spatial_resolution":"0.5 degrees","temporal_coverage":"1901 - 2016","temporal_frequency":"monthly; annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Total water storage","Canopy water storage","Snow water storage","Soil water storage","Groundwater storage","Local lake storage","Global lake storage","Local wetland storage","Global wetland storage","Reservoir storage","River storage","Monthly precipitation","Fast surface and fast subsurface runoff","Diffuse groundwater recharge","Groundwater recharge from surface water bodies","Total groundwater recharge","Runoff from land","Groundwater discharge","Actual evapotranspiration","Potential evapotranspiration","Net cell runoff","Naturalized net cell runoff","Streamflow","Naturalized streamflow","Actual net abstraction from surface water","Actual net abstraction from groundwater","Actual consumptive water use"],"vars":"Total water storage; Canopy water storage; Snow water storage; Soil water storage; Groundwater storage; Local lake storage; Global lake storage; Local wetland storage; Global wetland storage; Reservoir storage; River storage; Monthly precipitation; Fast surface and fast subsurface runoff; Diffuse groundwater recharge; Groundwater recharge from surface water bodies; Total groundwater recharge; Runoff from land; Groundwater discharge; Actual evapotranspiration; Potential evapotranspiration; Net cell runoff; Naturalized net cell runoff; Streamflow; Naturalized streamflow; Actual net abstraction from surface water; Actual net abstraction from groundwater; Actual consumptive water use","weight":1},{"access":[{"file_format":"NC","name":"ceres.larc.nasa.gov","url":"https://ceres.larc.nasa.gov/data/"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Kato, S., Rose, F.G., Rutan, D. A., Thorsen, T.E., Loeb, N.G., Doelling, D.R., Huang, X., Smith, W.L., Su, W., and Ham, S.H., 2018, Surface irradiances of Edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF): data product, J. Climate, 31, 4501-4527,  https://doi.org/10.1175/JCLI-D-17-0523.1","creator":[{"creator_email":"seiji.kato@nasa.gov","creator_name":"Seiji Kato"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The algorithm to produce the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4.0 (Ed4) Energy Balanced and Filled (EBAF)-surface data product is explained. The algorithm forces computed top-of-atmosphere (TOA) irradiances to match with Ed4 EBAF-TOA irradiances by adjusting surface, cloud, and atmospheric properties. Surface irradiances are subsequently adjusted using radiative kernels. The adjustment process is composed of two parts: bias correction and Lagrange multiplier. The bias in temperature and specific humidity between 200 and 500 hPa used for the irradiance computation is corrected based on observations by Atmospheric Infrared Sounder (AIRS). Similarly, the bias in the cloud fraction is corrected based on observations by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat. Remaining errors in surface, cloud, and atmospheric properties are corrected in the Lagrange multiplier process. Ed4 global annual mean (January 2005 through December 2014) surface net shortwave (SW) and longwave (LW) irradiances increase by 1.3 W m^−2 and decrease by 0.2 W m^−2, respectively, compared to EBAF Edition 2.8 (Ed2.8) counterparts (the previous version), resulting in an increase in net SW + LW surface irradiance of 1.1 W m^−2. The uncertainty in surface irradiances over ocean, land, and polar regions at various spatial scales are estimated. The uncertainties in all-sky global annual mean upward and downward shortwave irradiance are 3 and 4 W m^−2, respectively, and the uncertainties in upward and downward longwave irradiance are 3 and 6 W m^−2, respectively. With an assumption of all errors being independent, the uncertainty in the global annual mean surface LW + SW net irradiance is 8 W m^−2.","doi_url":"https://doi.org/10.1175/JCLI-D-17-0523.1","domain":["Climate"],"draft":false,"id":"3b6a0f9e-00fc-4d5a-a335-b34916429d30","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://journals.ametsoc.org/view/journals/clim/31/11/jcli-d-17-0523.1.xml"}],"name":"CERES EBAF Level 3b","permalink":"/catalog/datasets/3b6a0f9e-00fc-4d5a-a335-b34916429d30/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"GLOBAL","spatial_resolution":"1 degree","temporal_coverage":"2000 - 2024","temporal_frequency":"monthly","update_detail":"append","update_frequency":"monthly","update_type":"Dynamic","variables":["Net Total Flux (Radiation)"],"vars":"Net Total Flux (Radiation)","weight":1},{"access":[{"file_format":"GDB","name":"usgs.gov","url":"https://www.usgs.gov/national-hydrography/access-national-hydrography-products"}],"access_details":"The usgs.gov access link goes to Access National Hydrography Products where the NHDPlus HR WBD snapshot (a component of NHDPlus HR) can be accessed under the heading NHDPlus High Resolution (NHDPlus HR).","bbox":{"east":-65,"north":72,"south":23,"west":-178},"citation":null,"creator":[],"creator_project":[],"date_created":"2/28/2025","date_updated":"6/3/2026","description":"NHDPlus HR WBD Snapshot\u003cbr\u003eStarting in 2016 the USGS began developing a NHDPlus High Resolution (NHDPlus HR) dataset that built upon the data model, processes, and tools used in the development of the NHDPlus V2 data. The high-resolution National Hydrography Dataset (NHD) and WBD were used in conjunction with the 10 meter-resolution 3D Elevation Product (3DEP) DEM data in the development of the NHDPlus HR.\u003cbr\u003eThe version of the WBD used with the NHDPlus HR initial data release includes WBD snapshots from 2018-2021. Updated NHDPlus HR data was released for regions 01, 02, 06, 14, 15 and 16 in 2022 and 2023. These regions were reprocessed using updated software, NHD, WBD and 3DEP data.\u003cbr\u003eThe WBD contained within the NHDPlus HR includes many updates that were not available within the NHDPlus V2 data. Updates include the addition of full 8-digit hydrologic units for international basins that span the U.S.-Canada border and the U.S.-Mexico border. In addition, areas that are fully within Canada and Mexico that contribute flow to the United States were added to the WBD as HU8 boundaries.\u003cbr\u003eWhat is the Watershed Boundary Dataset (WBD)\u003cbr\u003eThe Watershed Boundary Dataset (WBD) is a seamless, national, hydrologic unit dataset that provides a standardized base for water-resources organizations to locate, store, retrieve, and exchange hydrologic data; to index and inventory hydrologic data and information; to catalog water-data acquisition activities; and to use in a variety of other applications. Hydrologic unit boundaries in the WBD are determined based on topographic, hydrologic, and other relevant landscape characteristics without regard for administrative, political, or jurisdictional boundaries.\u003cbr\u003eThe hydrologic units (HU) in the WBD are arranged in a nested, hierarchical system with each HU in the system identified using a unique hydrologic unit code (HUC). Each HU within a nested level is assigned a two-digit suffix that’s appended to the HUC of the HU in the next coarsest nesting level. Because there are eight nesting levels within WBD, the set of HUCs consists of ranges from two to sixteen digits based on the eight levels of classification in the WBD. The dataset is complete for the United States to the 12-digit hydrologic unit. The 14- and 16-digit hydrologic units have only been published for a subset of the nation. https://www.usgs.gov/media/images/watershed-boundary-dataset-structure-visualization","doi_url":null,"domain":["Hydrology"],"draft":false,"id":"4114c537-5dfa-4aa2-a397-e4c93a4d13ec","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://water.usgs.gov/usgs/themes-internal/hydrologic-units/"}],"name":"WBD NHDPlus HR Snapshot","permalink":"/catalog/datasets/4114c537-5dfa-4aa2-a397-e4c93a4d13ec/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; AK; parts of Canada and Mexico","spatial_resolution":"1:24,000; 1:63,360","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["OBJECTID","tnmid","metasourceid","sourcedatadesc","sourceoriginator","sourcefeatureid","loaddate","referencegnis_ids","areaacres","areasqkm","states","huc12","name","hutype","humod","tohuc","noncontributingareaacres","noncontributingareasqkm","nhdplusid","vpuid","Shape_Length","Shape_Area"],"vars":"OBJECTID; tnmid; metasourceid; sourcedatadesc; sourceoriginator; sourcefeatureid; loaddate; referencegnis_ids; areaacres; areasqkm; states; huc12; name; hutype; humod; tohuc; noncontributingareaacres; noncontributingareasqkm; nhdplusid; vpuid; Shape_Length; Shape_Area","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/63dbfac3d34e9fa19a98a10f"}],"access_details":null,"bbox":{"east":-66.0938,"north":48.9225,"south":24.687,"west":-124.9805},"citation":"Gorman Sanisaca, L.E., Galanter, A.E., Skinner, K.D., Harris, M.A., Diehl, T.H., Halper, A.S., Mohs, T.G., Roland, V.L., Stewart, J.S., and Niswonger, R., 2023, Thermoelectric-power condenser duty estimates by month and cooling type for use to calculate water use by power plant for the 2008-2020 reanalysis period for the conterminous United States: U.S. Geological Survey, https://doi.org/10.5066/P9XG876W","creator":[],"creator_project":[{"id":"DJ50UY1","name":"Water Use Model Development"}],"date_created":"5/10/2024","date_updated":"6/3/2026","description":"The U.S. Geological Survey (USGS) developed models to estimate the amount of water that is withdrawn and consumed by thermoelectric power plants (Diehl and others, 2013; Diehl and Harris, 2014; Harris and Diehl, 2019). The thermoelectric water use models are based on linked heat-and-water budgets that are constrained by power plant generation and cooling system technologies, the amount of fuels consumed and electricity generated, and environmental variables. The heat-budget side of the models calculates the amount of waste heat (fuel heat that is not converted to electricity) that is removed from the steam used to drive the turbines that generate electricity and transferred to the cooling system in a thermoelectric power plant’s condenser, which is defined as the condenser duty (Diehl and others, 2013). Condenser duty is an intermediate calculation and an input to the water-budget side of the thermoelectric water use models that estimates plant-specific water withdrawal and consumption. The models provide consistent methods for water-use estimation across U.S. thermoelectric plants and estimates independent of plant operator-reported data. This dataset presents a historical reanalysis of monthly condenser duty estimates from 2008 to 2020, and associated information for 1360 water-using, utility-scale thermoelectric power plants in the conterminous United States operational within the time period.  These monthly condenser duty estimates were input to the water-budget side of the thermoelectric water-use models for the 2008-2020 historical reanalysis of water withdrawals and consumption by HUC12, month, and year for the conterminous United States (Galanter and others, 2023).","doi_url":"https://doi.org/10.5066/P9XG876W","domain":["Water Use"],"draft":false,"id":"44822076-1ec7-4fd5-8f0a-dbaeeb646ed1","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/63dbfac3d34e9fa19a98a10f?f=__disk__d3%2F43%2F91%2Fd34391adabf8bec221c0b517cdb0d4354494b5c1\u0026allowOpen=true"}],"name":"Thermoelectric-power condenser duty estimates by month and cooling type for use to calculate water use by power plant for the 2008-2020 reanalysis period for the conterminous United States","permalink":"/catalog/datasets/44822076-1ec7-4fd5-8f0a-dbaeeb646ed1/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"unknown","temporal_coverage":"2008 - 2020","temporal_frequency":"monthly; annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["InputFile","InputSheetName","InputColumnName","scriptImportFunc","scriptTableName","scriptColnames","YEAR","downloadDate","Plant.Code","Boiler.ID","Generator.ID","bogen","bogencoo","plant_bo","plant_bo_bf.923","plant_gen","plant_gen.923","manualBogenEdit","manualBogencooEdit","Reported.Prime.Mover_page1","LAKE..OF..OC..RC.","RIVER..OF.","POND..OC..RC.","TOWER..RF..RI..RN.","DC","OS","sheet3_key","sheet4_key","bogencoo.key","combogencoo_cooly_type","combogencoo_cooly_type_nukes","YEAR","Plant.Code","cooling","percentAllocation","month","condenserDuty","YEAR","Plant.Code","Plant.Name","County","State","Name.of.Water.Source","cooling","percentAllocation","dom_fuel","Plant.level_dom_fuel","general_mover","Net.Generation.Year.To.Date","YEAR"],"vars":"InputFile; InputSheetName; InputColumnName; scriptImportFunc; scriptTableName; scriptColnames; YEAR; downloadDate; Plant.Code; Boiler.ID; Generator.ID; bogen; bogencoo; plant_bo; plant_bo_bf.923; plant_gen; plant_gen.923; manualBogenEdit; manualBogencooEdit; Reported.Prime.Mover_page1; LAKE..OF..OC..RC.; RIVER..OF.; POND..OC..RC.; TOWER..RF..RI..RN.; DC; OS; sheet3_key; sheet4_key; bogencoo.key; combogencoo_cooly_type; combogencoo_cooly_type_nukes; YEAR; Plant.Code; cooling; percentAllocation; month; condenserDuty; YEAR; Plant.Code; Plant.Name; County; State; Name.of.Water.Source; cooling; percentAllocation; dom_fuel; Plant.level_dom_fuel; general_mover; Net.Generation.Year.To.Date; YEAR","weight":1},{"access":[{"file_format":"TIF","name":"earlywarning.usgs.gov","url":"https://earlywarning.usgs.gov/ssebop/modis/daily/626"},{"file_format":"ZARR","name":"WMA STAC","url":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/ssebopeta"}],"access_details":null,"bbox":{"east":-64,"north":50,"south":23,"west":-127},"citation":"Senay, G. B. and Kagone, S., 2019, Daily SSEBop Evapotranspiration: U. S. Geological Survey data release, https://doi.org/10.5066/P9L2YMV","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Daily SSEBop evapotranspiration at the Moderate Resolution Imaging Spectroradiometer (MODIS) scale was created for the CONUS. These data are published on the USGS earlywarning site (https://earlywarning.usgs.gov/ssebop/modis/daily). The first phase included the creation on historical actual daily ET data from 2000 - 2018. The second phase will create the ET product operationally on a daily time scale. The corresponding data files for each day, geotiff and meta data file, are compressed as a zip file for download. The values for the daily ET data are scaled by a factor of 1000. NoData pixels are indicated as NoData (9999). Daily ETa data are produced at 1 km resolution and are available at: https://earlywarning.usgs.gov/ssebop/modis/daily ","doi_url":"https://doi.org/10.5066/P9L2YMV","domain":["Hydrology"],"draft":false,"id":"455c6eba-ab33-4099-80ec-ef3f03024caf","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[{"id":"d71a50f4-4014-4c6d-93c0-b3c728c477b4","rel_type":"IsSourceOf"},{"id":"a6b01211-f71f-4e7a-a02a-4fe7ede14f2b","rel_type":"IsSourceOf"},{"id":"78a93a34-3c9e-4859-b967-b68998fd7f9e","rel_type":"IsSourceOf"}],"linked_usecases":[{"id":"148a7db7-1175-47aa-8437-5ae39f62b1ce","rel_type":"IsSourceOf"}],"links":[{"name":"Documentation","url":"https://earlywarning.usgs.gov/docs/SSEBopETreadme.pdf"}],"name":"SSEBop (MODIS)","permalink":"/catalog/datasets/455c6eba-ab33-4099-80ec-ef3f03024caf/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1 kilometer","temporal_coverage":"2000 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Evapotranspiration"],"vars":"Evapotranspiration","weight":1},{"access":[{"name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/68c831e7d4be025032016570"}],"bbox":{"east":-67.6758,"north":48.7489,"south":24.3671,"west":-125.6836},"citation":"Carlisle, D.M., 2026, GAGES 3: Environmental Setting of USGS Stream Gage Locations in the Conterminous United States:  U.S. Geological Survey data release, https://doi.org/10.5066/P138XCM3.","creator":[{"creator_email":"dcarlisle@usgs.gov","creator_name":"Daren M Carlisle"}],"creator_project":[{"id":"DJ50VN0","name":"Ecoflows: CONUS Ecological Water Availability"}],"date_created":"2/24/2026","date_updated":"6/3/2026","description":"The US Geological Survey has collected daily streamflow data at more than 20,000 locations within the conterminous United States (CONUS). Although these data are publicly available, there is little to no information about the environmental setting under which the data were collected. Specifically, users of the data have no readily available means to determine whether the observed streamflow was primarily a function of natural hydrologic process or was in some way influenced by water management activities such as dams and diversions, or land cover such as urbanization or intensive agriculture. Previous publications on this topic were limited to gages with lengthy flow records or that were active at the time of publication. This product provides environmental context for all stream and river monitoring locations within CONUS through 2024. This information identifies which sites and which periods of record are likely to approximate natural hydrologic processes, and which sites are influenced by various land use or water-management structures.","doi_url":"https://doi.org/10.5066/P138XCM3","domain":["Hydrology","Stream Characteristics"],"draft":false,"id":"47280eea-105f-4aec-b065-94ddafe517fc","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/68c831e7d4be025032016570?f=__disk__0b%2F0a%2F4b%2F0b0a4b48ff3724f4976ea85c77b2064257603532\u0026allowOpen=true"}],"name":"GAGES 3: Environmental Setting of USGS Stream Gage Locations in the Conterminous U.S.","permalink":"/catalog/datasets/47280eea-105f-4aec-b065-94ddafe517fc/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"project_using":[{"id":"DJ50VN0","name":"Ecoflows: CONUS Ecological Water Availability"},{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1857 - 2025","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["ID","NWIS_Area","NHD_Area","Adj_NWIS_Area","Begin_Date","End_Date","End_Year","Station_Name","Lat_Dec","Long_Dec","Source","COMID_Man_QA","Gages2_Status","Gages3_Status","Gages3_Screen_Comments","Other_Info_Sources","HDI.Pct.Ag","HDI.Art.Path","HDI.Canals","HDI.NPDES","HDI.FW.With","HDI.MDam.Dens","HDI.Delta.Stg","HDI.Pct.Urban","NA_Eco3","Size.Class","Ag.Rank","Art.Path.Rank","Canals.Rank","NPDES.Rank","FW.With.Rank","MDam.Den.Rank","Delta.Stg.Rank","Urban.Rank","Gages2_Screen_Comments"],"vars":"ID; NWIS_Area; NHD_Area; Adj_NWIS_Area; Begin_Date; End_Date; End_Year; Station_Name; Lat_Dec; Long_Dec; Source; COMID_Man_QA; Gages2_Status; Gages3_Status; Gages3_Screen_Comments; Other_Info_Sources; HDI.Pct.Ag; HDI.Art.Path; HDI.Canals; HDI.NPDES; HDI.FW.With; HDI.MDam.Dens; HDI.Delta.Stg; HDI.Pct.Urban; NA_Eco3; Size.Class; Ag.Rank; Art.Path.Rank; Canals.Rank; NPDES.Rank; FW.With.Rank; MDam.Den.Rank; Delta.Stg.Rank; Urban.Rank; Gages2_Screen_Comments","weight":1},{"access":[{"file_format":"TXT","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5b9059b4e4b0702d0e80788f"}],"access_details":null,"bbox":{"east":-66.885444,"north":49.384358,"south":24.396308,"west":-124.848974},"citation":"Stewart, J.S., Schwarz, G.E., Brakebill, J.W., and Preston, S.D., 2019, Catchment-Level Predictions of Nitrogen and Phosphorus Fertilizer Use from Commercial Fertilizer Sales Data for the Conterminous U.S., 2012: U.S. Geological Survey data release, https://doi.org/10.5066/F7CZ36F4","creator":[{"creator_email":"jwbrakeb@usgs.gov","creator_name":"John Brakebill"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset contains catchment-level estimates of nitrogen and phosphorus fertilizer use, for agricultural lands, for the conterminous U.S., for 2012.  An approach was developed to relate farm commercial fertilizer sales data from the Association of American Plant Food Control Officials (AAPFCO) to a set of explanatory variables using spatially-referenced modeling methods. Separate models for nitrogen and phosphorus are developed to estimate elemental fertilizer use on agricultural lands for the conterminous U.S. at the National Hydrography Dataset Plus version 2 (NHDPlusV2) catchment scale. The approach builds on earlier efforts that use Association of American Plant Food Control Officials (AAPFCO) data on fertilizer sales to provide county-level estimates of nitrogen and phosphorus fertilizer use. The spatially-referenced method improves on these efforts by allowing for varying nitrogen to phosphorus (NP) ratios at the catchment scale and expanding the set of variables used to allocate county-level sales data to the catchment scale. The models include catchment-level factors that are either primary determinants of fertilizer use, such as the acreage of different crop types, or measures reflecting the intensity of use, such as climate. Explanatory variables available only at the county scale, such as United States Department of Agriculture (USDA) Census of Agriculture (COA) estimates of fertilizer expenditures, are included to improve the model predictions of elemental use. The nitrogen and phosphorus models explain over 90 percent of the variation in elemental use, and the statistical approach allows for the estimation of uncertainty of predicted use in each catchment. The spatial patterns of model estimates reflect known agricultural cropping practices across the U.S. that transcends political boundaries, despite the county/state-orientation of the fertilizer sales information. The results are expected to be useful for a variety of water-quality assessments that are intended to estimate nitrogen and phosphorus loads to streams. A companion data release provides catchment/county level model input for the nitrogen and phosphorus fertilizer use models and is listed in this metadata under Cross Reference. The software and methods used for producing these estimates are described in the USGS Scientific Investigations Report 2018-5145 (please see documentation link).","doi_url":"https://doi.org/10.5066/F7CZ36F4","domain":["Water Quality"],"draft":false,"id":"472d0a1a-34d7-4505-9231-423380fa781b","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5b9059b4e4b0702d0e80788f?f=__disk__b9%2F9f%2Fef%2Fb99fef410ac92a1e155629e83b97b5ac3653574a\u0026transform=1\u0026allowOpen=true"},{"name":"Documentation","url":"https://pubs.usgs.gov/publication/sir20185145"}],"name":"Catchment-Level Prediction of Nitrogen and Phosphorous Fertilizer Use from Commercial Fertilizer Sales Data for the Conterminous U.S. 2012","permalink":"/catalog/datasets/472d0a1a-34d7-4505-9231-423380fa781b/","project_use_history":[],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2012","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Nitrogen fertilizer use","phosphorus fertilizer use"],"vars":"Nitrogen fertilizer use; phosphorus fertilizer use","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5d16509ee4b0941bde5d8ffe"}],"access_details":null,"bbox":{"east":-65.7422,"north":49.3824,"south":24.2069,"west":-126.0352},"citation":"Schwarz, G.E., 2019, E2NHDPlusV2_us: Database of Ancillary Hydrologic Attributes and Modified Routing for NHDPlus Version 2.1 Flowlines: U.S. Geological Survey data release, https://doi.org/10.5066/P986KZEM","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"These data represent a topologically reconditioned version of the [medium-resolution NHDPlus](/datasets/8a60b6b4-d785-4265-af99-cd1870ea7928) routing network, including enhanced attributes to improve flow routing and the physical description of flowlines. The modifications pertain only to NHDPlus data table values; no physical changes were made to the spatial locations of flowlines or catchments. The approach assumes the pre-existence of the NHDPlus network and its attributes, although many of the attributes can be generated without reference to existing values. The enhanced dataset, named E2NHDPlusV2_us, represents a national framework useful for the hydrologic referencing of spatial data, and is an extension of the work in \u003ca href='https://www.sciencebase.gov/catalog/item/5669a79ee4b08895842a1d47' target='_blank'\u003eSchwarz and Wieczorek, 2018\u003c/a\u003e. The data are useful to hydrologic modeling, such as the SPARROW water-quality model, and other analyses relating spatial features to hydrologic data.","doi_url":"https://doi.org/10.5066/P986KZEM","domain":["Hydrology"],"draft":false,"id":"474f4425-266f-4eb1-8484-84940975dd70","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"ae9546b9-1eda-407a-9129-8a62b43ac056","rel_type":"IsSourceOf"},{"id":"553e2bbb-00ea-4052-b472-5561943d5dc6","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[{"id":"6389d98c-7de8-4dc7-938c-f98da1063455","rel_type":"IsSourceOf"}],"links":[],"name":"E2NHDPlusV2_us: Database of Ancillary Hydrologic Attributes and Modified Routing for NHDPlus Version 2.1 Flowlines","permalink":"/catalog/datasets/474f4425-266f-4eb1-8484-84940975dd70/","project_use_history":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Modified routing for NHDPlus Version 2.1 Flowlines","Hydrologic attributes"],"vars":"Modified routing for NHDPlus Version 2.1 Flowlines; Hydrologic attributes","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6283a083d34e3bef0c9a43a8"}],"access_details":null,"bbox":{"east":-66.0059,"north":49.3535,"south":24.6466,"west":-125.9473},"citation":"Goodling, P.J., LaFontaine, J.H., and Blodgett, D.L., 2022, National Hydrologic Model v1.0 water budget components aggregated to 10 and 12-digit Hydrologic Unit Code boundaries: U.S. Geological Survey data release, https://doi.org/10.5066/P9TYOJKN","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data release contains the output of the National Hydrologic Hydrologic Model (NHM) version 1.0 aggregated to twelve-digit and ten-digit Hydrologic Unit Code (HUC) boundaries contained in the NHDPlus v2.1 dataset. The data are intended to provide \"local\" water budgets for each HUC boundary as total aggregated streamflow across HUC boundaries is not included. The HUC boundaries are periodically updated; this data release uses HUC boundaries downloaded on 10-26-2020. The NHM outputs aggregated in this release are calibrated using a step-wise calibration procedure to determine optimal parameter set and utilize the Muskingum routing (referred to as byHRU Musk-Obs). See Hay and LaFontaine (2020) for additional information regarding calibration. A spatial weighting technique is used to aggregate the smaller hydrologic response units to the larger HUC boundaries. In this study, spatial weighting functions were adapted from Blodgett (2020) and Blodgett (2019). Because factors such as topography, land use, and flow routing are not included in aggregation, these aggregated values should be viewed as estimates of the spatial mean and used with caution. For instance, calculating flow volumes at the HUC pour points from the spatial mean runoff values would be an approximation due to the lack of routing information.\u003cbr\u003e\u003cbr\u003eThis data release contains\u003cbr\u003e1) The centroid coordinates for HUC10 and HUC12 boundaries in files named hucXXcoords.csv, where XX indicates the two-digit identifier.\u003cbr\u003e2) The area weights for HUC10 and HUC12 boundaries in files named hucXXarea_weights.csv, where XX indicates the two-digit identifier.\u003cbr\u003e3) The aggregated monthly and water year budget components for HUC10 and HUC12 boundaries in files named YY_hucXX_budgets.csv, where YY indicates monthly or water year and XX indicates the two-digit identifier\u003cbr\u003e4) a zip folder containing processing scripts called nhm_hru_to_huc12.zip.\u003cbr\u003e\u003cbr\u003eThis data release compliments the following related data releases:\u003cbr\u003eBlodgett, D.L., 2020, Twelve-digit hydrologic unit actual evapotranspiration and snowpack water equivalent storage from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System 1980-2016: U.S. Geological Survey data release, https://doi.org/10.5066/P9IH7CB8\u003cbr\u003eBlodgett, D.L., 2019, Twelve digit hydrologic unit soil moisture and recharge from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System: U.S. Geological Survey data release, https://doi.org/10.5066/P9ZZAWK4\u003cbr\u003e\u003cbr\u003eThis data release uses the following sources of information:\u003cbr\u003e1) NHM 1.0 Geospatial fabric, accessed here: http://dx.doi.org/doi:10.5066/F7542KMD GIS Features of the Geospatial Fabric for National Hydrologic Modeling.\u003cbr\u003e2) NHM 1.0 daily simulation outputs, accessed from the \"byHRU_musk_obs.tar\" file in this data release: Hay, L.E., and LaFontaine, J.H., 2020, Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS),1980-2016, Daymet Version 3 calibration: U.S. Geological Survey data release, https://doi.org/10.5066/P9PGZE0S.\u003cbr\u003e3) The HUC boundaries, downloaded on 10-26-2020, which are accessible here: https://www.sciencebase.gov/catalog/file/get/60cb5edfd34e86b938a373f4?name=WBD_National_GDB.zip.","doi_url":"https://doi.org/10.5066/P9TYOJKN","domain":["Hydrology"],"draft":false,"id":"48b783e3-f49e-41ef-b80a-4a94f7fd801f","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6283a083d34e3bef0c9a43a8?f=__disk__f4%2F0a%2F45%2Ff40a45499d9c2fb06cbdbfe386f8e38789bcf052\u0026transform=1\u0026allowOpen=true"},{"name":"Documentation","url":"https://pubs.usgs.gov/publication/tm6B9"}],"name":"National Hydrologic Model v1.0 water budget components aggregated to 10 and 12-digit Hydrologic Unit Code boundaries","permalink":"/catalog/datasets/48b783e3-f49e-41ef-b80a-4a94f7fd801f/","project_use_history":[],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1980 - 2016","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Spatial weights","Water budget","Gross precipitation","Actual Evapotranspiration","Streamflow","Groundwater storage","Surface runoff"],"vars":"Spatial weights; Water budget; Gross precipitation; Actual Evapotranspiration; Streamflow; Groundwater storage; Surface runoff","weight":1},{"access":[{"file_format":"GPKG; TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5a95dd5de4b06990606a805e"}],"access_details":null,"bbox":{"east":-59.59,"north":53.608,"south":19.45,"west":-134.108},"citation":"Clark, B.R., Barlow, P.M., Peterson, S.M., Hughes, J.D., Reeves, H.W., and Viger, R.J., 2018, National-scale grid to support regional groundwater availability studies and a national hydrogeologic database: U.S. Geological Survey data release, https://doi.org/10.5066/F7P84B24.","creator":[],"creator_project":[],"date_created":"5/5/2024","date_updated":"6/3/2026","description":"The National Hydrogeologic Grid (NHG) dataset includes a raster and vector representation of 1-km cells defining a uniform grid that encompasses the continental United States. The value of each cell of the raster dataset corresponds to the 1-km cell number defined as 'cellnum' in the attributes of the vector data. The NHG consists of 4,000 rows and 4,980 columns, numbered from the top left corner of the grid, to correspond to the traditional row and column numbering system of the MODFLOW groundwater-flow simulation code (Hughes and others, 2017; Langevin and others, 2017). The Albers projection was chosen for the NHG because of the capability to best preserve area, which is crucial in the computation of volume for water-resource investigations (Kuniansky, 2016). The upper left coordinate of the NHG in the Albers projection, in units of meters is -2553045.0, 3907285.0 with a rotation of zero degrees.","doi_url":"https://doi.org/10.5066/F7P84B24","domain":["Hydrogeology","Water Use"],"draft":false,"id":"490c54a5-f4fb-40ef-b839-0c651653d9e4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5a95dd5de4b06990606a805e?f=__disk__36%2F55%2F51%2F365551a66e787836e46b294f2e1ee6018022ecf0\u0026allowOpen=true"}],"name":"National-scale grid to support regional groundwater availability studies and a national hydrogeologic database","permalink":"/catalog/datasets/490c54a5-f4fb-40ef-b839-0c651653d9e4/","project_use_history":[{"id":"DJ50U93","name":"NEHF: National Extent Hydrogeologic Framework"}],"project_using":[{"id":"DJ50U93","name":"NEHF: National Extent Hydrogeologic Framework"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1 kilometer","temporal_coverage":"2017","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["irow","icol","cellnum","natlRow","natlCol","natlCellNum"],"vars":"irow; icol; cellnum; natlRow; natlCol; natlCellNum","weight":1},{"access":[{"file_format":"GDB; CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5d38aac0e4b01d82ce8b940a"}],"access_details":null,"bbox":{"east":-65.371,"north":71.9996,"south":17.9614,"west":-169.1069},"citation":"Johnson, M.R., Anderson, E.D., Ball, L.B., Drenth, B.J., Grauch, V.J.S., McCafferty, A.E., Scheirer, D.S., Schweitzer, P.N., Shah, A.K., and Smith, B.D., 2019, Airborne Geophysical Survey Inventory of the Conterminous United States, Alaska, Hawaii, and Puerto Rico (ver. 5.0, April 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P9K8YTW1.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data release is a compilation of the locations of airborne geophysical surveys in the United States. The inventory documents public airborne geophysical surveys primarily flown by or contracted by the USGS from 1943 to present. In addition, surveys from the State of Alaska, Department of Natural Resources, Division of Geological and Geophysical Surveys (DGGS): Airborne GeophysWeb, the Bureau of Land Management, the Department of Energy and other state agencies have also been included. The surveys have contributed to studies under USGS programs including Water, Geologic Mapping, Minerals, Energy, Environmental Health, Ecosystems, Hazards, and Climate. This dataset contains locations for known and inventoried surveys, shows the footprints of the survey area, and summarizes data type (aeromagnetic (M), electromagnetic (EM), radiometric (R), gravity (G), and very low frequency EM (V)) and survey details. This dataset continues inventory and documentation efforts including digitized and digital geophysical surveys of state and national compilations (Connard and others, 1999; USGS, 2002; Chandler, 2007; USGS, 1999; Hill and others, 2009; USGS, 2018; DGGS, 2019; Johnson and others, 2019) in addition to many individual published surveys (Open-File Reports (OFR), Geophysical Investigations (GP), and data releases to ScienceBase; a trusted digital repository (USGS, 2019). Access to the survey data itself is also provided where available as the inventory catalogs completed and contracted surveys including those that have not been published to date.\u003cbr\u003e?In support of the Earth Mapping Resources Initiative (Earth MRI) (Day, 2019) suitability rankings of airborne geophysical surveys for supporting geologic studies were evaluated and determined for aeromagnetic and aeroradiometric data by Eric Anderson, Ben Drenth, V. J. S. Grauch, Anne McCafferty, Anji Shah, and Dan Scheirer of the USGS. The aeromagnetic suitability rankings documented by Drenth and Grauch (2019) were applied to the geophysical survey inventory based on data type, survey specifications, and data issues with 1 being the best and 5 being the least suitable. The criteria used to rank the surveys are explained in Table 1 (Drenth and Grauch, 2019) and described in detail in the process step of this metadata. In addition aeroradiometric rankings were also derived by Anjana K. Shah and have been incorporated.","doi_url":"https://doi.org/10.5066/P9K8YTW1","domain":["Geophysical"],"draft":false,"id":"4954f5f2-b7da-4755-a306-46dddceeb409","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5d38aac0e4b01d82ce8b940a?f=__disk__a7%2F89%2F08%2Fa7890880b6c43da8c82e10d3d669f835f7f21de0\u0026allowOpen=true"}],"name":"Airborne geophysical survey inventory","permalink":"/catalog/datasets/4954f5f2-b7da-4755-a306-46dddceeb409/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; AK; HI; PR","spatial_resolution":"varies","temporal_coverage":"1943 - 2022","temporal_frequency":"NA","update_detail":"append","update_frequency":"irregular","update_type":"Dynamic","variables":["Content of the survey data (Electromagnetic, Gravity, Magnetic, Radiometric, Very low frequency EM data)","Form in which the data were collected","State","Descriptive name of the survey","Name of the organization carrying out the survey","Survey date or range of dates","Approximate distance between survey lines in km","General direction of the survey flight lines","Altitude type","Nominal altitude of the sensor during the survey","Total distance traversed during the survey","Publication identifier","Publication link","Type of data","Beginning date of the survey","Ending date of the survey","Year survey was conducted","Data quality ranking","Agency responsible for data acquisition","Length of feature","Area of feature","Preview image","URL to metadata for the survey","Access to data for download","Width of preview image","Height of preview image","Number of point records (data points) in the dataset","Size of the data package","Format of the package file containing the data"],"vars":"Content of the survey data (Electromagnetic, Gravity, Magnetic, Radiometric, Very low frequency EM data); Form in which the data were collected; State; Descriptive name of the survey; Name of the organization carrying out the survey; Survey date or range of dates; Approximate distance between survey lines in km; General direction of the survey flight lines; Altitude type; Nominal altitude of the sensor during the survey; Total distance traversed during the survey; Publication identifier; Publication link; Type of data; Beginning date of the survey; Ending date of the survey; Year survey was conducted; Data quality ranking; Agency responsible for data acquisition; Length of feature; Area of feature; Preview image; URL to metadata for the survey; Access to data for download; Width of preview image; Height of preview image; Number of point records (data points) in the dataset; Size of the data package; Format of the package file containing the data","weight":1},{"access":[{"file_format":"SQLITEDB; GPKG","name":"nrcs.usda.gov","url":"https://www.nrcs.usda.gov/resources/data-and-reports/ssurgo-portal?utm_medium=email\u0026utm_source=govdelivery"},{"file_format":"SHP","name":"websoilsurvey.nrcs.usda.gov","url":"https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx"}],"access_details":null,"bbox":{"east":-55,"north":72.5,"south":12,"west":-180},"citation":"Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture, Web Soil Survey, accessed [YYYY-MM-DD] at https://websoilsurvey.nrcs.usda.gov/","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. The maps outline areas called map units. The map units describe soils and other components that have unique properties, interpretations, and productivity. The information was collected at scales ranging from 1:12,000 to 1:63,360. More details were gathered at a scale of 1:12,000 than at a scale of 1:63,360. The mapping is intended for natural resource planning and management by landowners, townships, and counties. Some knowledge of soils data and map scale is necessary to avoid misunderstandings.\u003cbr\u003eThe maps are linked in the database to information about the component soils and their properties for each map unit. Each map unit may contain one to three major components and some minor components. The map units are typically named for the major components. Examples of information available from the database include available water capacity, soil reaction, electrical conductivity, and frequency of flooding; yields for cropland, woodland, rangeland, and pastureland; and limitations affecting recreational development, building site development, and other engineering uses.\u003cbr\u003eSSURGO datasets consist of map data, tabular data, and information about how the maps and tables were created. The extent of a SSURGO dataset is a soil survey area, which may consist of a single county, multiple counties, or parts of multiple counties. SSURGO map data can be viewed in the Web Soil Survey or downloaded in ESRI Shapefile format. The coordinate systems are Geographic. Attribute data can be downloaded in text format.","doi_url":null,"domain":["Soils"],"draft":false,"id":"4a08ecad-e945-4aaf-be26-b03be5e09a87","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.nrcs.usda.gov/resources/data-and-reports/soil-survey-geographic-database-ssurgo"}],"name":"SSURGO: Soil Survey Geographic Database","permalink":"/catalog/datasets/4a08ecad-e945-4aaf-be26-b03be5e09a87/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NRCS","spatial_extent":"United States and the Territories; Commonwealths; and Island Nations","spatial_resolution":"1:12,000; 1:63,360","temporal_coverage":"2016","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["SSURGO soil characteristics"],"vars":"SSURGO soil characteristics","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5ebad56382ce25b51361806a"}],"access_details":null,"bbox":{"east":-65.023446015,"north":50.630121493,"south":22.592853684,"west":-127.922322113},"citation":"Falcone, J.A., 2021, Tabular county-level nitrogen and phosphorus estimates from fertilizer and manure for approximately 5-year periods from 1950 to 2017: U.S. Geological Survey data release, https://doi.org/10.5066/P9VSQN3C","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This product provides tabular estimates of kilograms of nitrogen and phosphorus from a) fertilizer, and b) manure, for counties in the conterminous United States for the period 1950-2017. Data are generated for approximate five-year periods over the time, coinciding with U.S. Department of Agriculture Census of Agriculture census years. This data release also includes a model archive suitable for recreating the 2017 fertilizer estimates.","doi_url":"https://doi.org/10.5066/P9VSQN3C","domain":["Water Quality"],"draft":false,"id":"4a8875ff-af33-4215-943f-ae25caeb78e6","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5ebad56382ce25b51361806a?f=__disk__61%2F42%2Fcc%2F6142cc33e8b946d5bdbd1e35384c53ad9fd28d79\u0026transform=1\u0026allowOpen=true"}],"name":"Tabular county-level nitrogen and phosphorus estimates from fertilizer and manure for approximately 5-year periods from 1950 to 2017","permalink":"/catalog/datasets/4a8875ff-af33-4215-943f-ae25caeb78e6/","project_use_history":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1950 - 2017","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Total nitrogen from farm and non-farm fertilizer","Total phosphorus from farm and non-farm fertilizer","Nitrogen from farm fertilizer","Phosphorus from farm fertilizer","Nitrogen from non-farm fertilizer","Phosphorus from non-farm fertilizer","Number of beef cows","Number of milk cows","Number of total cows","Number of other cows (not beef, not milk)","Number of hogs and pigs","Number of chickens (layers)","Number of broiler chickens","Number of turkeys","Number of sheep and lambs","Number of horses and ponies","Kilograms of nitrogen from cattle","Kilograms of phosphorus from cattle","Kilograms of nitrogen from hogs and pigs","Kilograms of phosphorus from hogs and pigs","Kilograms of nitrogen from poultry","Kilograms of phosphorus from poultry","Kilograms of nitrogen from sheep and horses","Kilograms of phosphorus from sheep and horses","Kilograms of nitrogen from all animal types","Kilograms of nitrogen from all animal types"],"vars":"Total nitrogen from farm and non-farm fertilizer; Total phosphorus from farm and non-farm fertilizer; Nitrogen from farm fertilizer; Phosphorus from farm fertilizer; Nitrogen from non-farm fertilizer; Phosphorus from non-farm fertilizer; Number of beef cows; Number of milk cows; Number of total cows; Number of other cows (not beef, not milk); Number of hogs and pigs; Number of chickens (layers); Number of broiler chickens; Number of turkeys; Number of sheep and lambs; Number of horses and ponies; Kilograms of nitrogen from cattle; Kilograms of phosphorus from cattle; Kilograms of nitrogen from hogs and pigs; Kilograms of phosphorus from hogs and pigs; Kilograms of nitrogen from poultry; Kilograms of phosphorus from poultry; Kilograms of nitrogen from sheep and horses; Kilograms of phosphorus from sheep and horses; Kilograms of nitrogen from all animal types; Kilograms of nitrogen from all animal types","weight":1},{"access":[{"file_format":"HDF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-mod15a2h-006"}],"access_details":"An Earthdata Login is required before users can download data or use selected tools that comprise NASA's Earth Observing System Data and Information System (EOSDIS).","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Myneni, R., Knyazikhin, Y., Park, T., 2015, MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006 [insert dataset name]. NASA EOSDIS Land Processes DAAC. Accessed [YYYY-MM-DD] at https://doi.org/10.5067/MODIS/MOD15A2H.006","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The MOD15A2H Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the \"best\" pixel available from all the acquisitions of the Terra sensor from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation, 400-700 nanometers (nm), absorbed by the green elements of a vegetation canopy. Science Datasets (SDSs) in the Level 4 (L4) MOD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MOD15A2H granule. Please note that a newer version of MODIS land products is available and plans are being developed for the retirement of Version 6 MODIS data products. Users are advised to transition to the improved Version 6.1 products as soon as possible.","doi_url":"https://doi.org/10.5067/MODIS/MOD15A2H.006","domain":["Land Cover"],"draft":false,"id":"4b0e5df0-e909-45d0-b20b-eba23452edc3","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf"}],"name":"MOD15A2H v006: MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid","permalink":"/catalog/datasets/4b0e5df0-e909-45d0-b20b-eba23452edc3/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"500 meter","temporal_coverage":"2000 - 2023","temporal_frequency":"8 days","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Leaf area index"],"vars":"Leaf area index","weight":1},{"access":[{"file_format":"GRIB","name":"rda.ucar.edu","url":"https://rda.ucar.edu/datasets/ds094.2/dataaccess/"},{"file_format":"GRIB","name":"ncei.noaa.gov","url":"https://www.ncei.noaa.gov/data/climate-forecast-system/access/operational-9-month-forecast/"},{"file_format":"GRIB","name":"ncei.noaa.gov","url":"https://www.ncei.noaa.gov/thredds/catalog/model/cfs.html"}],"access_details":"The first ncei.noaa.gov access allows for direct download of dataset files. The second ncei.noaa.gov access point links to a THREDDS Data Server (TDS).","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M., van den Dool, H., Zhang, Q., Wang, W., Chen, M. and Becker, E., 2014, The NCEP Climate Forecast System Version 2: Journal of Climate, v. 27, no. 6, p. 2185-2208, https://doi.org/10.1175/JCLI-D-12-00823.1","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the interaction between the Earth's oceans, land and atmosphere. The four-times-daily, 9-month control runs, consist of all 6-hourly forecasts, and the monthly means and variable time-series (all variables). The CFSv2 outputs include: 2-D Energetics (EGY); 2-D Surface and Radiative Fluxes (FLX); 3-D Pressure Level Data (PGB); 3-D Isentropic Level Data (IPV); 3-D Ocean Data (OCN); Low-resolution output (GRBLOW); Dumps (DMP); and High- and Low-resolution Initial Conditions (HIC and LIC). The monthly CDAS variable timeseries includes all variables. The CFSv2 period of record begins on April 1, 2011 and continues onward. CFS output is in GRIB-2 file format.","doi_url":"https://doi.org/10.1175/JCLI-D-12-00823.1","domain":["Climate"],"draft":false,"id":"4c13f2e5-1e8d-463b-8457-10d68c582844","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.1175/JCLI-D-12-00823.1"},{"name":"Metadata","url":"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00877;view=xml;responseType=text/xml"}],"name":"NCEP Climate Forecast System Version 2 (CFSv2)","permalink":"/catalog/datasets/4c13f2e5-1e8d-463b-8457-10d68c582844/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NOAA","spatial_extent":"Global","spatial_resolution":"0.3 degrees; 0.5 degrees; 1.0 degrees; 1.9 degrees; 2.5 degrees","temporal_coverage":"2011 - Present","temporal_frequency":"hourly; monthly","update_detail":"append","update_frequency":"6 hours","update_type":"Dynamic","variables":["Surface Pressure","Heat Flux","Incoming Solar Radiation","Longwave Radiation","Outgoing Longwave Radiation","Radiative Flux","Shortwave Radiation","Air Temperature","Maximum/Minimum Temperature","Upper Air Temperature","Humidity","Total Precipitable Water","Water Vapor Profiles","Precipitation Rate","Soil Moisture/Water Content","Land Surface Temperature","Snow Water Equivalent","Soil Temperature"],"vars":"Surface Pressure; Heat Flux; Incoming Solar Radiation; Longwave Radiation; Outgoing Longwave Radiation; Radiative Flux; Shortwave Radiation; Air Temperature; Maximum/Minimum Temperature; Upper Air Temperature; Humidity; Total Precipitable Water; Water Vapor Profiles; Precipitation Rate; Soil Moisture/Water Content; Land Surface Temperature; Snow Water Equivalent; Soil Temperature","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5fb7e483d34eb413d5e14873"}],"access_details":null,"bbox":{"east":-63.6722,"north":52.851,"south":21.7423,"west":-130.2328},"citation":"Wieczorek, M.E., Wolock, D.M., and McCarthy, P.M., 2021, Dam impact/disturbance metrics for the conterminous United States, 1800 to 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P92S9ZX6.","creator":[{"creator_email":"mewieczo@usgs.gov","creator_name":"Michael E Wieczorek"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This metadata record describes two metrics that quantitatively measure the impact of reservoir storage on every flowline in the NHDPlus version 2 data suite (NHDPlusV2) for the conterminous United States. These metrics are computed for every 10 years from 1800 - 2015. The first metric (DamIndex_EROM.zip) estimates reservoir storage intensity in units of days based on reservoir storage in a contributing area normalized by the mean annual streamflow. This metric indicates the duration of storage impact upstream from each stream segment relative to the typical flow condition. In addition, this metric provides an assessment of the potential influence of a dam on average and low flows because the metric estimates the number of days of flow that can be sustained by contributing area storage alone, without additional water or groundwater input. The second metric (DamIndex_PMC.zip) represents the degree of regulation of a river reach based on upstream reservoir storage relative to the 30-year average annual precipitation, as well as the upstream dam and watershed areas. This second metric provides an estimate of the capacity of the contributing area to store precipitation and is oriented to understanding how peak flows may be affected by dams throughout the flow network; this metric is dimensionless. Reservoir storage, construction date and location data were obtained from the US Army Corps of Engineers' National Inventory of Dams (NID, 2018). Also, the dataset in this data release includes dam locations addressed to NHDPlusv2 (Final_NID_2018.zip). These calculations are based on the maximum NID storage , which indicates the maximum amount of water that can be stored behind each dam and therefore may overestimate the true reservoir storage impacts.","doi_url":"https://doi.org/10.5066/P92S9ZX6","domain":["Infrastructure"],"draft":false,"id":"4c75ee6c-af2f-4e5c-84dc-2082beb70c30","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5fb7e483d34eb413d5e14873?f=__disk__8d%2Fa9%2F49%2F8da9497a8744df87b2f3e73f4163227a206fbdc7\u0026allowOpen=true"}],"name":"Dam impact/disturbance metrics for the conterminous United States, 1800 to 2018","permalink":"/catalog/datasets/4c75ee6c-af2f-4e5c-84dc-2082beb70c30/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1800 - 2018","temporal_frequency":"10 years","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["OID","COMID","MAX_DI","OID","COMID","DamIndex","FID","Shape","NIDID","YEAR_COMPL","MAX_STORAG","NORMAL_STO","NID_STORAG","FlowLcomid","J_DAM_DAcr","Dam_Acres","EROM","YR_Rmvd"],"vars":"OID; COMID; MAX_DI; OID; COMID; DamIndex; FID; Shape; NIDID; YEAR_COMPL; MAX_STORAG; NORMAL_STO; NID_STORAG; FlowLcomid; J_DAM_DAcr; Dam_Acres; EROM; YR_Rmvd","weight":1},{"access":[{"file_format":"","name":"mrcc.purdue.edu","url":"https://mrcc.purdue.edu/research/awssi"}],"access_details":null,"bbox":{},"citation":null,"creator":[],"creator_project":[],"description":"The Accumulated Winter Season Severity Index (AWSSI) objectively quantifies the relative severity of the winter season. Daily scores are calculated based on temperature, snowfall, and snow depth thresholds and accumulated through the winter season, providing both a running total during the season and a final cumulative value characterizing the full season. Derived from daily records of snowfall, snow depth, maximum and minimum surface air temperature.","doi_url":null,"domain":["Hydrology","Snow"],"draft":false,"id":"4cada657-1d64-401d-aff8-8c6c16beab32","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"553e2bbb-00ea-4052-b472-5561943d5dc6","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Accumulated Winter Season Severity Index (AWSSI)","permalink":"/catalog/datasets/4cada657-1d64-401d-aff8-8c6c16beab32/","project_use_history":[],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"MRCC","spatial_extent":"United States","spatial_resolution":"NA","temporal_coverage":"1950 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":[],"vars":"winter severity index; temperature score; snowfall score; snow depth score","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/64a84670d34e70357a27dd86"}],"access_details":null,"bbox":{"east":-120.9375,"north":72.6071,"south":48.691,"west":-180},"citation":"Koczot, K.M., Markstrom, S.L., McDonald, R.R., Norton, P.A., Dickinson, J.E., Regan, R.S., Bock, A., Santiago, M., Wieczorek, M.E., and LaFontaine, J.H., 2025, Alaska National Hydrologic Model (NHM) application,1980 - 2021 (ver. 2.0, September 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P92VZBEV.","creator":[{"creator_email":"kmkoczot@usgs.gov","creator_name":"Kathryn M, Koczot"}],"creator_project":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"\u003cp\u003eThis work contributes to an understanding of the hydrologic cycle of the State of Alaska, USA, and parts of the Canadian Yukon Territory and British Columbia Province draining into Alaska. For purposes of this study, the domain is simply referred to as “Alaska (AK)\".\u003c/p\u003e\n\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003eThis data release contains inputs for and outputs from hydrologic simulations for the Alaska (AK) domain using the Precipitation Runoff Modeling System (PRMS) version 5.2.1.1 for the precalibration, by Hydrologic Response Unit (byHRU) release, and by Point Of Interest Observation (byPOIobs) release using the USGS National Hydrologic Model infrastructure (NHM; Regan and others, 2018). These simulations were developed to provide estimates of the water budget for the calendar-year period 1980 to2021. Specific file types include: 1) monthly calibration target baselines derived from Global Circulation Model (GCM) simulations (Koczot and others, 2025), 2) map of permanent snow during January 1980 - October 2022 and resulting parameter values of initial snowpack depths, 3) input atmospheric forcings of minimum air temperature, maximum air temperature, and daily precipitation accumulation derived from Daymet Version 4 gridded estimates of daily weather parameters (Thornton and others, 2020) and input parameter and control files for each release (Bock and others, 2024; Markstrom and others, 2015), 4) output files of simulated water budget components for each hydrologic response unit and stream segment and 5) performance statistics at selected streamgage locations. Figure 1 (Rosa and others, 2025) shows the calibration methodology that was used for the model application (see Hay and others, 2023 for additional information). Figure 2 shows all the HRUs in the geospatial fabric for the AK domain (Bock and others, 2024). Table 1 (Table_1_AK_gages.csv) lists the streamgages that are included in the model application. Of the total 279 in this set, 249 were used as calibration targets in the final calibration step (added to this data release after its first version publication (January 2025)). The first three years of the simulations are considered 'model initialization' and should not be included in any subsequent analysis. The executable used for these simulations may be downloaded from https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms (version 5.2.1.1). A batch files to run the model configurations has also been included.\u003c/p\u003e\n\n\u003cdiv\u003e\u0026nbsp;\n\u003cdiv\u003e\n\u003cdiv\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cstrong\u003eCONTENTS OF THIS PARENT PAGE:\u003c/strong\u003e\n\n\u003col\u003e\n\t\u003cli\u003eFigure 1.png = Schematic of calibration procedure.\u003c/li\u003e\n\t\u003cli\u003eFigure 2.png = Map of Hydrologic Response Units (HRUs).\u003c/li\u003e\n\t\u003cli\u003eTable_1_AK_gages.csv = List of Alaska and Yukon streamflow gages and streamflow data provided as part of this modeling application (279 total).\u003c/li\u003e\n\t\u003cli\u003econtrol_data_dictionary.csv = Control File parameter and variable definitions.\u003c/li\u003e\n\t\u003cli\u003eAK_output_variables_data_dictionary.csv = Output variables definitions.\u003c/li\u003e\n\t\u003cli\u003eparameters_data_dictionary.csv = Parameter definition.\u003c/li\u003e\n\t\u003cli\u003eAK_simulated_streamflow_data_dictionary.csv = Streamflow-specific variable definitions.\u003c/li\u003e\n\t\u003cli\u003eAK_DR_V2_revision_history.txt = Readme to record changes since version 1.0 was published \u0026nbsp;(10/1/2025) at this same URL..\u003c/li\u003e\n\u003c/ol\u003e\n\u0026nbsp;\u003cbr\u003e\n\u003cstrong\u003eSee also Child Items.\u003c/strong\u003e\u003cbr\u003e\n\u0026nbsp;","doi_url":"https://doi.org/10.5066/P92VZBEV","domain":["Hydrology","Snow","Soils"],"draft":false,"id":"4e5fcba2-4a1b-40fb-bb74-6c19a319b18e","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/64a84670d34e70357a27dd86?f=__disk__10%2F3c%2F25%2F103c25b955107d3e7dbed20726f6080f195e2637\u0026allowOpen=true"}],"name":"Alaska National Hydrologic Model (NHM) application,1980 - 2021 (ver. 2.0, September 2025)","permalink":"/catalog/datasets/4e5fcba2-4a1b-40fb-bb74-6c19a319b18e/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"AK","spatial_resolution":"1:100,000","temporal_coverage":"1980 - 2021","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["variable_name","datatype","description","default","parameter_name","datatype","units","description","valid_minimum","valid_maximum","default","dimension","modules","variable_name","datatype","description","units","dimension","variable_name","datatype","long_name","units","dimensions","Station ID","Station name","Reported Altitude (feet)","Reported Drainage Area (square miles)","Modeled Drainage Area (square miles)","Warm-up Period","Calibration Period","HRU Count","Calib Picks","CanadianProvTerr","GIS_derived_Altitude (feet)","Source of streamflow data","NOTES"],"vars":"variable_name; datatype; description; default; parameter_name; datatype; units; description; valid_minimum; valid_maximum; default; dimension; modules; variable_name; datatype; description; units; dimension; variable_name; datatype; long_name; units; dimensions; Station ID; Station name; Reported Altitude (feet); Reported Drainage Area (square miles); Modeled Drainage Area (square miles); Warm-up Period; Calibration Period; HRU Count; Calib Picks; CanadianProvTerr; GIS_derived_Altitude (feet); Source of streamflow data; NOTES","weight":1},{"access":[{"file_format":"ASCII","name":"waterwatch.usgs.gov","url":"https://waterwatch.usgs.gov/index.php?id=wwds_runoff"}],"access_details":null,"bbox":{"east":-65,"north":50,"south":24,"west":-127},"citation":"Jian, X., Wolock, D., Lins, H., 2008, WaterWatch - Maps, graphs, and tables of current, recent, and past streamflow conditions, U.S. Geological Survey Fact sheet, https://doi.org/10.3133/fs20083031","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The estimates of HUC runoff were generated by combining historical flow data collected at streamgages, the drainage basins of the streamgages, and the boundaries of the HUCs. The required steps (listed below) were applied to each individual water-year (WY) from 1901 to the most recent wateryear and to each of the HUC levels (HUC2, HUC4, HUC6, and HUC8). A water-year is defined as the period from October 1 to September 30, and the water-year designation (e.g., 1971) corresponds to the year of the ending date (e.g., September 30, 1971).\u003cbr\u003e1. Streamgages were selected for each water-year based on two criteria: a complete daily flow dataset for the water-year and a reasonably accurate basin boundary. The criterion for an acceptable basin boundary was an estimated drainage basin area within 25% of the basin area recorded in the USGS National Water Information System (NWIS). The streamgage basin boundaries were delineated using the WATERSHED command in ArcGIS and the flow direction grid provided with NHDPlus.\u003cbr\u003e2. Runoff (flow per unit area) was estimated for each basin by dividing the average daily flow for the water-year by the drainage basin area. The runoff was assumed to be uniform over the entire basin area.\u003cbr\u003e3. Each basin was overlain on a geospatial dataset of HUCs to determine the area of intersection between the basin the HUCs. For each basin/HUC combination, the percentage of the basin in the HUC and the percentage of the HUC in the basin were calculated. These percentages were multiplied by each other to compute a weighting factor assigned to the basin runoff value in estimating the runoff for the HUC.\u003cbr\u003e4. The runoff values and associated weighting factors for all basins with any overlapping area with a HUC were combined, and a single weighted-average runoff value was computed for the HUC.","doi_url":"https://doi.org/10.3133/fs20083031","domain":["Hydrology"],"draft":false,"id":"4ed2dc62-cdc7-4f79-857e-495a3a21fd5c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Water Watch HUC Runoff","permalink":"/catalog/datasets/4ed2dc62-cdc7-4f79-857e-495a3a21fd5c/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1901 - Present","temporal_frequency":"monthly; quarterly; annual","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Runoff"],"vars":"Runoff","weight":1},{"access":[{"file_format":"HDF","name":"disc.gsfc.nasa.gov","url":"https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_07/summary"},{"file_format":"HDF","name":"disc.gsfc.nasa.gov","url":"https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_07/summary"},{"file_format":"HDF","name":"disc.gsfc.nasa.gov","url":"https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGM_07/summary"},{"file_format":"TIFF; HDF; NC","name":"gpm.nasa.gov","url":"https://gpm.nasa.gov/data/directory"}],"access_details":"User may have to register with an email address to access data.","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"[30 minute] Huffman, G.J., Stocker, E.F., Bolvin, D.T., Nelkin, E.J., and Tan, J., 2023, GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed [YYYY=MM-DD], https://doi.org/10.5067/GPM/IMERG/3B-HH/07\u003cbr\u003e[daily] Huffman, G.J., Stocker, E.F., Bolvin, D.T., Nelkin, E.J., and Tan, J., 2023, GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07, Edited by Andrey Savtchenko, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed [YYYY-MM-DD], https://doi.org/10.5067/GPM/IMERGDF/DAY/07\u003cbr\u003e[monthly] Huffman, G.J., Stocker, E.F., Bolvin, D.T., Nelkin, E.J., and Tan, J., 2023, GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed [YYYY-MM-DD], https://doi.org/10.5067/GPM/IMERG/3B-MONTH/07","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Global Precipitation Measurement (GPM) Final Run v07: Research-quality gridded global multi-satellite precipitation estimates with quasi-Lagrangian time interpolation, gauge data, and climatological adjustment.\u003cbr\u003eThe Integrated Multi-satellite Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team. This algorithm is intended to intercalibrate, merge, and interpolate all satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators at fine time and space scales for the TRMM and GPM eras over the entire globe. The system is run several times for each observation time, first giving a quick estimate (IMERG Early Run) and successively providing better estimates as more data arrive (IMERG Late Run). The final step uses monthly gauge data to create research-level products (IMERG Final Run).\u003cbr\u003eOverview:\u003cbr\u003eThis algorithm is intended to intercalibrate, merge, and interpolate “all” satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators at fine time and space scales for the TRMM and GPM eras over the entire globe. The system is run several times for each observation time, first giving a quick estimate (IMERG Early Run) and successively providing better estimates as more data arrive (IMERG Late Run). The final step uses monthly gauge data to create research-level products (IMERG Final Run).\u003cbr\u003eThe main differences between the IMERG Early and Late Run are:\u003cbr\u003e1. The half-hourly Final Run product uses a month-to-month adjustment to the monthly Final Run product, which combines the multi-satellite data for the month with GPCC gauge analysis. The adjustment within the month in each half hour is a ratio multiplier that's fixed for the month, but spatially varying.\u003cbr\u003e2. The Late Run is computed about 14 hours after observation time, so sometimes a microwave overpass is not delivered in time for the Late Run, but subsequently comes in and can be used in the Final.  This would affect both the half hour in which the overpass occurs, and (potentially) morphed values in nearby half hours.","doi_url":"https://doi.org/10.5067/GPM/IMERG/3B-MONTH/07","domain":["Climate"],"draft":false,"id":"4f8c63cc-1789-4114-828d-9f90be22c531","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf"}],"name":"GPM-IMERG Final Run","permalink":"/catalog/datasets/4f8c63cc-1789-4114-828d-9f90be22c531/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"0.1 degrees","temporal_coverage":"2000 - Present","temporal_frequency":"30 minutes; daily; monthly","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Precipitation"],"vars":"Precipitation","weight":1},{"access":[{"file_format":"NC; CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/626c0d67d34e76103cd2ce4a"}],"access_details":null,"bbox":{"east":-66.709,"north":52.1605,"south":24.4868,"west":-125.6836},"citation":"Markstrom, S.L., Norton, P.A., Dickinson, J.E., LaFontaine, J.H., McDonald, R.R., and Regan, R.S., 2024, Application of the National Hydrologic Model Infrastructure (NHM) with the Precipitation-Runoff Modeling System (PRMS) and Geospatial Fabric version 1.1, 1979-2021, GridMET: U.S. Geological Survey data release, https://doi.org/10.5066/P9J1LY80.","creator":[{"creator_email":"jlafonta@usgs.gov","creator_name":"Jacob H LaFontaine"}],"creator_project":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data release contains inputs for and outputs from hydrologic simulations for the conterminous United States (CONUS) using the Precipitation Runoff Modeling System (PRMS) version 5.2.1 and the USGS National Hydrologic Model infrastructure (NHM, Regan and others, 2018). These simulations were developed to provide estimates of the water budget for the period 1979 to 2021 for one pre-calibration and three calibration configurations: 1) calibration by hydrologic response unit (byHRU), 2) calibration by select headwaters (byHW), and 3) calibration by select headwaters with streamflow observations (byHWobs). The four versions of model parameters and associated model output included in this data release are described in Hay and others (2023). Specific file types include: 1) input atmospheric forcings of minimum air temperature, maximum air temperature, and daily precipitation accumulation derived from a gridded observation-based dataset developed by Abatzoglou (2013), 2) input parameter files, 3) output files of simulated water budget components for each hydrologic response unit and stream segment, and 4) performance statistics at selected streamgage locations. The first three years of the simulations are considered 'model initialization' and should not be included in any subsequent analysis.","doi_url":"https://doi.org/10.5066/P9J1LY80","domain":["Hydrology","Snow","Soils"],"draft":false,"id":"4f968a67-bc3e-4d9f-a52f-ef673a4530b1","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/626c0d67d34e76103cd2ce4a?f=__disk__8f%2F2a%2Fd6%2F8f2ad6040230688515309b130270e4e79b9ce437\u0026allowOpen=true"}],"name":"Application of the National Hydrologic Model Infrastructure (NHM) with the Precipitation-Runoff Modeling System (PRMS) and Geospatial Fabric version 1.1, 1979-2021, gridMET","permalink":"/catalog/datasets/4f968a67-bc3e-4d9f-a52f-ef673a4530b1/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1979 - 2021","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["variable_name","datatype","description","default","parameter_name","datatype","units","description","valid_minimum","valid_maximum","default","dimension","modules","datatype","description","units","dimension","poi_id","ns","nslog","mon_ns","mon_nslog","bias","mon_bias","da_actual_obs","drainage_area_model","falcone_class","latitude","longitude","variable_name","datatype","long_name","units","dimensions"],"vars":"variable_name; datatype; description; default; parameter_name; datatype; units; description; valid_minimum; valid_maximum; default; dimension; modules; datatype; description; units; dimension; poi_id; ns; nslog; mon_ns; mon_nslog; bias; mon_bias; da_actual_obs; drainage_area_model; falcone_class; latitude; longitude; variable_name; datatype; long_name; units; dimensions","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/652dc63fd34edd15305a952f"}],"access_details":null,"bbox":{"east":-67.2475,"north":70.4954,"south":21.3834,"west":-162.8829},"citation":"Johnson, Z.C., 2024, Long-term monotonic trends in annual and monthly stream temperature metrics at multi-source monitoring locations in the United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9V10XSF.","creator":[],"creator_project":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"date_created":"5/7/2024","date_updated":"6/3/2026","description":"The U.S. Geological Survey (USGS) Water Resources Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand and an understanding of the natural and human factors affecting the balance between supply and demand. A key part of the Integrated Water Availability Assessments (IWAAs) Trends and Drivers project is identifying long-term national trends in water availability, including groundwater and surface water quantity, quality, and use. This data release contains Mann-Kendall monotonic trend analyses for 55 observed annual (calendar, water, and climate years) and monthly stream temperature metrics. Data were collated (Oliver et al., 2024) from the USEPA/USGS Water Quality Portal (WQP), the USGS National Water Information System (NWIS) and EcoSHEDS, and the USDA NorWeST databases. Metrics were calculated at a total of 2,080 stream temperature monitoring locations within the conterminous United States, Alaska, Hawaii, and Puerto Rico that passed initial screening criteria, and are also included as part of this data release. Stream temperature metrics include monthly and annual summaries, extreme (i.e., min/max) and central (i.e., mean) tendencies, variability, and timing characteristics. Monthly (\"mean_[month]\") and annual (\"mean\") mean, annual maximum of seven-day averages (\"high7d\"), and annual sinusoidal regression metrics (\"ampl_median\" and \"phase_median\") were calculated using daily mean values. Monthly (\"high7dmax_[month]\") and annual (\"high7dmax\") maximum of seven-day averages and monthly (\"cvmax_[month]\") and annual (\"cvmax\") coefficient of variation were calculated using daily maximum values. The monthly (\"low7dmin_[month]\") and annual (\"low7dmin\") minimum of seven-day averages were calculated using daily minimum values.\u003cbr\u003eTrend magnitudes were computed for 1,967 qualifying monitoring locations as a modified form of the Theil-Sen slope that accounts for missing values. Trend analyses were computed between years 1948-2022 and trend periods are between 10-72 years long. Metric time series analyzed for trends satisfied two requirements to be considered complete records: (i) have values in at least eight out of every 10 years (i.e., 80 percent) within the entire trend period and (ii) have values in at least eight out of the first and last 10 years of the trend period. Trends at each site are available for four main periods: (i) the longest possible ≥10-year period that meets completeness criteria at each site, (ii) 1980-2020, (iii) 1990-2020, and (iv) 2000-2020. Additionally, trends for various ≥10-year sub-periods, between 1949-2022, are included.\u003cbr\u003eCaution must be exercised when utilizing monotonic trend analyses conducted over periods of up to several decades (and in some places longer ones) due to the potential for confounding deterministic gradual trends with multi-decadal climatic fluctuations. In addition, trend results for USGS locations (site_id prefix \"USGS-\") are only available for post-reservoir construction years to avoid including abrupt changes arising from the construction of larger reservoirs in periods for which gradual monotonic trends are computed. Reservoir impacts on non-USGS sites were not evaluated. Other abrupt changes, such as changes to water withdrawals and wastewater return flows, or episodic disturbances with multi-year recovery periods, such as wildfires, are also not evaluated for any site. Sites with pronounced abrupt changes or other non-monotonic trajectories of change may require more sophisticated trend analyses than those presented in this data release.","doi_url":"https://doi.org/10.5066/P9V10XSF","domain":["Hydrology","Water Quality"],"draft":false,"id":"50a8781a-83a5-45ae-817b-9fc869a53c5a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/652dc63fd34edd15305a952f?f=__disk__b9%2F75%2Ff6%2Fb975f65d1cd82b147756fb70c5b0d48d836119a1\u0026allowOpen=true"}],"name":"Long-term monotonic trends in annual and monthly stream temperature metrics at multi-source monitoring locations in the United States","permalink":"/catalog/datasets/50a8781a-83a5-45ae-817b-9fc869a53c5a/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"unknown","temporal_coverage":"1948 - 2022","temporal_frequency":"varies","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["id","site_id","metric_id","poi_type_id","record_segment_type_id","year_type_id","year_start","year_end","test_id","test_result","screen1_same","screen2_flag","screen3_combine","screen4_data","screen5_n_obs","screen6_data","screen7_trends","year_value","value","count","sources","longitude","latitude"],"vars":"id; site_id; metric_id; poi_type_id; record_segment_type_id; year_type_id; year_start; year_end; test_id; test_result; screen1_same; screen2_flag; screen3_combine; screen4_data; screen5_n_obs; screen6_data; screen7_trends; year_value; value; count; sources; longitude; latitude","weight":1},{"access":[{"file_format":"TXT; SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5d407318e4b01d82ce8d9b3c"}],"access_details":null,"bbox":{"east":-108.03,"north":50.19,"south":31.61,"west":-127.6},"citation":"Wise, D.R., 2020, SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Pacific Region of the United States, 2012 Base Year (ver 1.1, June 2020): U.S. Geological Survey data release, https://doi.org/10.5066/P9AXLOSM.","creator":[{"creator_email":"dawise@usgs.gov","creator_name":"Dan Wise"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The U.S. Geological Survey's (USGS) SPAtially Referenced Regression On Watershed attributes (SPARROW) model was used to aid in the interpretation of monitoring data and simulate streamflow and water-quality conditions in streams across the Pacific Region of the Unites States. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based water-quality load estimates. This USGS data release includes input and output files associated with 2012 SPARROW simulations of streamflow, total nitrogen, total phosphorus and suspended-sediment load in streams of the Pacific region. Model construction, calibration and results are described in Wise (2019, https://doi.org/10.3133/sir20195112).","doi_url":null,"domain":["Hydrology","Water Quality"],"draft":false,"id":"51eae19b-3ecf-4962-880d-a9d879fa4eb7","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[{"id":"8e649d48-f8fc-4cc6-a147-5645b54693eb","rel_type":"IsSourceOf"}],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5d407318e4b01d82ce8d9b3c?f=__disk__ab%2F27%2F08%2Fab27083f354bd851ec09bc0f33c2dc130f808bb5\u0026transform=1\u0026allowOpen=true"},{"name":"Documentation","url":"https://doi.org/10.3133/sir20195112"}],"name":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Pacific Region of the United States, 2012 Base Year (ver 1.1, June 2020)","permalink":"/catalog/datasets/51eae19b-3ecf-4962-880d-a9d879fa4eb7/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Pacific Region of the United States","spatial_resolution":"1:100,000","temporal_coverage":"1999 - 2014","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Streamflow","Total nitrogen loads","Total phosphorus loads","Suspended-sediment loads"],"vars":"Streamflow; Total nitrogen loads; Total phosphorus loads; Suspended-sediment loads","weight":1},{"access":[{"name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/655ceb8ad34ee4b6e05cc51a"},{"file_format":"TIFF","name":"WMA STAC","url":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/nlcd"}],"bbox":{"east":-63.1184,"north":52.9217,"south":21.8051,"west":-129.2773},"citation":"U.S. Geological Survey (USGS), 2024, Annual NLCD Collection 1 Science Products (ver. 1.1, June 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P94UXNTS.","creator":[],"creator_project":[],"date_created":"12/8/2025","date_updated":"6/3/2026","description":"The USGS Land Cover program has combined the tried-and-true methodologies from premier land cover projects, National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP), together with modern innovations in geospatial deep learning technologies to create the next generation of land cover and land change information. The product suite is called, “Annual NLCD” and includes six annual products that represent land cover and surface change characteristics of the U.S.:\u003cbr\u003e\n\u003cbr\u003e\n\u0026nbsp; 1) Land Cover,\u003cbr\u003e\n\u0026nbsp; 2) Land Cover Change,\u003cbr\u003e\n\u0026nbsp; 3) Land Cover Confidence,\u003cbr\u003e\n\u0026nbsp; 4) Fractional Impervious Surface,\u003cbr\u003e\n\u0026nbsp; 5) Impervious Descriptor, and\u003cbr\u003e\n\u0026nbsp; 6) Spectral Change Day of Year.\u003cbr\u003e\n\u003cbr\u003e\nThese land cover science product algorithms harness the remotely sensed Landsat data record to provide state-of-the-art land surface change information needed by scientists, resource managers, and decision-makers. Annual NLCD uses a modernized, integrated approach to map, monitor, synthesize, and understand the complexities of land use, cover, and condition change. With this second release, Annual NLCD, Collection 1.1, the six products are available for the Conterminous U.S. for 1985–2024.\u0026nbsp;\u003cbr\u003e\n\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cbr\u003e\nQuestions about the Annual NLCD product suite can be directed to the Annual NLCD mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or custserv@usgs.gov. See included spatial metadata for more details.","doi_url":"https://doi.org/10.5066/P94UXNTS","domain":["Land Cover"],"draft":false,"id":"5351b7cc-9457-4e75-a51d-808933e0446b","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"553e2bbb-00ea-4052-b472-5561943d5dc6","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[{"id":"148a7db7-1175-47aa-8437-5ae39f62b1ce","rel_type":"IsSourceOf"}],"links":[],"name":"Annual National Land Cover Database (NLCD) Collection 1.1","permalink":"/catalog/datasets/5351b7cc-9457-4e75-a51d-808933e0446b/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"1985 - 2024","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Fractional Impervious Surface","Impervious Descriptor","Land Cover","Land Cover Change","Land Cover Confidence","Spectral Change Day of Year"],"vars":"Fractional Impervious Surface; Impervious Descriptor; Land Cover; Land Cover Change; Land Cover Confidence; Spectral Change Day of Year","weight":1},{"access":[{"file_format":"WS; CSV; KML; SHP; JSON","name":"levees.sec.usace.army.mil","url":"https://levees.sec.usace.army.mil/#/"}],"access_details":null,"bbox":{"east":-56,"north":72,"south":16,"west":-179},"citation":null,"creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The National Levee Database (NLD) is the authoritative resource for information about levees in the United States. It is a modern, web-based information system that connects levee-related information and activities, including flood risk communication, levee system evaluation for the National Flood Insurance Program (NFIP), levee inspections, flood plain management, and risk assessment. The NLD is intended to be a primary information resource for federal, state and local governments, agencies, and organizations, as well as the general public.","doi_url":null,"domain":["Infrastructure"],"draft":false,"id":"537571cf-294c-4964-ae56-13fd57337c60","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://levees.sec.usace.army.mil/#/help/getting-started"}],"name":"National Levee Database","permalink":"/catalog/datasets/537571cf-294c-4964-ae56-13fd57337c60/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USACE","spatial_extent":"CONUS; AK; HI","spatial_resolution":"unknown","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":"append","update_frequency":"irregular","update_type":"Dynamic","variables":["Levee locations and other levee specific  data"],"vars":"Levee locations and other levee specific  data","weight":1},{"access":[{"file_format":"HDF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-mod16a2-006"}],"access_details":"An Earthdata Login is required before users can download data or use selected tools that comprise NASA's Earth Observing System Data and Information System (EOSDIS).","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Running, S., Mu, Q., Zhao, M., 2021, MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061., distributed by NASA EOSDIS Land Processes DAAC, accessed [YYYY-MM-DD] https://doi.org/10.5067/MODIS/MOD16A2.061. ","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover.\u003cbr\u003eProvided in the MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2 granule.\u003cbr\u003eThe pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year.","doi_url":"https://doi.org/10.5067/MODIS/MOD16A2.061","domain":["Climate","Hydrology"],"draft":false,"id":"53825ac5-aa69-4925-a767-db3eb02eaef5","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/products/mod16a2v061/"}],"name":"MOD16A2 v061: MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid","permalink":"/catalog/datasets/53825ac5-aa69-4925-a767-db3eb02eaef5/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"500 meter","temporal_coverage":"2000 - 2023","temporal_frequency":"8 days","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Total evapotranspiration (ET)","Average latent heat flux (LE)","Total potential evapotranspiration (PET)","Average potential latent heat flux (PLE)","Evapotranspiration quality control flags (ET_QC)"],"vars":"Total evapotranspiration (ET); Average latent heat flux (LE); Total potential evapotranspiration (PET); Average potential latent heat flux (PLE); Evapotranspiration quality control flags (ET_QC)","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/62336af9d34ec9f19eeb48fd"}],"access_details":null,"bbox":{"east":-65.2148,"north":49.838,"south":24.0465,"west":-125.1563},"citation":"Towler, E., Foks, S.S., Staub, L.E., Dickinson, J.E., Dugger, A.L., Essaid, H.I., Gochis, D., Hodson, T.O., Viger, R.J., and Zhang, Y., 20220711, Daily streamflow performance benchmark defined by the standard statistical suite (v1.0) for the National Water Model Retrospective (v2.1) at benchmark streamflow locations for the conterminous United States (ver 3.0, March 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9QT1KV7","creator":[],"creator_project":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"date_created":"5/2/2024","date_updated":"6/3/2026","description":"This data release contains the standard statistical suite (version 1.0) daily streamflow performance benchmark results for the National Water Model Retrospective (v2.1) at streamflow benchmark locations defined by Foks and others (2022). Modeled hourly timesteps were converted to mean daily timesteps. Model error was determined by evaluating predicted daily mean streamflow versus observed daily mean streamflow using various statistics; the Nash-Sutcliffe efficiency (NSE), the Kling-Gupta efficiency (KGE), the logNSE, the Pearson correlation coefficient, the Spearman correlation coefficient, the ratio of the standard deviation, the percent bias, the percent bias in flow duration curve midsegment slope, the percent bias in the flow duration curve high-segment volume, and the percent bias in flow duration curve low-segment volume. Two climatological KGE benchmarks are included that are calculated using daily mean streamflow observations and interannual daily mean or median flows. Additionally, KGE uncertainty estimates have been added as a separate csv file including the standard error of jackknife, standard error of bootstrap, the 5th, 50th and 95th percentiles of the estimates, the jackknife score, the bias of jackknife, the bias of bootstrap, and the standard error of jackknife after bootstrap.","doi_url":"https://doi.org/10.5066/P9QT1KV7","domain":["Hydrology"],"draft":false,"id":"53831e72-f522-4d2d-9de9-e425b9d81346","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/62336af9d34ec9f19eeb48fd?f=__disk__d5%2Fa7%2Faa%2Fd5a7aab647736d700e7832636c2233346d952819\u0026allowOpen=true"}],"name":"Streamflow standard suite benchmark results (NWM v2.1): Daily streamflow performance benchmark defined by the standard statistical suite (v1.0) for the National Water Model Retrospective (v2.1) at benchmark streamflow locations for the conterminous United States (ver 3.0, March 2023)","permalink":"/catalog/datasets/53831e72-f522-4d2d-9de9-e425b9d81346/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"unknown","temporal_coverage":"1983 - 2016","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["site_no","KGE","NSE","logNSE","r","rSpearman","rSD","PBIAS","pbiasfdc","t_n","PBIAS_HF","t_n_HF","PBIAS_LF","t_n_LF","KGE_Avg_DOY","KGE_Med_DOY","camels","GOF_stat","seJack","seBoot","p05","p50","p95","score","biasJack","biasBoot","seJab"],"vars":"site_no; KGE; NSE; logNSE; r; rSpearman; rSD; PBIAS; pbiasfdc; t_n; PBIAS_HF; t_n_HF; PBIAS_LF; t_n_LF; KGE_Avg_DOY; KGE_Med_DOY; camels; GOF_stat; seJack; seBoot; p05; p50; p95; score; biasJack; biasBoot; seJab","weight":1},{"access":[{"file_format":"NC","name":"registry.opendata.aws","url":"https://registry.opendata.aws/nwm-archive/"}],"access_details":null,"bbox":{"east":-56,"north":72,"south":14,"west":-175},"citation":"NOAA National Water Model V2.1 CONUS Retrospective Dataset was accessed [YYYY-MM-DD] at https://registry.opendata.aws/nwm-archive.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The NOAA National Water Model Retrospective dataset contains input and output from multi-decade CONUS retrospective simulations. These simulations used meteorological input fields from meteorological retrospective datasets.One application of this dataset is to provide historical context to current near real-time streamflow, soil moisture and snowpack conditions. The retrospective data can be used to infer flow frequencies and perform temporal analyses with hourly streamflow output and 3-hourly land surface output. This dataset can also be used in the development of end user applications which require a long baseline of data for system training or verification purposes.","doi_url":null,"domain":["Climate","Hydrology","Snow"],"draft":false,"id":"547ec566-cfd2-4ae2-ae9f-a9029bfc07f4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://water.noaa.gov/about/nwm"}],"name":"National Water Model v2.1 output","permalink":"/catalog/datasets/547ec566-cfd2-4ae2-ae9f-a9029bfc07f4/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NOAA","spatial_extent":"North America","spatial_resolution":"1 kilometer","temporal_coverage":"1979 - 2020","temporal_frequency":"hourly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Accumulated precipitation","Accumulated evapotranpiration","Snow water equivalent, integrated for all snow layers","Fractional snow cover","Accumulated surface runoff","Accumulated underground runoff","Volumetric soil moisture"],"vars":"Accumulated precipitation; Accumulated evapotranpiration; Snow water equivalent, integrated for all snow layers ; Fractional snow cover; Accumulated surface runoff; Accumulated underground runoff; Volumetric soil moisture","weight":1},{"access":[{"file_format":"NC","name":"globsnow.info","url":"https://www.globsnow.info/"}],"access_details":null,"bbox":{"east":180,"north":90,"south":0,"west":-180},"citation":"Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., Moisander, M., 2020, GlobSnow v3.0 snow water equivalent (SWE), PANGAEA, https://doi.org/10.1594/PANGAEA.911944","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The aim of the ESA DUE GlobSnow project is the production of global long term records of snow parameters intended for climate research purposes on hemispherical scale. Information on two essential snow parameters are available: snow water equivalent (SWE) and fractional snow extent (SE), provided for a period of 30+ years and 15+ years respectively. Demonstration of a Near Real Time snow mapping service has been on-going since October 2010. GlobSnow-combined SWE merges ex-situ passive microwave and in-situ weather station observations.","doi_url":null,"domain":["Snow"],"draft":false,"id":"56863179-4498-4ac1-853e-deae94e6a3da","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.globsnow.info/swe/GlobSnow2_SE_SWE_Product_User_Guide_v1_r1.pdf"}],"name":"GlobSnow","permalink":"/catalog/datasets/56863179-4498-4ac1-853e-deae94e6a3da/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"ESA; Academic Institution(s)","spatial_extent":"Northern Hemishpere","spatial_resolution":"0.01 dgrees","temporal_coverage":"1979 - 2018","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Snow extent","Snow water equivalent"],"vars":"Snow extent; Snow water equivalent","weight":1},{"access":[{"file_format":"TIF","name":"cec.org","url":"http://www.cec.org/north-american-environmental-atlas/land-cover-30m-2015-landsat-and-rapideye/"}],"access_details":null,"bbox":{"east":-50,"north":85,"south":14,"west":-180},"citation":"Commission for Environmental Cooperation (CEC), 2020, 2015 Land Cover of North America at 30 Meters. North American Land Change Monitoring System, Canada Centre for Remote Sensing (CCRS), U.S. Geological Survey (USGS), Comision Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO), Comision Nacional Forestal (CONAFOR), Instituto Nacional de Estadistica y Geografia (INEGI) Edition 2.0, Raster digital data [30-m], accessed [YYYY-MM-DD], http://www.cec.org/north-american-environmental-atlas/land-cover-30m-2015-landsat-and-rapideye/","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The 2015 North American Land Cover 30-meter dataset was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia), National Commission for the Knowledge and Use of the Biodiversity (Comision Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comision Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries.\u003cbr\u003eThe general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country's specific requirements.\u003cbr\u003eThis 30-meter dataset of North American Land Cover reflects land cover information for 2015 from Mexico and Canada and 2016 for the United States. Each country developed its own classification method to identify Land Cover classes and then provided an input layer to produce a continental Land Cover map across North America. Canada, Mexico, and the United States developed their own 30-meter land cover products; see specific sections on data generation in the metadta.\u003cbr\u003eThe main inputs for image classification were 30-meter Landsat 4-8 Collection 1 Level 2 data in the Canada and United States Maps (including Alaska) and 5-meters RapidEye for Mexico land cover map. Image selection processes and reduction to specific spectral bands varied among the countries due to study-site-specific requirements. While Canada selected most images from the year 2015 with a few from 2014 and 2016, the United States employed mainly images from 2016 with few images from 2015 and 2017. Mexico used all available 5-meters RapidEye images from January to December 2015, orthorectified as individual 25 by 25 kilometers tiles and then resampled to 30-meters pixel size.\u003cbr\u003eIn order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by CONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each country chose their own approaches, ancillary data, and land cover mapping methodologies to create national datasets. This North America dataset was produced by combining the national land cover datasets. The integration of the three national products merged four Land Cover map sections, Alaska, Conterminous United States and Mexico.\u003cbr\u003eIn this second version of the North America Land Cover map, the Alaska section has been updated from the map released in February 2020 (version 1). The United States Geological Survey (USGS) provided an updated land cover map of Alaska in July 2020, which was consequently integrated in the continental map and replaced the first version. ","doi_url":null,"domain":["Land Cover"],"draft":false,"id":"569d3f89-6168-4034-9421-c40fd8ffcaf7","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"http://www.cec.org/north-american-environmental-atlas/land-cover-30m-2015-landsat-and-rapideye/"}],"name":"Land cover 2015 (Landsat and RapidEye)","permalink":"/catalog/datasets/569d3f89-6168-4034-9421-c40fd8ffcaf7/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"CEC","spatial_extent":"North America","spatial_resolution":"30 meter","temporal_coverage":"2015","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Temperate or sub-polar needleleaf forest","Sub-polar taiga needleleaf forest","Tropical or sub-tropical broadleaf evergreen forest","Tropical or sub-tropical broadleaf deciduous forest","Temperate or sub-tropical broadleaf evergreen forest","Mixed forest","Tropical or sub-tropical shrubland","Temperate or sub-polar shrubland","Tropical or sub-tropical grassland","Temperate or sub-polar grassland","Sub-polar or polar shrubland-lichen-moss","Sub-polar or polar grassland-lichen-moss","Sub-polar or polar barren-lichen-moss, Wetland","Cropland","Barren land","Urban and built-up","Water","Snow and ice"],"vars":"Temperate or sub-polar needleleaf forest; Sub-polar taiga needleleaf forest; Tropical or sub-tropical broadleaf evergreen forest; Tropical or sub-tropical broadleaf deciduous forest; Temperate or sub-tropical broadleaf evergreen forest; Mixed forest; Tropical or sub-tropical shrubland; Temperate or sub-polar shrubland; Tropical or sub-tropical grassland; Temperate or sub-polar grassland; Sub-polar or polar shrubland-lichen-moss; Sub-polar or polar grassland-lichen-moss; Sub-polar or polar barren-lichen-moss, Wetland; Cropland; Barren land; Urban and built-up; Water; Snow and ice","weight":1},{"access":[{"file_format":"HDF","name":"gmao.gsfc.nasa.gov","url":"https://gmao.gsfc.nasa.gov/gmao-products/merra-2/data-access_merra-2/"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Global Modeling and Assimilation Office, 2015, MERRA-2 3D IAU State, Meteorology Instantaneous 3-hourly (p-coord, 0.625x0.5L42), version 5.12.4: Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), Accessed [YYYY-MM-DD] at https://doi.org/10.5067/VJAFPLI1CSIV.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"NOTE: MERRA production has been discontiued with the completion of analyses through February 29, 2016.\u003cbr\u003eMERRA-2 is now available.\u003cbr\u003e\u003cbr\u003eMERRA-Land is a land-only (\"off-line\") replay of the MERRA land model component with two key changes from MERRA:\u003cbr\u003e1. MERRA-Land precipitation forcing is based on merging a gauge-based data product from the NOAA Climate Prediction Center with MERRA precipitation.\u003cbr\u003e2. The Catchment land surface model used in MERRA-Land was updated from the \"MERRA\" version (used in GEOS-5.2.0) to the \"Fortuna-2.5\" version (used operationally in GEOS-5.7.2 since Aug 2011).\u003cbr\u003eThese two changes lead to various science improvements that are documented, for a preliminary version of the MERRA-Land data product, in Reichle and others, 2011 and, for the official MERRA-Land data product, in the MERRA-Land documentation.\u003cbr\u003eWith a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies.\u003cbr\u003e\u003cbr\u003eMERRA-Land is a land-only (\"off-line\") replay of the MERRA land model component with two key changes from MERRA: MERRA-Land precipitation forcing is based on merging a gauge-based data product from the NOAA Climate Prediction Center with MERRA precipitation.\u003cbr\u003eThe MERRA-Land data product provides an improved set of land surface hydrological fields. Its \"mld\" collection of time-varying land surface fields is designed to include all fields that are present in the standard MERRA \"lnd\" collection and a few additional fields of interest for land surface research and applications, notably: (i) the 6-layer soil temperature profile; (ii) the gridded land surface temperature, and (iii) the gridded soil moisture in volumetric units (m3 m-3) as well as in dimensionless units of degree of saturation (or wetness), including the total profile soil moisture. Additional details can be found in the MERRA-Land documentation.","doi_url":"https://doi.org/10.5067/VJAFPLI1CSIV","domain":["Climate"],"draft":false,"id":"56f2886f-8a21-4155-881e-08d4c995b768","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://gmao.gsfc.nasa.gov/gmao-products/merra-2/documentation_merra-2/"},{"name":"Documentation","url":"https://doi.org/10.1175/JCLI-D-10-05033.1"}],"name":"MERRA-Land","permalink":"/catalog/datasets/56f2886f-8a21-4155-881e-08d4c995b768/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"0.5 degrees by 0.625 degrees","temporal_coverage":"1979 - 2016","temporal_frequency":"hourly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Soil moisture","Soil temperature","Land surface temperature"],"vars":"Soil moisture; Soil temperature; Land surface temperature","weight":1},{"access":[{"file_format":"GDB; GPKG","name":"fws.gov","url":"https://www.fws.gov/wetlands/data/State-Downloads.html"}],"access_details":"Access is by State, for larger area contact the Wetlands Team (wetlands_team@fws.gov) to request a custom download.","bbox":{"east":180,"north":72,"south":0,"west":-180},"citation":"Cowardin, L.M., Carter, V., Golet, F.C., and LaRoe, E.T., 1979. Classification of Wetlands and Deepwater Habitats of the United States: U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31, https://www.fws.gov/program/national-wetlands-inventory","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The National Wetlands Inventory (NWI) was established by the US Fish and Wildlife Service (FWS) to conduct a nationwide inventory of U.S. wetlands to provide biologists and others with information on the distribution and type of wetlands to aid in conservation efforts. To do this, the NWI developed a wetland classification system (Cowardin and others 1979) that is now the official FWS wetland classification system and the Federal standard for wetland classification (adopted by the Federal Geographic Data Committee on July 29, 1996: 61 Federal Register 39465). The NWI also developed techniques for mapping and recording the inventory findings. The NWI relies on trained image analysts to identify and classify wetlands and deep water habitats from aerial imagery. NWI started mapping wetlands at a small scale (1:250,000 map which covers an area the size of 128-1:24,000 USGS topographic maps or approximately 7,400 square miles). Eventually, large-scale (1:24K scale) maps became the standard product delivered by NWI. As computerized mapping and geospatial technology evolved, NWI discontinued production of paper maps in favor of distributing data via on-line \"mapping tools\" where information can be viewed and downloaded. Today, FWS serves its data via an on-line data discovery, the Wetlands Mapper. GIS users can access wetlands data through an on-line wetland mapping service or download data for various applications (maps, data analyses, and reports). The techniques used by NWI have recently been adopted by the Federal Geographic Data Committee as the federal wetland mapping standard (FGDC Wetlands Subcommittee 2009). This standard applies to all federal grants involving wetland mapping to insure the data can be added to the Wetlands Layer of the National Spatial Data Infrastructure. NWI also produces national wetlands status and trends reports required by Congress.","doi_url":null,"domain":["Land Cover"],"draft":false,"id":"5703ddee-27c0-4d23-be5c-1868a47c4434","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.fws.gov/program/national-wetlands-inventory/metadata"},{"name":"Documentation","url":"https://www.fws.gov/library/collections/wetlands-status-and-trends-technical-documents"}],"name":"National Wetlands Inventory","permalink":"/catalog/datasets/5703ddee-27c0-4d23-be5c-1868a47c4434/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USFWS","spatial_extent":"CONUS; AK; HI; PR; VI; Pacific Trust Islands","spatial_resolution":"1:24,000","temporal_coverage":"1977 - 2021","temporal_frequency":"irregular","update_detail":"append and modify","update_frequency":"irregular","update_type":"Dynamic","variables":["Wetlands"],"vars":"Wetlands","weight":1},{"access":[{"file_format":"SHP","name":"podaac.jpl.nasa.gov","url":"https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_D"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Jet Propulsion Laboratory, 2025, Surface Water Ocean Topography (SWOT): SWOT Level 2 River Single-Pass Vector Data Product, Version D. Ver. D. PO.DAAC, accessed [YYYY-MM-DD] at https://doi.org/10.5067/SWOT-RIVERSP-D","creator":[],"creator_project":[],"date_created":"5/2/2025","date_updated":"6/3/2026","description":"The SWOT Level 2 River Single-Pass Vector Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, slope, width, and discharge derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025. Water surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.","doi_url":"https://doi.org/10.5067/SWOT-RIVERSP-D","domain":["Hydrology"],"draft":false,"id":"5848cf5c-0165-452b-a0ee-8542efe666c2","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://deotb6e7tfubr.cloudfront.net/s3-edaf5da92e0ce48fb61175c28b67e95d/podaac-ops-cumulus-docs.s3.us-west-2.amazonaws.com/web-misc/swot_mission_docs/D-109532_SWOT_UserHandbook_RevA_20250311_sig-final.pdf?A-userid=None\u0026Expires=1758933028\u0026Signature=lJs9NvLaxhlhN2oh98cFynJfvEkhK3jjijkwG1nvlsC7Zc3-TJa2Oe2KDEsn~-NNq6FXubuWRwtS1VRJl7rDv7Ohgwt5yn2nmqAQXREDzLkCfUEf28GY9ydVEPuCw1z6fKWTaYnjPjnN4tE3FQ1ES83DykYRgZqWUZbTTQ5ZJW-FYvyvya8vkIGRvozVuwnosFTtj19RBXGCkArrxiYPfQAXhBo3R1bweaXmHH1nK585qoZc3ziVwwPOgppcezSN4wA47jFZMPoA95G-kQuG5c9LHBRoxwlDu6IhnPH-Qqq1Eh0Zk5J0QCcpdezqOB~-8KMXlZ7mURJUaWzDbeEW4w__\u0026Key-Pair-Id=K3RGFTW2DFGMID"}],"name":"SWOT Level 2 River Single-Pass Vector Data Product, Version D","permalink":"/catalog/datasets/5848cf5c-0165-452b-a0ee-8542efe666c2/","project_use_history":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"project_using":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"ref_fabric":false,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"120 kilometer","temporal_coverage":"2023 - Present","temporal_frequency":"3 weeks","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["reach ID from prior river database","time (UTC)","time (TAI)","latitude","longitude","river names","water surface elevation with respect to the geoid","total uncertainty in the water surface elevation","random-only uncertainty in the water surface elevation","constrained water surface elevation with respect to the geoid","total uncertainty in the constrained water surface elevation","water surface slope with respect to the geoid","total uncertainty in the water surface slope","random uncertainty in the water surface slope","enhanced water surface slope with respect to the geoid","uncertainty in the enhanced water surface slope","random uncertainty in the enhanced water surface slope","reach width","total uncertainty in the reach width","constrained reach width","total uncertainty in the constrained reach width","total water surface area including dark water","uncertainty in the total water surface area","surface area of detected water pixels","uncertainty in the surface area of detected water","area used to compute water surface elevation","change in cross-sectional area","total uncertainty of the change in the cross-sectional area","metric of layover effect","mean distance between observed and prior river database node locations","along-stream location offset between the observed and prior reach location","distance to the satellite ground track","consensus discharge","uncertainty in consensus discharge","fractional systematic uncertainty in consensus discharge","consensus discharge quality flag","gauge-constrained consensus discharge","uncertainty in gauge-constrained consensus discharge","fractional systematic uncertainty in gauge-constrained consensus discharge","gauge-constrained consensus discharge quality flag","MetroMan discharge","uncertainty in MetroMan discharge","fractional systematic uncertainty in MetroMan discharge","MetroMan discharge quality flag","gauge-constrained MetroMan discharge","uncertainty in gauge-constrained MetroMan discharge","fractional systematic uncertainty in gauge-constrained MetroMan discharge","gauge-constrained MetroMan discharge quality flag","BAM discharge","uncertainty in BAM discharge","fractional systematic uncertainty in BAM discharge","BAM discharge quality flag","gauge-constrained BAM discharge","uncertainty in gauge-constrained BAM discharge","fractional systematic uncertainty in gauge-constrained BAM discharge","gauge-constrained BAM discharge quality flag","HiVDI discharge","uncertainty in HiVDI discharge","fractional systematic uncertainty in HiVDI discharge","HiVDI discharge quality flag","gauge-constrained HiVDI discharge","uncertainty in gauge-constrained HiVDI discharge","fractional systematic uncertainty in gauge-constrained HiVDI discharge","gauge-constrained HiVDI discharge quality flag","MOMMA discharge","uncertainty in MOMMA discharge","fractional systematic uncertainty in MOMMA discharge","MOMMA discharge quality flag","gauge-constrained MOMMA discharge","uncertainty in gauge-constrained MOMMA discharge","fractional systematic uncertainty in gauge-constrained MOMMA discharge","gauge-constrained MOMMA discharge quality flag","SADS discharge","uncertainty in SADS discharge","fractional systematic uncertainty in SADS discharge","SADS discharge quality flag","gauge-constrained SADS discharge","uncertainty in gauge-constrained SADS discharge","fractional systematic uncertainty in gauge-constrained SADS discharge","gauge-constrained SADS discharge quality flag","SIC4DVar discharge","uncertainty in SIC4DVar discharge","fractional systematic uncertainty in SIC4DVar discharge","SIC4DVar discharge quality flag","gauge-constrained SIC4DVar discharge","uncertainty in gauge-constrained SIC4DVar discharge","fractional systematic uncertainty in gauge-constrained SIC4DVar discharge","gauge-constrained SIC4DVar discharge quality flag","bitwise quality indicator for discharge","bitwise quality indicator for gauge-constrained discharge","summary quality indicator for the reach","bitwise quality indicator for the reach","fractional area of dark water","climatological ice cover flag","dynamic ice cover flag","partial reach coverage flag","number of nodes in the reach that have a valid WSE","fraction of nodes that have a valid WSE","quality of the cross-over calibration","geoid height","geoid slope","solid Earth tide height","geocentric load tide height (FES)","geocentric load tide height (GOT)","geocentric pole tide height","dry troposphere vertical correction","wet troposphere vertical correction","ionosphere vertical correction","WSE correction from KaRIn crossovers","number of upstream reaches","number of downstream reaches","reach ID of upstream reaches","reach ID of downstream reaches","reach water surface elevation","reach water surface elevation variability","reach width","reach width variability","number of nodes in the reach","distance from the reach to the outlet","length of reach","mean annual flow","dam ID from GRanD database","maximum number of channels detected in the reach","mode of the number of channels in the reach","low slope flag"],"vars":"reach ID from prior river database; time (UTC); time (TAI); latitude; longitude; river names; water surface elevation with respect to the geoid; total uncertainty in the water surface elevation; random-only uncertainty in the water surface elevation; constrained water surface elevation with respect to the geoid; total uncertainty in the constrained water surface elevation; water surface slope with respect to the geoid; total uncertainty in the water surface slope; random uncertainty in the water surface slope; enhanced water surface slope with respect to the geoid; uncertainty in the enhanced water surface slope; random uncertainty in the enhanced water surface slope; reach width; total uncertainty in the reach width; constrained reach width; total uncertainty in the constrained reach width; total water surface area including dark water; uncertainty in the total water surface area; surface area of detected water pixels; uncertainty in the surface area of detected water; area used to compute water surface elevation; change in cross-sectional area; total uncertainty of the change in the cross-sectional area; metric of layover effect; mean distance between observed and prior river database node locations; along-stream location offset between the observed and prior reach location; distance to the satellite ground track; consensus discharge; uncertainty in consensus discharge; fractional systematic uncertainty in consensus discharge; consensus discharge quality flag; gauge-constrained consensus discharge; uncertainty in gauge-constrained consensus discharge; fractional systematic uncertainty in gauge-constrained consensus discharge; gauge-constrained consensus discharge quality flag; MetroMan discharge; uncertainty in MetroMan discharge; fractional systematic uncertainty in MetroMan discharge; MetroMan discharge quality flag; gauge-constrained MetroMan discharge; uncertainty in gauge-constrained MetroMan discharge; fractional systematic uncertainty in gauge-constrained MetroMan discharge; gauge-constrained MetroMan discharge quality flag; BAM discharge; uncertainty in BAM discharge; fractional systematic uncertainty in BAM discharge; BAM discharge quality flag; gauge-constrained BAM discharge; uncertainty in gauge-constrained BAM discharge; fractional systematic uncertainty in gauge-constrained BAM discharge; gauge-constrained BAM discharge quality flag; HiVDI discharge; uncertainty in HiVDI discharge; fractional systematic uncertainty in HiVDI discharge; HiVDI discharge quality flag; gauge-constrained HiVDI discharge; uncertainty in gauge-constrained HiVDI discharge; fractional systematic uncertainty in gauge-constrained HiVDI discharge; gauge-constrained HiVDI discharge quality flag; MOMMA discharge; uncertainty in MOMMA discharge; fractional systematic uncertainty in MOMMA discharge; MOMMA discharge quality flag; gauge-constrained MOMMA discharge; uncertainty in gauge-constrained MOMMA discharge; fractional systematic uncertainty in gauge-constrained MOMMA discharge; gauge-constrained MOMMA discharge quality flag; SADS discharge; uncertainty in SADS discharge; fractional systematic uncertainty in SADS discharge; SADS discharge quality flag; gauge-constrained SADS discharge; uncertainty in gauge-constrained SADS discharge; fractional systematic uncertainty in gauge-constrained SADS discharge; gauge-constrained SADS discharge quality flag; SIC4DVar discharge; uncertainty in SIC4DVar discharge; fractional systematic uncertainty in SIC4DVar discharge; SIC4DVar discharge quality flag; gauge-constrained SIC4DVar discharge; uncertainty in gauge-constrained SIC4DVar discharge; fractional systematic uncertainty in gauge-constrained SIC4DVar discharge; gauge-constrained SIC4DVar discharge quality flag; bitwise quality indicator for discharge; bitwise quality indicator for gauge-constrained discharge; summary quality indicator for the reach; bitwise quality indicator for the reach; fractional area of dark water; climatological ice cover flag; dynamic ice cover flag; partial reach coverage flag; number of nodes in the reach that have a valid WSE; fraction of nodes that have a valid WSE; quality of the cross-over calibration; geoid height; geoid slope; solid Earth tide height; geocentric load tide height (FES); geocentric load tide height (GOT); geocentric pole tide height; dry troposphere vertical correction; wet troposphere vertical correction; ionosphere vertical correction; WSE correction from KaRIn crossovers; number of upstream reaches; number of downstream reaches; reach ID of upstream reaches; reach ID of downstream reaches; reach water surface elevation; reach water surface elevation variability; reach width; reach width variability; number of nodes in the reach; distance from the reach to the outlet; length of reach; mean annual flow; dam ID from GRanD database; maximum number of channels detected in the reach; mode of the number of channels in the reach; low slope flag","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5c58873ae4b0708288ff28ad"}],"access_details":null,"bbox":{"east":-65,"north":50,"south":24,"west":-127},"citation":"Wieczorek, M.E., and Sabitov, T.Y., 2019, 30 year (1981 - 2010) annual average number of occurrences of dry and wet events for the Conterminous United States and District of Columbia: U.S. Geological Survey data release, https://doi.org/10.5066/P9XIXKAG.","creator":[{"creator_email":"mewieczo@usgs.gov","creator_name":"Michael E Wieczorek"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This metadata record describes the annual average number of occurrences of dry and wet events during the 30-year period 1981 – 2010 for the conterminous United States. A wet event is defined as a period when the number of consecutive days with precipitation equals or exceeds 1 millimeter. A dry event is defined as a period when the number of consecutive days with precipitation equals 0 millimeters. The source data was produced and acquired from DAYMET (2018) and is presented here as a 1-kilometer resolution GeoTIFF file.","doi_url":"https://doi.org/10.5066/P9XIXKAG","domain":["Climate"],"draft":false,"id":"5bc5695b-092b-4187-9ae1-5f63ebe482bc","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5c58873ae4b0708288ff28ad?f=__disk__f8%2Fdb%2Fc8%2Ff8dbc8fd0f2aa2ab7df00280df094afac392cb9a\u0026allowOpen=true"}],"name":"30 year (1981 - 2010) annual average number of occurrences of dry and wet events for the Conterminous United States and District of Columbia","permalink":"/catalog/datasets/5bc5695b-092b-4187-9ae1-5f63ebe482bc/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1 kilometer","temporal_coverage":"1981 - 2010","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Precipitation, occurrences of dry events","Precipitation, occurrences of wet events"],"vars":"Precipitation, occurrences of dry events; Precipitation, occurrences of wet events","weight":1},{"access":[{"file_format":"CSV; SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5f6a26af82ce38aaa2449100"}],"access_details":null,"bbox":{"east":-74.380785128688,"north":42.4544544671721,"south":38.7894548062558,"west":-76.3879905924101},"citation":"Oliver, S.K., Appling, A.A., Atshan, R., Watkins, W.D., Sadler, J., Corson-Dosch, H., Zwart, J.A., and Read, J.S., 2021, Predicting water temperature in the Delaware River Basin: U.S. Geological Survey data release, https://doi.org/10.5066/P9GD8I7A","creator":[{"creator_email":"soliver@usgs.gov","creator_name":"Sam Oliver"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Daily temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish and mussel species. This data release supports a variety of flow and water temperature modeling efforts and provides the inputs and outputs of both machine learning and process-based modeling methods across 456 river reaches and 2 reservoirs in the DRB.\u003cbr\u003eThe data are organized into these items:\u003cbr\u003e1. Waterbody Information - One shapefile of polylines for the 456 river segments in this study, a reservoir polygon metadata file, and one shapefile of reservoir polygons for the Pepacton and Cannonsville reservoirs\u003cbr\u003e2. Observations - Water temperature and streamflow observations for river reaches used in this study. Water temperature and streamflow observations for inflow and outflow reaches of the Pepacton and Cannonsville reservoirs. Water temperature, water level, and release observations for the Pepacton and Cannonsville reservoirs.\u003cbr\u003e3. Model Configurations - Model parameters and metadata used to configure GLM 3.1 reservoir models.\u003cbr\u003e4. Model Inputs - Data used to drive predictive models (distance matrices, river reach metadata, daily meteorology for river reaches and reservoirs, observed reservoir diversions and releases).\u003cbr\u003e5. Model Predictions - PRMS-SNTemp predictions of water temperature for inflow and outflow reaches of the Pepacton and Cannonsville reservoirs, GLM 3.1 predictions of ourflow and water temperature for reservoir outflow reaches, GLM 3.1 predictions of in-reservoir water temperatures at the depth of reservoir outlets, stream temperature predictions from the distance-weighted-average lotic-lentic input network, and 7-day ahead deep learning water temperature forecasts at 5 priority sites.","doi_url":"https://doi.org/10.5066/P9GD8I7A","domain":["Water Quality"],"draft":false,"id":"5be166fb-aab6-4a55-aff7-db4ad73748ce","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5f6a26af82ce38aaa2449100?f=__disk__b8%2F6b%2Fb2%2Fb86bb2934efd202aeaa3b54e22bb32040adfe0d9\u0026transform=1\u0026allowOpen=true"},{"name":"Documentation","url":"https://doi.org/10.5066/P9GD8I7A"}],"name":"Predicting water temperature in the Delaware River Basin","permalink":"/catalog/datasets/5be166fb-aab6-4a55-aff7-db4ad73748ce/","project_use_history":[],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Delaware River basin","spatial_resolution":"1:24,000; 1:100,000","temporal_coverage":"1980 - 2021","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["NHGF v1.0 segments","NHD HR reservoir polygons","EcoSHEDS and NWIS sites","Model configurations","Model inputs","Salinity prediction data"],"vars":"NHGF v1.0 segments; NHD HR reservoir polygons; EcoSHEDS and NWIS sites; Model configurations; Model inputs; Salinity prediction data","weight":1},{"access":[{"file_format":"HDF","name":"nsidc.org","url":"https://nsidc.org/data/SPL2SMAP_S/versions/3"}],"access_details":null,"bbox":{"east":180,"north":60,"south":-60,"west":-180},"citation":"Das, N., D. Entekhabi, R. S. Dunbar, S. Kim, S. Yueh, A. Colliander, P. E. O'Neill, T. Jackson, T. Jagdhuber, F. Chen, W. T. Crow, J. Walker, A. Berg, D. Bosch, T. Caldwell, and M. Cosh, 2020, SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture, Version 3. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed [YYYY-MM-DD], https://doi.org/10.5067/ASB0EQO2LYJV","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).","doi_url":"https://doi.org/10.5067/ASB0EQO2LYJV","domain":["Soils"],"draft":false,"id":"5ca64cee-a28c-4662-992f-85aceacf3e63","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://nsidc.org/data/SPL2SMAP_S/versions/3"}],"name":"SMAP/Sentinel-1 L2 v003","permalink":"/catalog/datasets/5ca64cee-a28c-4662-992f-85aceacf3e63/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NSIDC","spatial_extent":"Global","spatial_resolution":"3 kilometer","temporal_coverage":"2015 - Present","temporal_frequency":"30 seconds","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Soil moisture"],"vars":"Soil moisture","weight":1},{"access":[{"file_format":"SHP; GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/537a59ece4b0efa8af081526"}],"access_details":null,"bbox":{"east":-63.21,"north":50.741871378,"south":17.1,"west":-161.31},"citation":"Viger, R.J., 2014, Geospatial Fabric Attribute Tables for PRMS Geographic/Geometric Parameters (Preliminary): U.S. Geological Survey, http://dx.doi.org/doi:10.5066/F7HD7SPJ","creator":[{"creator_email":"rviger@usgs.gov","creator_name":"Roland Viger"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"\u003cbr /\u003e \n\u003cstrong\u003e(\u003ca href=\"http://wwwbrr.cr.usgs.gov/projects/SW_MoWS/GeospatialFabric.html\"\u003eHyperlink to Official Landing Page for Geospatial Fabric products\u003c/a\u003e)\u003c/strong\u003e \n\u003cbr /\u003e \n\u003cbr /\u003eThis dataset contains a set of attributes describing the \u0026quot;nhru\u0026quot; GIS features (Hydrologic Response Units)in the Geospatial Fabric Features dataset(http://dx.doi.org/doi:10.5066/F7542KMD) that have been developed in support of the USGS PRMS watershed model. These tables are organized according to Geospatial Fabric Region; see the thumbnail of the Geospatial Fabric Features Regions (https://www.sciencebase.gov/catalog/item/535edb4ae4b08e65d60fc837). Each table contains a key field, \u0026quot;hru_id\u0026quot;, that can be used to relate to the nhru feature class in the Geospatial Fabric Feature dataset for the corresponding Region. The methodologies used to derive the individual attributes can be located in the Appendix of the GIS Weasel Users Manual by the name of the attribute, which is the same as the name of the corresponding PRMS parameter, in (Viger and Leavesley, 2007). The metadata for each table within the current container identifies any ancillary datasets used to produce the table fields.For nhru instances that are partially or entirely beyond the borders of the United States, supporting GIS data was generally lacking. Where the value for a field could not be determined, values derived at the border were spatially extended to these areas to support derivation of a value. Users may want to review and modify the field values for these HRUs.Viger, R.J., and Leavesley, G.H., 2007, The GIS Weasel user's manual: U.S. Geological Survey Techniques and Methods, book 6, chap. B4, 201 p.","doi_url":"https://doi.org/10.5066/F7HD7SPJ","domain":["Hydrology"],"draft":false,"id":"5f521dde-9105-47ed-8367-7f6c4d673c59","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/537a59ece4b0efa8af081526?f=__disk__0c%2Ff4%2F21%2F0cf421d883b3e9d4fecd950731b00a9e7c7d64d2\u0026allowOpen=true"}],"name":"Geographic parameters for the Geospatial Fabric v1.0","permalink":"/catalog/datasets/5f521dde-9105-47ed-8367-7f6c4d673c59/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; HI; PR","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["hru_id","hru_long","hru_x","hru_y","hru_lat","hru_area"],"vars":"hru_id; hru_long; hru_x; hru_y; hru_lat; hru_area","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/628e0f4ed34ef70cdba3f98d"}],"access_details":null,"bbox":{"east":-69.1875,"north":49.0235,"south":26.0159,"west":-123.2435},"citation":"Lindsey, B.D., May, A.N., and Johnson, T.D., 2022, Data from Decadal Change in Groundwater Quality Web Site, 1988-2021: U.S. Geological Survey data release, https://doi.org/10.5066/P9FZT1WO.","creator":[{"creator_email":"blindsey@usgs.gov","creator_name":"Bruce D. Lindsey"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Evaluating Decadal Changes in Groundwater Quality: Groundwater-quality data were collected from 5,000 wells between 1988-2001 (first decadal sampling event) by the National Water-Quality Assessment Project. Samples are collected in groups of 20-30 wells with similar characteristics called networks. About 1,500 of these wells in 67 networks were sampled again approximately 10 years later between 2002-2012 (second sampling event) to evaluate decadal changes in groundwater quality. Between 2012 and 2021 (third sampling event), a subset of these networks were sampled again, allowing additional results to be displayed on the web page: Decadal changes in groundwater quality (https://nawqatrends.wim.usgs.gov/decadal/). This is the sixth iteration of data added to the website. With the additional data, it is possible to evaluate changes in water quality between the 2nd and 3rd sampling events for 73 networks, changes in water quality between the 1st and 3rd sampling events for 61 networks, and changes across all 3 sampling events for 58 networks. Samples were obtained from monitoring wells, domestic-supply wells, and some public-supply wells before any treatment on the system.\u003cbr\u003e\n\u003cbr\u003e\nGroundwater samples used to evaluate decadal change were collected from networks of wells with similar characteristics. Some networks, consisting of domestic or public-supply wells, were used to assess changes in the quality of groundwater used for drinking water supply. Other networks, consisting of monitoring wells, assessed changes in the quality of shallow groundwater underlying key land-use types such as agricultural or urban lands. Networks were chosen based on geographic distribution across the Nation and to represent the most important water-supply aquifers and specific land-use types.\u003cbr\u003e\n\u003cbr\u003e\nDecadal changes in concentrations of nutrients, metals, and pesticides and other organic contaminants in groundwater were evaluated in a total of 89 networks across the Nation by comparing changes between selected sampling events.\u003cbr\u003e\n\u003cbr\u003e\nDecadal changes in median concentrations for a network are classified as large, small, or no change in comparison to a benchmark concentration. For example, a large change in chloride concentrations indicates that the probability of the test is less than or equal to 0.10 and the median of all differences in concentrations in a network is greater than 5 percent of the chloride benchmark per decade. For chloride, which has a Secondary Maximum Contaminant level of 250 milligrams per liter, this would mean the change in concentration exceeded 12.5 milligrams per liter (mg/L), or 5 percent of the benchmark.\u003cbr\u003e\n\u003cbr\u003e\n230 networks were sampled from 1988 to 2001 to assess the status of the Nation's groundwater quality. Each dot on the map on the \"About-Learn more\" tab of the Decadal mapper website, (https://nawqatrends.wim.usgs.gov/decadal/) represents the center point (centroid) of a network of about 20 to 30 wells. Networks sampled in the first sampling event only are shown in green. There were 67 networks resampled from 2002 to 2012 to assess decadal changes in groundwater quality. Networks sampled from 2012 to 2021 and at least one previous sampling event are shown in orange and trend networks that have not yet been resampled in the third decadal sampling event are shown in blue. Networks sampled in the first and second sampling events but are no longer being sampled are shown in gray.","doi_url":"https://doi.org/10.5066/P9FZT1WO","domain":["Hydrology"],"draft":false,"id":"5fb01c03-388f-471c-9332-406116ecd365","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/628e0f4ed34ef70cdba3f98d?f=__disk__0c%2Fc2%2Fe2%2F0cc2e2680debd07fb6b8c7c7fa2c5e16e8a0e1a9\u0026allowOpen=true"}],"name":"Data from Decadal Change in Groundwater Quality Web Site, 1988-2021","permalink":"/catalog/datasets/5fb01c03-388f-471c-9332-406116ecd365/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1988 - 2021","temporal_frequency":"10 years","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Constituent short name","Long name","Preferred Join Order by Cycle","PCODE","CAL","RECODE COMM COLUMN","Network","STAID","WELL_DEPTH","CY#_DATE_Constituent","CY#_FIELD_Constituent","CY#_LAB_Constituent","CY#_COMM_Constituent","CY#_RMK_Constituent","Network","Network Name","Network Type","Latitude NAD83 (DD)","Longitude NAD83 (DD)","Network","STAID","Latitude NAD83 (DD)","Longitude NAD83 (DD)","Network","Constituent p value","Constituent N","Constituent Annual Difference","Constituent Decadal Difference","Constituent Median Difference","Code_Constituent"],"vars":"Constituent short name; Long name; Preferred Join Order by Cycle; PCODE; CAL; RECODE COMM COLUMN; Network; STAID; WELL_DEPTH; CY#_DATE_Constituent; CY#_FIELD_Constituent; CY#_LAB_Constituent; CY#_COMM_Constituent; CY#_RMK_Constituent; Network; Network Name; Network Type; Latitude NAD83 (DD); Longitude NAD83 (DD); Network; STAID; Latitude NAD83 (DD); Longitude NAD83 (DD); Network; Constituent p value; Constituent N; Constituent Annual Difference; Constituent Decadal Difference; Constituent Median Difference; Code_Constituent","weight":1},{"access":[{"file_format":"SHP; GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/537a6a33e4b0efa8af08154d"}],"access_details":null,"bbox":{"east":-63.21,"north":50.741871378,"south":17.1,"west":-161.31},"citation":"Viger, R.J., 2014, Geospatial Fabric Attribute Tables for PRMS Subsurface Flux Parameters based on Gleeson (Preliminary), U.S. Geological Survey; https://doi.org/10.5066/F7CN71XR","creator":[{"creator_email":"rviger@usgs.gov","creator_name":"Roland Viger"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"\u003cbr /\u003e \n\u003cstrong\u003e(\u003ca href=\"http://wwwbrr.cr.usgs.gov/projects/SW_MoWS/GeospatialFabric.html\"\u003eHyperlink to Official Landing Page for Geospatial Fabric products\u003c/a\u003e)\u003c/strong\u003e \n\u003cbr /\u003e \n\u003cbr /\u003eThis dataset contains a set of attributes describing the \u0026quot;nhru\u0026quot; GIS features (Hydrologic Response Units)in the Geospatial Fabric Features dataset(http://dx.doi.org/doi:10.5066/F7542KMD) that have been developed in support of the USGS PRMS watershed model. These tables are organized according to Geospatial Fabric Region; see the thumbnail of the Geospatial Fabric Features Regions (https://www.sciencebase.gov/catalog/item/535edb4ae4b08e65d60fc837). Each table contains a key field, \u0026quot;hru_id\u0026quot;, that can be used to relate to the nhru feature class in the Geospatial Fabric Feature dataset for the corresponding Region. The methodologies used to derive the individual attributes can be located in the Appendix of the GIS Weasel Users Manual by the name of the attribute, which is the same as the name of the corresponding PRMS parameter, in (Viger and Leavesley, 2007). The metadata for each table within the current container identifies any ancillary datasets used to produce the table fields.For nhru instances that are partially or entirely beyond the borders of the United States, supporting GIS data was generally lacking. Where the value for a field could not be determined, values derived at the border were spatially extended to these areas to support derivation of a value. Users may want to review and modify the field values for these HRUs.Viger, R.J., and Leavesley, G.H., 2007, The GIS Weasel user's manual: U.S. Geological Survey Techniques and Methods, book 6, chap. B4, 201 p.","doi_url":"https://doi.org/10.5066/F7CN71XR","domain":["Hydrology"],"draft":false,"id":"600205af-ec3f-4f9e-8bb4-908726052741","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/537a6a33e4b0efa8af08154d?f=__disk__dd%2F97%2F6e%2Fdd976e4358f08ea674fe14998e50791cff20a8a9\u0026allowOpen=true"}],"name":"Geospatial Fabric Attribute Tables for PRMS Subsurface Flux Parameters based on Gleeson (Preliminary)","permalink":"/catalog/datasets/600205af-ec3f-4f9e-8bb4-908726052741/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; HI; PR","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["hru_id","soil2gw_max","fastcoef_lin","slowcoef_lin","gwflow_coef","dprst_seep_rate_open","dprst_se_rate_closed","dprst_flow_coef","fflux"],"vars":"hru_id; soil2gw_max; fastcoef_lin; slowcoef_lin; gwflow_coef; dprst_seep_rate_open; dprst_se_rate_closed; dprst_flow_coef; fflux","weight":1},{"access":[{"file_format":"TIF; CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/639a11b9d34e0de3a1f0ddde"}],"access_details":null,"bbox":{"east":-65.0679016113281,"north":49.8264669080018,"south":24.7505727720685,"west":-127.382354736328},"citation":"Martin, D.J., Regan, R.S., Haynes, J.V., Read, A.L., Henson, W.R., Stewart, J.S., Brandt, J.T., and Niswonger, R.G., 2023, Irrigation water use reanalysis for the 2000-20 period by HUC12, month, and year for the conterminous United States (ver. 2.0, September 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P9YWR0OJ","creator":[],"creator_project":[{"id":"DJ50UY1","name":"Water Use Model Development"}],"date_created":"5/10/2024","date_updated":"6/3/2026","description":"This data release provides a monthly irrigation water use reanalysis for the period 2000-20 for all U.S. Geological Survey (USGS) Watershed Boundary Dataset of Subwatersheds (Hydrologic Unit Code 12 [HUC12]) in the conterminous United States (CONUS). Results include reference evapotranspiration (ETo), actual evapotranspiration (ETa), irrigated areas, consumptive use, and effective precipitation for each HUC12. ETo and ETa were estimated using the operational Simplified Surface Energy Balance (SSEBop, Senay and others, 2013; Senay and others, 2020) model executed in the OpenET (Melton and others, 2021) web-based application implemented in Google Earth Engine. Results provided by OpenET/SSEBop were summarized to hydrologic response units (HRUs) in the National Hydrologic Model (NHM; Regan and others, 2019) to estimate consumptive use and effective precipitation on irrigated lands. Irrigated lands for the CONUS were provided by the Landsat-based Irrigation Dataset (LANID; Xie and others, 2019) for each year of the reanalysis period. Consumptive use estimates provided by the NHM were disaggregated to HUC12s using area weighted intersections with HRUs and the relative proportion of irrigated lands in each intersected area.\u003cbr\u003eThis data release includes data and source code required to develop the irrigation reanalysis workflow along with the scripts and data required to replicate the output results. The workflow has three main steps that were automated using python scripts: 1) convert daily OpenET/SSEBop results into input for the NHM, 2) run a modified version of the NHM that is an application of the GSFLOW software package (GSFLOW version 2.3) to estimate daily results, and 3) post-process NHM results to monthly, then summarize and disaggregate ETo, ETa, irrigated areas, consumptive use, and effective precipitation to all HUC12s in the CONUS for the period 2000-20.\u003cbr\u003eThe main page of this data release hosts the final outputs of the irrigation water use reanalysis, a user guide, along with a zip file that contains the data and scripts to recreate the workflow.","doi_url":"https://doi.org/10.5066/P9YWR0OJ","domain":["Water Use"],"draft":false,"id":"649711fe-637f-4cc0-9020-09a21e691d88","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/639a11b9d34e0de3a1f0ddde?f=__disk__a8%2F32%2F4b%2Fa8324b05979a75ac35b9181ffbede51f49b9494e\u0026allowOpen=true"}],"name":"Irrigation water use reanalysis for the 2000-20 period by HUC12, month, and year for the conterminous United States (ver. 2.0, September 2024)","permalink":"/catalog/datasets/649711fe-637f-4cc0-9020-09a21e691d88/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2000 - 2020","temporal_frequency":"monthly; annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Monthly AET","Annual AET","Monthly irrigation","Annual irrigation","Monthly effective precipitation","Annual effective precipitation"],"vars":"Monthly AET; Annual AET; Monthly irrigation; Annual irrigation; Monthly effective precipitation; Annual effective precipitation","weight":1},{"access":[{"file_format":"IMG","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5f21cef582cef313ed940043"},{"file_format":"IMG","name":"usgs.gov","url":"https://www.usgs.gov/centers/eros/science/national-land-cover-database"},{"file_format":"IMG","name":"mrlc.gov","url":"https://www.mrlc.gov/data"}],"access_details":null,"bbox":{"east":-63.6722,"north":52.851,"south":21.7423,"west":-130.2328},"citation":"Dewitz, J., and U.S. Geological Survey, 2021, National Land Cover Database (NLCD) 2019 Products (ver. 3.0, February 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P9KZCM54.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The National Land Cover Database (NLCD) provides nationwide data on land cover and land cover change at a 30m resolution with a 16-class legend based on a modified Anderson Level II classification system. NLCD 2019 represents the latest evolution of NLCD land cover products focused on providing innovative land cover and land cover change data for the Nation. NLCD 2019 offers 8 integrated epochs of land cover for years 2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019. Developed classes in these years are directly derived from percent developed impervious surface and include a descriptor label that identifies the type of each impervious surface pixel. The NLCD Land Cover change index combines information from all years of land cover change and provides a simple and comprehensive way to visualize change from all 8 dates of land cover in a single layer. The change index was designed to assist NLCD users to understand complex land cover change with a single product. NLCD 2019 does not yet contain updated products for Alaska, Hawaii and Puerto Rico.","doi_url":null,"domain":["Land Cover"],"draft":false,"id":"6670fc47-62de-4d2a-82cf-524e3b6eb0c7","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5f21cef582cef313ed940043?f=__disk__65%2Fc0%2Fee%2F65c0ee69c1041021dce93cfd950af256d145eb66\u0026allowOpen=true"}],"name":"National Land Cover Database (NLCD) 2019 Products (ver. 3.0, February 2024)","permalink":"/catalog/datasets/6670fc47-62de-4d2a-82cf-524e3b6eb0c7/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS; MRLC","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"2001; 2004; 2006; 2008; 2011; 2013; 2016; 2019","temporal_frequency":"annual","update_detail":"append","update_frequency":"irregular","update_type":"Dynamic","variables":["Land use and land cover","Impervious surface"],"vars":"Land use and land cover; Impervious surface","weight":1},{"access":[{"file_format":"TXT; SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5d6e70e5e4b0c4f70cf635a1"}],"access_details":null,"bbox":{"east":-75.35,"north":37.61,"south":24.43,"west":-89.91},"citation":"Roland, V.L., II, and Hoos, A.B., 2020, SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southeastern United States, 2012 Base Year: U.S. Geological Survey data release, https://doi.org/10.5066/P9A682GW","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The U.S. Geological Survey's (USGS) SPAtially Referenced Regression On Watershed attributes (SPARROW) model was used to aid in the interpretation of monitoring data and simulate streamflow and water-quality conditions in streams across the Southeast Region of the United States. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based water-quality load estimates. This USGS data release includes input and output files associated with 2012 SPARROW simulations of streamflow, total nitrogen, total phosphorus and suspended-sediment load in streams of the Southeast region. Model construction, calibration and results are described in Hoos and Roland (2019, https://doi.org/10.3133/sir20195135).","doi_url":"https://doi.org/10.5066/P9A682GW","domain":["Hydrology"],"draft":false,"id":"684ccf40-27f3-4733-b6bb-58e8a54e52ea","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[{"id":"8e649d48-f8fc-4cc6-a147-5645b54693eb","rel_type":"IsSourceOf"}],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5d6e70e5e4b0c4f70cf635a1?f=__disk__93%2Fba%2F5c%2F93ba5c50c58ced4116ad2e5b9783fc7848ab2cb5\u0026allowOpen=true"},{"name":"Documentation","url":"https://pubs.usgs.gov/publication/sir20195135"}],"name":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southeastern United States, 2012 Base Year","permalink":"/catalog/datasets/684ccf40-27f3-4733-b6bb-58e8a54e52ea/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Southeastern United States","spatial_resolution":"1:100,000","temporal_coverage":"1999 - 2014","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Streamflow","Total nitrogen loads","Total phosphorus loads","Suspended-sediment loads"],"vars":"Streamflow; Total nitrogen loads; Total phosphorus loads; Suspended-sediment loads","weight":1},{"access":[{"file_format":"GRIB","name":"registry.opendata.aws","url":"https://registry.opendata.aws/noaa-gefs-reforecast/"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"NOAA Global Ensemble Forecast System (GEFS) Re-forecast, accessed [YYYY-MM-DD at https://registry.opendata.aws/noaa-gefs-reforecast.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"NOAA has generated a multi-decadal reanalysis and reforecast data set to accompany the next-generation version of its ensemble prediction system, the Global Ensemble Forecast System, version 12 (GEFSv12). Accompanying the real-time forecasts are \"reforecasts\" of the weather, that is, retrospective forecasts spanning the period 2000-2019. These reforecasts are not as numerous as the real-time data; they were generated only once per day, from 00 UTC initial conditions, and only 5 members were provided, with the following exception. Once weekly, an 11-member reforecast was generated, and these extend in lead time to +35 days.","doi_url":null,"domain":["Climate"],"draft":false,"id":"688f75af-2ae3-431e-b55a-47013087a20c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf"}],"name":"Global Ensemble Forecast System Re-forecast version 12 (GEFSv12)","permalink":"/catalog/datasets/688f75af-2ae3-431e-b55a-47013087a20c/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NCEP","spatial_extent":"Global","spatial_resolution":"0.25 degrees","temporal_coverage":"2000 - 2019","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Mean sea-level pressure","Surface pressure","Surface height","Skin temperature","Soil temperature","Volumetric soil moisture content (fraction between wilting and saturation)","Water equivalent of accumulated snow depth","2-meter temperature","2-meter specific humidity","Maximum temperature in last 6-h period or in last 3-h period","Minimum temperature in last 6-h period or in last 3-h period","Surface wind gust","Surface wind stress, u and v components","Surface roughness","Total precipitation sum over the last 6-h period or the last 3-h period","Convective precipitation sum over the last 6-h period or the last 3-h period","Non-convective precipitation sum over the last 6-h period or the last 3-h period","Boundary layer height","Cloud ceiling","Water runoff sum over the last 6-h period or in last 3-h period","Average surface latent heat net flux average in last 6-h period or in last 3-h period","Average surface sensible heat net flux average in last 6-h period or in last 3-h period","Average ground heat net flux average in last 6-h period or in last 3-h period","Convective available potential energy","Convective inhibition","0-3 km Storm relative helicity","Precipitable water","Total ozone","Total cloud cover average in last 6-h period or in last 3-h period","Downward short-wave radiation flux at the surface average in last 6-h period or in last 3-h period","Downward long-wave radiation flux at the surface average in last 6-h period or in last 3-h period","Upward short-wave radiation flux at the surface average in last 6-h period or in last 3-h period","Upward long-wave radiation flux at the surface average in last 6-h period or in last 3-h period","Upward long-wave radiation flux at the top of the atmosphere","Potential vorticity on the 310, 320 and 350K isentropic surfaces","Momentum Flux, U-Component average in last 6-h period or in last 3-h period","Momentum Flux, V-Component average in last 6-h period or in last 3-h period"],"vars":"Mean sea-level pressure; Surface pressure; Surface height; Skin temperature; Soil temperature; Volumetric soil moisture content (fraction between wilting and saturation); Water equivalent of accumulated snow depth; 2-meter temperature; 2-meter specific humidity; Maximum temperature in last 6-h period or in last 3-h period; Minimum temperature in last 6-h period or in last 3-h period; Surface wind gust; Surface wind stress, u and v components; Surface roughness; Total precipitation sum over the last 6-h period or the last 3-h period; Convective precipitation sum over the last 6-h period or the last 3-h period; Non-convective precipitation sum over the last 6-h period or the last 3-h period; Boundary layer height; Cloud ceiling; Water runoff sum over the last 6-h period or in last 3-h period; Average surface latent heat net flux average in last 6-h period or in last 3-h period; Average surface sensible heat net flux average in last 6-h period or in last 3-h period; Average ground heat net flux average in last 6-h period or in last 3-h period; Convective available potential energy; Convective inhibition; 0-3 km Storm relative helicity; Precipitable water; Total ozone; Total cloud cover average in last 6-h period or in last 3-h period; Downward short-wave radiation flux at the surface average in last 6-h period or in last 3-h period; Downward long-wave radiation flux at the surface average in last 6-h period or in last 3-h period; Upward short-wave radiation flux at the surface average in last 6-h period or in last 3-h period; Upward long-wave radiation flux at the surface average in last 6-h period or in last 3-h period; Upward long-wave radiation flux at the top of the atmosphere; Potential vorticity on the 310, 320 and 350K isentropic surfaces; Momentum Flux, U-Component average in last 6-h period or in last 3-h period; Momentum Flux, V-Component average in last 6-h period or in last 3-h period","weight":1},{"access":[{"file_format":"ZARR","name":"hytest-org.github.io","url":"https://hytest-org.github.io/hytest/dataset_access/CONUS404_ACCESS.html"},{"file_format":"NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6372cd09d34ed907bf6c6ab1"},{"file_format":"ZARR","name":"WMA STAC","url":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/conus404"}],"access_details":null,"bbox":{"east":-63.1184,"north":52.898,"south":20.1149,"west":-131.1649},"citation":"Rasmussen, R.M., Chen, F., Liu, C., Ikeda, K., Prein, A., Kim, J., Schneider, T., Dai, A., Gochis, D., Dugger, A., Zhang, Y., Jaye, A., Dudhia, J., He, C., Harrold, M., Xue, L., Chen, S., Newman, A., Dougherty, E., Abolafia-Rozenzweig, R., Lybarger, N., Viger, R., Dunne, K., Rasmussen, K., and Miguez-Macho, G., 2023, CONUS404: Four-kilometer long-term regional hydroclimate reanalysis over the conterminous United States (ver. 3.0, June 2026): U.S. Geological Survey data release, https://doi.org/10.5066/P9PHPK4F.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"CONUS404 is a unique, high-resolution hydro-climate dataset appropriate for forcing hydrological models and conducting meteorological analysis over the contiguous United States. CONUS404, so named because it covers the CONtiguous United States for 40 years at 4-km resolution, was produced by the Weather Research and Forecasting (WRF) Model simulations run by National Center for Atmospheric Research (NCAR) as part of a collaboration with the U.S. Geological Survey (USGS) Water Mission Area.  In fact, CONUS404 includes 43 years of data (water years 1980-2022) and the spatial domain extends beyond the CONUS into Canada and Mexico, thereby capturing transboundary river basins and covering all contributing areas for the CONUS surface waters.\u003cbr\u003eThe CONUS404 dataset, produced using WRF version 3.9.1.1, is the successor to the CONUS1 dataset (Liu and others, 2017) with improved representation of weather and climate conditions in the central United States due to the addition of a shallow groundwater module and several other improvements in the Noah-Multiparameterization (Noah-MP) Land Surface Model.  It also uses a more up-to-date and higher-resolution reanalysis dataset (ERA5; Hersbach and others, 2020) as input and covers a longer period than CONUS1.","doi_url":null,"domain":["Climate"],"draft":false,"id":"68d031e8-4101-4cdf-952a-657396e92a5a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"f930b17a-c6ea-4623-b89d-d7d85ac698aa","rel_type":"IsSourceOf"},{"id":"b697c2e0-919d-40e8-a070-78618f5356d1","rel_type":"IsVariantFormOf"},{"id":"52eb523d-0fb7-4c75-8a35-1953c0054132","rel_type":"IsSourceOf"},{"id":"553e2bbb-00ea-4052-b472-5561943d5dc6","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[{"id":"148a7db7-1175-47aa-8437-5ae39f62b1ce","rel_type":"IsSourceOf"}],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6372cd09d34ed907bf6c6ab1?f=__disk__65%2Ff6%2F74%2F65f674ed33a256648dc244b5d54bb83163fa5954\u0026allowOpen=true"},{"name":"Documentation","url":"https://doi.org/10.1175/BAMS-D-21-0326.1"}],"name":"CONUS404: Four-kilometer long-term regional hydroclimate reanalysis over the conterminous United States (ver. 3.0, June 2026)","permalink":"/catalog/datasets/68d031e8-4101-4cdf-952a-657396e92a5a/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS; NCAR","spatial_extent":"CONUS","spatial_resolution":"4 kilometer","temporal_coverage":"1979 - 2024","temporal_frequency":"hourly; daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Accumulated canopy dew rate","Accumulated canopy precipitation drip rate","Accumulated canopy snow drip rate","Accumulated net evaporation of canopy water","Accumulated net soil evaporation or snowpack sublimation","Accumulated energy influx from soil bottom","Accumulated total evaporation","Accumulated plant transpiration","Accumulated canopy evaporation","Accumulated latent heat flux over bare ground","Accumulated latent heat flux for canopy layer","Accumulated ground latent heat flux below canopy","Accumulated canopy frost","Accumulated refreezing of canopy liquid water","Accumulated heat flux into soil or snowpack for bare ground","Accumulated total ground heat flux into soil or snowpack","Accumulated heat flux into soil or snowpack under canopy","Accumulated ground heat flux","Accumulated upward sensible heat flux at the surface","Accumulated canopy rain interception rate","Accumulated canopy snow interception rate","Accumulated net longwave radiation for bare ground","Accumulated net longwave radiation from canopy","Accumulated net longwave radiation from ground below canopy","Accumulated upward latent heat flux at the surface","Accumulated total latent heat flux","Accumulated downwelling longwave radiation flux at bottom","Accumulated downwelling clear sky longwave radiation flux at bottom","Accumulated longwave downwelling radiation at land surface model","Accumulated downwelling longwave radiation flux at top","Accumulated downwelling clear sky longwave radiation flux at top","Accumulated upwelling longwave radiation flux at bottom","Accumulated upwelling clear sky longwave radiation flux at bottom","Accumulated longwave upwelling radiation at land surface model","Accumulated upwelling longwave radiation flux at top","Accumulated upwelling clear sky longwave radiation flux at top","Accumulated canopy snow melt","Accumulated precipitation advected energy to bare ground","Accumulated precipitation advected energy to below canopy","Accumulated total precipitation heat flux advected to surface","Accumulated precipitation advected energy to vegetation","Accumulated surface ponding from complete pack melt","Accumulated groundwater lateral flow","Accumulated groundwater baseflow","Accumulated liquid precipitation into land surface model","Accumulated rain on snow pack","Accumulated subsurface runoff","Accumulated surface runoff","Accumulated solar radiation absorbed by bare ground","Accumulated solar radiation absorbed by vegetated ground","Accumulated solar radiation absorbed by vegetated fraction","Accumulated sensible heat flux at bare fraction","Accumulated sensible heat flux, canopy to atmosphere","Accumulated total sensible heat flux","Accumulated sensible heat flux from ground below canopy","Accumulated liquid water flux out of bottom of snowpack","Accumulated snowpack frost","Accumulated total liquid water out of the snowpack","Accumulated frozen precipitation into land surface model","Accumulated snowpack sublimation","Accumulated canopy snow sublimation","Accumulated downwelling shortwave radiation flux at bottom","Accumulated downwelling clear sky shortwave radiation flux at bottom","Accumulated shortwave radiation down at land surface model","Accumulated downwelling shortwave radiation flux at top","Accumulated downwelling clear sky shortwave radiation flux at top","Accumulated upwelling shortwave radiation flux at bottom","Accumulated upwelling clear sky shortwave radiation flux at bottom","Accumulated shortwave radiation up at land surface model","Accumulated upwelling shortwave radiation flux at top","Accumulated upwelling clear sky shortwave radiation flux at top","Accumulated canopy rain throughfall","Accumulated canopy snow throughfall","Accumulated transpiration","Background surface albedo","Surface albedo including snow effects","Canopy intercepted ice mass","Canopy intercepted water","2nd order extrapolation constant","2nd order extrapolation constant","2nd order extrapolation constant","Extrapolation constant","Extrapolation constant","Computational grid latitude, south is negative","Cloud fraction","Local cosine of map rotation","Cosine of solar zenith angle","Thickness of soil layers","Coriolis cosine latitude term","Surface emissivity","Coriolis sine latitude term","Upper weight for vertical stretching","Lower weight for vertical stretching","Lowest model pressure into land surface model","Lowest model mixing ratio into land surface model","Lowest model temperature into land surface model","Lowest model wind speed into land surface model","Lowest model height above ground level (AGL) into land surface model","Downward long wave flux at ground surface","Accumulated total grid scale graupel","Accumulated graupel water equivalent","Ground heat flux","Maximum column-integrated graupel","Accumulated total grid scale hail","Maximum hail diameter entire column","Maximum hail diameter","Upward heat flux at the surface","Single-column model (SCM) ideal surface sensible heat flux","Single-column model (SCM) ideal surface sensible heat flux tendency","Terrain Height","Array to hold seed for restart, RAND_PERT2","Array to hold seed for restart, RAND_PERT4","Array to hold seed for restart, RAND_PERT3","Array to hold seed for restart, RAND_PERT","Array to hold seed for restart, stochastic kinetic-energy backscatter scheme (SKEBS)","Array to hold seed for restart, stochastically perturbed physics tendencies (SPPT)","Dominant soil category","Daily maximum water vapor mixing ratio at 2 meters","Daily mean water vapor mixing ratio at 2 meters","Daily minimum water vapor mixing ratio at 2 meters","Daily standard deviation of water vapor mixing ratio at 2 meters","Daily maximum cumulus precipitation flux","Daily mean cumulus precipitation flux","Daily standard deviation of cumulus precipitation flux","Daily maximum grid scale precipitation flux","Daily mean grid scale precipitation flux","Daily standard deviation of grid scale precipitation","Daily maximum skin temperature","Daily mean skin temperature","Daily minimum skin temperature","Daily standard deviation of skin temperature","Daily maximum wind speed at 10 meters","Daily mean wind speed at 10 meters","Daily standard deviation of wind speed at 10 meters","Daily maximum temperature at 2 meters","Daily mean temperature at 2 meters","Daily minimum temperature at 2 meters","Daily standard deviation of temperature at 2 meters","Time of daily maximum water vapor mixing ratio at 2 meters","Time of daily minimum water vapor mixing ratio at 2 meters","Time of daily maximum cumulus precipitation flux","Time of daily maximum grid scale precipitation flux","Time of daily maximum skin temperature","Time of daily minimum skin temperature","Time of daily maximum wind speed at 10 meters","Time of daily maximum temperature at 2 meters","Time of daily minimum temperature at 2 meters","Model time","Daily maximum U-component of wind at 10 meters with respect to model grid","Daily mean U-component of wind at 10 meters with respect to model grid","Daily standard deviation of U-component of wind at 10 meters with respect to model grid","Daily maximum V-component of wind at 10 meters with respect to model grid","Daily mean V-component of wind at 10 meters with respect to model grid","Daily standard deviation of V-component of wind at 10 meters with respect to model grid","Daily maximum water vapor mixing ratio at 2 meters","Daily mean water vapor mixing ratio at 2 meters","Daily minimum water vapor mixing ratio at 2 meters","Daily standard deviation of water vapor mixing ratio at 2 meters","Daily maximum cumulus precipitation flux","Daily mean cumulus precipitation flux","Daily standard deviation of cumulus precipitation flux","Daily maximum grid scale precipitation flux","Daily mean grid scale precipitation flux","Daily standard deviation of grid scale precipitation","Daily maximum skin temperature","Daily mean skin temperature","Daily minimum skin temperature","Daily standard deviation of skin temperature","Daily maximum wind speed at 10 meters","Daily mean wind speed at 10 meters","Daily standard deviation of wind speed at 10 meters","Daily maximum temperature at 2 meters","Daily mean temperature at 2 meters","Daily minimum temperature at 2 meters","Daily standard deviation of temperature at 2 meters","Time of daily maximum water vapor mixing ratio at 2 meters","Time of daily minimum water vapor mixing ratio at 2 meters","Time of daily maximum cumulus precipitation flux","Time of daily maximum grid scale precipitation flux","Time of daily maximum skin temperature","Time of daily minimum skin temperature","Time of daily maximum wind speed at 10 meters","Time of daily maximum temperature at 2 meters","Time of daily minimum temperature at 2 meters","Model time in string format","Daily maximum U-component of wind at 10 meters with respect to model grid","Daily mean U-component of wind at 10 meters with respect to model grid","Daily standard deviation of U-component of wind at 10 meters with respect to model grid","Daily maximum V-component of wind at 10 meters with respect to model grid","Daily mean V-component of wind at 10 meters with respect to model grid","Daily standard deviation of V-component of wind at 10 meters with respect to model grid"],"vars":"Accumulated canopy dew rate; Accumulated canopy precipitation drip rate; Accumulated canopy snow drip rate; Accumulated net evaporation of canopy water; Accumulated net soil evaporation or snowpack sublimation; Accumulated energy influx from soil bottom; Accumulated total evaporation; Accumulated plant transpiration; Accumulated canopy evaporation; Accumulated latent heat flux over bare ground; Accumulated latent heat flux for canopy layer; Accumulated ground latent heat flux below canopy; Accumulated canopy frost; Accumulated refreezing of canopy liquid water; Accumulated heat flux into soil or snowpack for bare ground; Accumulated total ground heat flux into soil or snowpack; Accumulated heat flux into soil or snowpack under canopy; Accumulated ground heat flux; Accumulated upward sensible heat flux at the surface; Accumulated canopy rain interception rate; Accumulated canopy snow interception rate; Accumulated net longwave radiation for bare ground; Accumulated net longwave radiation from canopy; Accumulated net longwave radiation from ground below canopy; Accumulated upward latent heat flux at the surface; Accumulated total latent heat flux; Accumulated downwelling longwave radiation flux at bottom; Accumulated downwelling clear sky longwave radiation flux at bottom; Accumulated longwave downwelling radiation at land surface model ; Accumulated downwelling longwave radiation flux at top; Accumulated downwelling clear sky longwave radiation flux at top; Accumulated upwelling longwave radiation flux at bottom; Accumulated upwelling clear sky longwave radiation flux at bottom; Accumulated longwave upwelling radiation at land surface model; Accumulated upwelling longwave radiation flux at top; Accumulated upwelling clear sky longwave radiation flux at top; Accumulated canopy snow melt; Accumulated precipitation advected energy to bare ground; Accumulated precipitation advected energy to below canopy; Accumulated total precipitation heat flux advected to surface; Accumulated precipitation advected energy to vegetation; Accumulated surface ponding from complete pack melt; Accumulated groundwater lateral flow; Accumulated groundwater baseflow; Accumulated liquid precipitation into land surface model; Accumulated rain on snow pack; Accumulated subsurface runoff; Accumulated surface runoff; Accumulated solar radiation absorbed by bare ground; Accumulated solar radiation absorbed by vegetated ground; Accumulated solar radiation absorbed by vegetated fraction; Accumulated sensible heat flux at bare fraction; Accumulated sensible heat flux, canopy to atmosphere; Accumulated total sensible heat flux; Accumulated sensible heat flux from ground below canopy; Accumulated liquid water flux out of bottom of snowpack; Accumulated snowpack frost; Accumulated total liquid water out of the snowpack; Accumulated frozen precipitation into land surface model; Accumulated snowpack sublimation; Accumulated canopy snow sublimation; Accumulated downwelling shortwave radiation flux at bottom; Accumulated downwelling clear sky shortwave radiation flux at bottom; Accumulated shortwave radiation down at land surface model; Accumulated downwelling shortwave radiation flux at top; Accumulated downwelling clear sky shortwave radiation flux at top; Accumulated upwelling shortwave radiation flux at bottom; Accumulated upwelling clear sky shortwave radiation flux at bottom; Accumulated shortwave radiation up at land surface model; Accumulated upwelling shortwave radiation flux at top; Accumulated upwelling clear sky shortwave radiation flux at top; Accumulated canopy rain throughfall; Accumulated canopy snow throughfall; Accumulated transpiration; Background surface albedo; Surface albedo including snow effects; Canopy intercepted ice mass; Canopy intercepted water; 2nd order extrapolation constant; 2nd order extrapolation constant; 2nd order extrapolation constant; Extrapolation constant; Extrapolation constant; Computational grid latitude, south is negative; Cloud fraction; Local cosine of map rotation; Cosine of solar zenith angle; Thickness of soil layers; Coriolis cosine latitude term; Surface emissivity; Coriolis sine latitude term; Upper weight for vertical stretching; Lower weight for vertical stretching; Lowest model pressure into land surface model; Lowest model mixing ratio into land surface model; Lowest model temperature into land surface model; Lowest model wind speed into land surface model; Lowest model height above ground level (AGL) into land surface model; Downward long wave flux at ground surface; Accumulated total grid scale graupel; Accumulated graupel water equivalent; Ground heat flux; Maximum column-integrated graupel; Accumulated total grid scale hail; Maximum hail diameter entire column; Maximum hail diameter; Upward heat flux at the surface; Single-column model (SCM) ideal surface sensible heat flux; Single-column model (SCM) ideal surface sensible heat flux tendency; Terrain Height; Array to hold seed for restart, RAND_PERT2; Array to hold seed for restart, RAND_PERT4; Array to hold seed for restart, RAND_PERT3; Array to hold seed for restart, RAND_PERT; Array to hold seed for restart, stochastic kinetic-energy backscatter scheme (SKEBS); Array to hold seed for restart, stochastically perturbed physics tendencies (SPPT); Dominant soil category; Daily maximum water vapor mixing ratio at 2 meters; Daily mean water vapor mixing ratio at 2 meters; Daily minimum water vapor mixing ratio at 2 meters; Daily standard deviation of water vapor mixing ratio at 2 meters; Daily maximum cumulus precipitation flux; Daily mean cumulus precipitation flux; Daily standard deviation of cumulus precipitation flux; Daily maximum grid scale precipitation flux; Daily mean grid scale precipitation flux; Daily standard deviation of grid scale precipitation; Daily maximum skin temperature; Daily mean skin temperature; Daily minimum skin temperature; Daily standard deviation of skin temperature; Daily maximum wind speed at 10 meters; Daily mean wind speed at 10 meters; Daily standard deviation of wind speed at 10 meters; Daily maximum temperature at 2 meters; Daily mean temperature at 2 meters; Daily minimum temperature at 2 meters; Daily standard deviation of temperature at 2 meters; Time of daily maximum water vapor mixing ratio at 2 meters; Time of daily minimum water vapor mixing ratio at 2 meters; Time of daily maximum cumulus precipitation flux; Time of daily maximum grid scale precipitation flux; Time of daily maximum skin temperature; Time of daily minimum skin temperature; Time of daily maximum wind speed at 10 meters; Time of daily maximum temperature at 2 meters; Time of daily minimum temperature at 2 meters; Model time; Daily maximum U-component of wind at 10 meters with respect to model grid; Daily mean U-component of wind at 10 meters with respect to model grid; Daily standard deviation of U-component of wind at 10 meters with respect to model grid; Daily maximum V-component of wind at 10 meters with respect to model grid; Daily mean V-component of wind at 10 meters with respect to model grid; Daily standard deviation of V-component of wind at 10 meters with respect to model grid; Daily maximum water vapor mixing ratio at 2 meters; Daily mean water vapor mixing ratio at 2 meters; Daily minimum water vapor mixing ratio at 2 meters; Daily standard deviation of water vapor mixing ratio at 2 meters; Daily maximum cumulus precipitation flux; Daily mean cumulus precipitation flux; Daily standard deviation of cumulus precipitation flux; Daily maximum grid scale precipitation flux; Daily mean grid scale precipitation flux; Daily standard deviation of grid scale precipitation; Daily maximum skin temperature; Daily mean skin temperature; Daily minimum skin temperature; Daily standard deviation of skin temperature; Daily maximum wind speed at 10 meters; Daily mean wind speed at 10 meters; Daily standard deviation of wind speed at 10 meters; Daily maximum temperature at 2 meters; Daily mean temperature at 2 meters; Daily minimum temperature at 2 meters; Daily standard deviation of temperature at 2 meters; Time of daily maximum water vapor mixing ratio at 2 meters; Time of daily minimum water vapor mixing ratio at 2 meters; Time of daily maximum cumulus precipitation flux; Time of daily maximum grid scale precipitation flux; Time of daily maximum skin temperature; Time of daily minimum skin temperature; Time of daily maximum wind speed at 10 meters; Time of daily maximum temperature at 2 meters; Time of daily minimum temperature at 2 meters; Model time in string format; Daily maximum U-component of wind at 10 meters with respect to model grid; Daily mean U-component of wind at 10 meters with respect to model grid; Daily standard deviation of U-component of wind at 10 meters with respect to model grid; Daily maximum V-component of wind at 10 meters with respect to model grid; Daily mean V-component of wind at 10 meters with respect to model grid; Daily standard deviation of V-component of wind at 10 meters with respect to model grid","weight":1},{"access":[{"file_format":"GDB","name":"nrcs.usda.gov","url":"https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625"}],"access_details":null,"bbox":{"east":-65,"north":50,"south":24,"west":-127},"citation":"Soil Survey Staff, Gridded National Soil Survey Geographic (gNATSGO) Database for the Conterminous United States: United States Department of Agriculture, Natural Resources Conservation Service, accessed [YYYY-MM-DD] at https://nrcs.app.box.com/v/soils.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil \u0026 Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.","doi_url":null,"domain":["Soils"],"draft":false,"id":"69832e09-bf7b-4481-af06-4186c023a38f","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.nrcs.usda.gov/resources/data-and-reports/gridded-national-soil-survey-geographic-database-gnatsgo"}],"name":"Gridded National Soil Survey Geographic (gNATSGO) Database for the Conterminous United States","permalink":"/catalog/datasets/69832e09-bf7b-4481-af06-4186c023a38f/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NRCS","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"2023","temporal_frequency":"NA","update_detail":"append and modify","update_frequency":"irregular","update_type":"Dynamic","variables":["SSURGO soil characteristics"],"vars":"SSURGO soil characteristics","weight":1},{"access":[{"file_format":"GDB; GPKG","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/61794fc2d34ea58c3c6f9f69"}],"access_details":null,"bbox":{"east":180,"north":71.312,"south":-15.3861,"west":-180},"citation":"U.S. Geological Survey (USGS) Gap Analysis Project (GAP), 2022, Protected Areas Database of the United States (PAD-US) 3.0 (ver. 2.0, March 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9Q9LQ4B.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://ngda-cadastre-geoplatform.hub.arcgis.com/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling best available data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries.\u003cbr\u003e\n\u003cbr\u003e\nAn additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of best available spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1).\u003cbr\u003e\n\u003cbr\u003e\nFederal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/federal-lands-workgroup ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the \"State Name\" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency.\u0026nbsp;5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field.\u003cbr\u003e\n\u003cbr\u003e\nState updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-manual .\u003cbr\u003e\n\u003cbr\u003e\nA version history of PAD-US updates is summarized below (See https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-history for more information):\u003cbr\u003e\n1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov).\u003cbr\u003e\n2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov).\u003cbr\u003e\n3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov).\u003cbr\u003e\n4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD\u003cbr\u003e\n5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ\u003cbr\u003e\n6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE\u003cbr\u003e\n7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT\u003cbr\u003e\n8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B\u003cbr\u003e\n\u003cbr\u003e\nComparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.","doi_url":"https://doi.org/10.5066/P9Q9LQ4B","domain":["Land Cover"],"draft":false,"id":"69ce5a60-19fa-4478-b84f-c7ea2bd0fd26","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/61794fc2d34ea58c3c6f9f69?f=__disk__c3%2Fcc%2F46%2Fc3cc46c55aa6c746feb21be143a587b26732c706\u0026allowOpen=true"}],"name":"Protected Areas Database of the United States (PAD-US) 3.0 (ver. 2.0, March 2023)","permalink":"/catalog/datasets/69ce5a60-19fa-4478-b84f-c7ea2bd0fd26/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2005 - 2022","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["OBJECTID","Shape","FeatClass","Category","Own_Type","Own_Name","Loc_Own","Mang_Type","Mang_Name","Loc_Mang","Des_Tp","Loc_Ds","Unit_Nm","Loc_Nm","State_Nm","Agg_Src","GIS_Src","Src_Date","GIS_Acres","GAP_Sts","GAPCdSrc","GAPCdDt","IUCN_Cat","IUCNCtSrc","IUCNCtDt","Pub_Access","Access_Src","Access_Dt","Date_Est","Source_PAID","WDPA_Cd","EHoldTyp","EsmtHldr","Comments","Shape_Length","Shape_Area"],"vars":"OBJECTID; Shape; FeatClass; Category; Own_Type; Own_Name; Loc_Own; Mang_Type; Mang_Name; Loc_Mang; Des_Tp; Loc_Ds; Unit_Nm; Loc_Nm; State_Nm; Agg_Src; GIS_Src; Src_Date; GIS_Acres; GAP_Sts; GAPCdSrc; GAPCdDt; IUCN_Cat; IUCNCtSrc; IUCNCtDt; Pub_Access; Access_Src; Access_Dt; Date_Est; Source_PAID; WDPA_Cd; EHoldTyp; EsmtHldr; Comments; Shape_Length; Shape_Area","weight":1},{"access":[{"file_format":"TXT; SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5f8f1f1282ce06b040efc90e"}],"access_details":null,"bbox":{"east":-93.2148742675781,"north":42.5854442573849,"south":25.6019022611157,"west":-121.076202392578},"citation":"Miller, O.L., Wise, D.R., and Anning, D.W., 2020, SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southwestern United States, 2012 Base Year (ver. 2.0, October 2020): U.S. Geological Survey data release, https://doi.org/10.5066/P94EKLPP","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The U.S. Geological Survey's (USGS) SPAtially Referenced Regression On Watershed attributes (SPARROW) model was used to aid in the interpretation of monitoring data and simulate streamflow and water-quality conditions in streams across the Southwestern Region of the Unites States. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based water-quality load estimates. This USGS data release includes input and output files associated with 2012 SPARROW simulations of streamflow, total nitrogen, total phosphorus and suspended-sediment load in streams of the Southwestern region. This dataset was revised in October 2020. Variables were added to the sw_sparrow_model_input.txt file that had previously been calculated in SW SPARROW model control files. A coding error was fixed in the suspended sediment model control files, resulting in updates to sw_sparrow_model_output_SS.txt. Duplicated COMIDs were removed from all output files. Suspended sediment values were reported in metric tons per year. Model construction, calibration and results are described in Wise, Anning, and Miller (2019, https://doi.org/10.3133/sir20195106).","doi_url":"https://doi.org/10.5066/P94EKLPP","domain":["Hydrology"],"draft":false,"id":"6b02c121-2a09-46fb-a8a9-8fbb66990332","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[{"id":"8e649d48-f8fc-4cc6-a147-5645b54693eb","rel_type":"IsSourceOf"}],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5f8f1f1282ce06b040efc90e?f=__disk__44%2Ff6%2F74%2F44f674b54b2fa571191a597c8dfae0923893d3d3\u0026allowOpen=true"},{"name":"Documentation","url":"https://pubs.usgs.gov/publication/sir20195106"}],"name":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southwestern United States, 2012 Base Year (ver. 2.0, October 2020)","permalink":"/catalog/datasets/6b02c121-2a09-46fb-a8a9-8fbb66990332/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Southwestern United States","spatial_resolution":"1:100,000","temporal_coverage":"1999 - 2014","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Streamflow","Total nitrogen loads","Total phosphorus loads","Suspended-sediment loads"],"vars":"Streamflow; Total nitrogen loads; Total phosphorus loads; Suspended-sediment loads","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/61d4c9e9d34ed79293fe91b4"}],"access_details":null,"bbox":{"east":-65.2148,"north":49.838,"south":24.0465,"west":-125.1563},"citation":"Hodson, T.O., Foks, S.S., Dugger, A.L., Dunne, K.A., Miles, K.A., Over, T.M., Penn, C.A., Saxe, S.W., Simeone, C.E., Towler, E., and Viger, R.J., 2022, Daily streamflow performance benchmark defined by D-score (v0.1) for the National Water Model (v2.1) at benchmark streamflow locations: U.S. Geological Survey data release, https://doi.org/10.5066/P9MJDNRL","creator":[],"creator_project":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"date_created":"5/2/2024","date_updated":"6/3/2026","description":"This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Water Model (NWM) Retrospective version 2.1 computed at streamgage benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow (aggregated from an hourly timestep) versus observed daily mean streamflow. Using those errors, the D-score performance benchmark computes the mean squared logarithmic error (MSLE), then decomposes the overall MSLE into orthogonal components such as bias, distribution, and sequence (Hodson and others, 2021). For easier interpretation, the MSLE components can be passed through a scoring function as described in Hodson and others (2021).\u003cbr\u003eReferences:\u003cbr\u003e\nFoks, S.S., Towler, E., Hodson, T.O., Bock, A.R., Dickinson, J.E., Dugger, A.L., Dunne, K.A., Essaid, H.I., Miles, K.A., Over, T.M., Penn, C.A., Russell, A.M., Saxe, S.W., and Simeone, C.E., 2022, Streamflow benchmark locations for conterminous United States (cobalt gages): U.S. Geological Survey data release, https://doi.org/10.5066/P972P42Z.\u003cbr\u003eHodson, T.O., Over, T.M., and Foks, S.S., 2021. Mean squared error, deconstructed. Journal of Advances in Modeling Earth Systems, 13, e2021MS002681. https://doi.org/10.1029/2021MS002681","doi_url":"https://doi.org/10.5066/P9MJDNRL","domain":["Hydrology"],"draft":false,"id":"6b9aadbe-a1ce-4595-b048-227dfd60c719","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/61d4c9e9d34ed79293fe91b4?f=__disk__57%2Fef%2Fd5%2F57efd5219e43ecc82da885504400450051a5e56b\u0026allowOpen=true"}],"name":"Streamflow decomp suite benchmark results (NWM v2.1): Daily streamflow performance benchmark defined by D-score (v0.1) for the National Water Model Retrospective (v2.1) at benchmark streamflow locations","permalink":"/catalog/datasets/6b9aadbe-a1ce-4595-b048-227dfd60c719/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"unknown","temporal_coverage":"1983 - 2016","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["site_no","overall","trend","seasonality","variability","bias","distribution","sequence","winter","spring","summer","fall","low","below_avg","above_avg","high"],"vars":"site_no; overall; trend; seasonality; variability; bias; distribution; sequence; winter; spring; summer; fall; low; below_avg; above_avg; high","weight":1},{"access":[{"file_format":"NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/612e264ed34e40dd9c091228"}],"access_details":null,"bbox":{"east":-65.6096,"north":49.4331,"south":24.638,"west":-125.9466},"citation":"Blodgett, D.L., 2022, National Water Model V2.1 retrospective for selected NWIS gage locations, (1979-2020): U.S. Geological Survey data release, https://doi.org/10.5066/P9K5BEJG.","creator":[{"creator_email":"dblodgett@usgs.gov","creator_name":"David L Blodgett"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset contains modeled hourly streamflow in cubic meters per second at each of about eighteen thousand selected operational and water-quality stream gage locations. It was assembled from publicly available retrospective V2.1 National Water Model outputs (See NWM Retrospective source info). The streamflow variable was extracted from model output files and the data were reshaped to optimize read performance. The stream gage locations were derived from several ongoing USGS project gages for evaluation of streamflow, water quality, and real-time monitoring, however only National Water Model identifiers and NHDPlusV2.1 catchment outlet locations (as contained in the National Water Model output files) are used to identify model results. Relationships between U.S. Geological Survey National Water Information Service gages and National Water Model prediction locations were not reviewed for release at the time of publication of this data. Please contact the author for up-to-date information. All processing resources used for data reformatting and extraction can be found in this repository: https://code.usgs.gov/water/nwm_subset. This supporting code also contains Docker images that are capable of processing real-time National Water Model outputs into similar formats as this dataset and provide data services via the THREDDS Data Server. The retrospective is available in one NetCDF file. These data conform to the NetCDF-CF Discrete Sampling Geometry conventions. The retrospective data are available from public cloud data outlets (such as: https://noaa-nwm-retrospective-2-1-pds.s3.amazonaws.com/index.html). More information on these data is available from the Office of Water Prediction National Water Model page here: https://water.noaa.gov/about/nwm. Additional release notes are available here: https://www.weather.gov/media/notification/pdf2/scn20-119nwm_v2_1aad.pdf. This pdf has been archived with this data release.","doi_url":"https://doi.org/10.5066/P9K5BEJG","domain":["Hydrology"],"draft":false,"id":"6ccf64a2-9562-4cf9-ac9d-553cb181afa4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[{"id":"6389d98c-7de8-4dc7-938c-f98da1063455","rel_type":"IsSourceOf"}],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/612e264ed34e40dd9c091228?f=__disk__3f%2Fbd%2F7e%2F3fbd7e2a52ef81ae9015c16c138605515c2e6bfb\u0026allowOpen=true"}],"name":"National Water Model V2.1 retrospective for selected NWIS gage locations, (1979-2020)","permalink":"/catalog/datasets/6ccf64a2-9562-4cf9-ac9d-553cb181afa4/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; HI; PR; VI","spatial_resolution":"250 meter; 1 kilometer","temporal_coverage":"1979 - 2020","temporal_frequency":"hourly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["NWM v2.1 output"],"vars":"NWM v2.1 output","weight":1},{"access":[{"file_format":"CSV; XLSX","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5f63be9a82ce38aaa23b0739"}],"access_details":null,"bbox":{"east":179.775939941213,"north":71.3514404291142,"south":18.9254779809854,"west":-178.216552734375},"citation":"Harris, M.A., and Diehl, T.H., 2019, Water withdrawal and consumption estimates for thermoelectric power plants in the United States, 2015 (ver. 1.2, July 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P9V0T04B.","creator":[],"creator_project":[{"id":"DJ50UY1","name":"Water Use Model Development"}],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset presents water withdrawal estimates, consumption estimates, and associated information for 1,122 water-using, utility scale thermoelectric power plants in the United States for 2015. The U.S. Geological Survey (USGS) developed models to estimate thermoelectric water use based on linked heat-and-water budgets, including thermodynamically plausible ranges of minimum and maximum withdrawal and consumption, to provide a consistent method for water-use estimation across the fleet of U.S. thermoelectric plants. Historically, thermoelectric water withdrawal and consumption has been estimated by the Department of Energy's Energy Information Administration (EIA) based on surveys of plant operator-reported data and the USGS's 5-year water-use reports based on compiling data from State water agencies, plant operators, and the EIA. The USGS models provide independent estimates from plant operator-reported data. The total estimated withdrawal for 2015 was about 103 billion gallons per day (Bgal/d), and total estimated consumption was about 2.7 Bgal/d. Estimates presented in this dataset are rounded to three significant digits.  This data release supports the findings published in Harris and Diehl (2019).","doi_url":"https://doi.org/10.5066/P9V0T04B","domain":["Hydrology"],"draft":false,"id":"6d104ce6-c598-452c-8480-fd396d224f5e","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5f63be9a82ce38aaa23b0739?f=__disk__8b%2Fa6%2F15%2F8ba615f560b1298b6bf70009784cd616061bf86b\u0026allowOpen=true"}],"name":"Water withdrawal and consumption estimates for thermoelectric power plants in the United States, 2015 (ver. 1.2, July 2024)","permalink":"/catalog/datasets/6d104ce6-c598-452c-8480-fd396d224f5e/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"unknown","temporal_coverage":"2015","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["EIA_PLANT_ID","PLANT_NAME","COUNTY","STATE","NAME_OF_WATER_SOURCE","COOLING_TYPE","GENERATION_TYPE","WATER_SOURCE_CODE","WATER_TYPE_CODE","WITHDRAWAL","CONSUMPTION","MIN_WITHDRAWAL","MAX_WITHDRAWAL","MIN_CONSUMPTION","MAX_CONSUMPTION","NET_GENERATION","MODEL_TYPE","PERCENT_CD_ALLOCATION","ELEVATION","POND_AREA"],"vars":"EIA_PLANT_ID; PLANT_NAME; COUNTY; STATE; NAME_OF_WATER_SOURCE; COOLING_TYPE; GENERATION_TYPE; WATER_SOURCE_CODE; WATER_TYPE_CODE; WITHDRAWAL; CONSUMPTION; MIN_WITHDRAWAL; MAX_WITHDRAWAL; MIN_CONSUMPTION; MAX_CONSUMPTION; NET_GENERATION; MODEL_TYPE; PERCENT_CD_ALLOCATION; ELEVATION; POND_AREA","weight":1},{"access":[{"file_format":"HDF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-mcd12q1-061"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Friedl, M. and Sulla-Menashe, D., 2022, MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061 [Data set]: NASA EOSDIS Land Processes Distributed Active Archive Center, accessed [YYYY-MM-DD] at https://doi.org/10.5067/MODIS/MCD12Q1.061","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6.1 data product provides global land cover types at yearly intervals (2001-2022). The MCD12Q1 Version 6.1 data product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. Land cover types are derived from the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), Leaf Area Index (LAI), BIOME-Biogeochemical Cycles (BGC), and Plant Functional Types (PFT) classification schemes. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes. Additional land cover property assessment layers are provided by the Food and Agriculture Organization (FAO) Land Cover Classification System (LCCS) for land cover, land use, and surface hydrology.\u003cbr\u003eLayers for Land Cover Type 1-5, Land Cover Property 1-3, Land Cover Property Assessment 1-3, Land Cover Quality Control (QC), and a Land Water Mask are provided in each MCD12Q1 Version 6.1 Hierarchical Data Format 4 (HDF4) file.","doi_url":"https://doi.org/10.5067/MODIS/MCD12Q1.061","domain":["Land Cover"],"draft":false,"id":"6d85c6e4-6fc4-4f5a-a8ab-3a8bc3b42033","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/documents/1409/MCD12_User_Guide_V61.pdf"}],"name":"MCD12Q1 v061: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061","permalink":"/catalog/datasets/6d85c6e4-6fc4-4f5a-a8ab-3a8bc3b42033/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"500 meter","temporal_coverage":"2001 - 2022","temporal_frequency":"annual","update_detail":"append","update_frequency":"annual","update_type":"Dynamic","variables":["Land use and land cover"],"vars":"Land use and land cover","weight":1},{"access":[{"file_format":"SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5e541969e4b0ff554f753113"}],"access_details":null,"bbox":{"east":-65.35,"north":70.139,"south":17.957,"west":-165.881},"citation":"U.S. Geological Survey, USDA Forest Service, and Nelson, K., 2021, Monitoring Trends in Burn Severity (ver. 12.0, April 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P9IED7RZ.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (including wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer includes a vector point shapefile and a burned area boundary shapefile of the location of all currently inventoried fires occurring between calendar year 1984 and 2024 for CONUS, Alaska, Hawaii, and Puerto Rico. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires were not discernable from available imagery.\u0026nbsp;\u003cbr\u003e\n\u003cbr\u003e\nFirst posted - January 1, 2021\u003cbr\u003e\nRevised -\u0026nbsp; June 28, 2022\u0026nbsp;(version 2.0)\u003cbr\u003e\nRevised -\u0026nbsp; January 6, 2023 (version 3.0)\u003cbr\u003e\nRevised - \u0026nbsp;March 31, 2023 (version 4.0)\u003cbr\u003e\nRevised -\u0026nbsp; August 3, 2023 (version 5.0)\u003cbr\u003e\nRevised -\u0026nbsp; October 26, 2023 (version 6.0)\u003cbr\u003e\nRevised -\u0026nbsp; January 23, 2024 (version 7.0)\u003cbr\u003e\nRevised - April 29, 2024 (version 8.0)\u003cbr\u003e\nRevised - August 22, 2024 (version 9.0)\u003cbr\u003e\nRevised - October 29, 2024 (version 10.0)\u003cbr\u003e\nRevised - January 31, 2025 (version 11.0)\u003cbr\u003e\nRevised - April 22, 2025 (version 12.0)","doi_url":"https://doi.org/10.5066/P9IED7RZ","domain":["Land Cover"],"draft":false,"id":"6dca7311-5aa6-4a41-93b9-23d4f6e02f00","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5e541969e4b0ff554f753113?f=__disk__f6%2F5b%2F90%2Ff65b90fd2ef0347bd69b83af8670aedb7f91e4fc\u0026allowOpen=true"}],"name":"Monitoring Trends in Burn Severity (ver. 12.0, April 2025)","permalink":"/catalog/datasets/6dca7311-5aa6-4a41-93b9-23d4f6e02f00/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; AK; HI; PR","spatial_resolution":"1:50,000","temporal_coverage":"1984 - 2024","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["FID","Shape","Event_ID","irwinID","Incid_Name","Incid_Type","Map_ID","Map_Prog","Asmnt_Type","BurnBndAc","BurnBndLat","BurnBndLon","Ig_Date","Pre_ID","Post_ID","Perim_ID","dNBR_offst","dNBR_stdDv","NoData_T","IncGreen_T","Low_T","Mod_T","High_T","Comment"],"vars":"FID; Shape; Event_ID; irwinID; Incid_Name; Incid_Type; Map_ID; Map_Prog; Asmnt_Type; BurnBndAc; BurnBndLat; BurnBndLon; Ig_Date; Pre_ID; Post_ID; Perim_ID; dNBR_offst; dNBR_stdDv; NoData_T; IncGreen_T; Low_T; Mod_T; High_T; Comment","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6447f553d34ee8d4aded3b53"}],"bbox":{"east":-63.1667,"north":54.8088,"south":21.0821,"west":-129.7712},"citation":"Wieczorek, M.E., Staub, L.E., and Wnuk, K.C., Hafen, K.C., 2023, Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portions of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins (ver. 2.0, July 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P98IG8LO.","creator":[{"creator_email":"mewieczo@usgs.gov","creator_name":"Michael E Wieczorek"}],"creator_project":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"date_created":"2/25/2026","date_updated":"6/3/2026","description":"These tabular data sets represent the average daily soil moisture water content (kg/m^2) for four different soil layers processed from North American Land Data Assimilation System (NLDAS-2) data (Xia and others, 2012) for the period of record 1980 through 2020 and compiled for three spatial components: 1) select United States Geological Survey stream gage basins (Staub and Wieczorek, 2023), 2) individual reach flowline catchments of the Upper Colorado (ucol) portion of the Geospatial Fabric for the National Hydrologic Model, version 1.1 (nhgfv11, Bock and others, 2020 ), and 3) the upstream watersheds of each individual nhgfv11 flowline catchments. Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021). Upstream watershed values for each reach catchment were computed using the published python software package Xstrm (Wieferich and others). The following mean daily soil moisture water content layers were processed: 0-10 centimeters, 10-40 centimeters, and 40-100 centimeters.","doi_url":"https://doi.org/10.5066/P98IG8LO","domain":["Climate","Hydrology"],"draft":false,"id":"6e7fbb30-4ebb-4024-b25c-9eb1f1a7f7a3","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6447f553d34ee8d4aded3b53?f=__disk__0e%2F07%2Fa3%2F0e07a3e2159fac59c0b7c738b6745d7c162d080a\u0026allowOpen=true"}],"name":"Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portion of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins: Daily Climate Metrics Derived from NLDAS2, 1980 - 2020","permalink":"/catalog/datasets/6e7fbb30-4ebb-4024-b25c-9eb1f1a7f7a3/","project_use_history":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"project_using":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1980 - 2020","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Date","station_no","mean soil moisture"],"vars":"Date; station_no; mean soil moisture","weight":1},{"access":[{"file_format":"HDF","name":"gmao.gsfc.nasa.gov","url":"https://gmao.gsfc.nasa.gov/gmao-products/merra/"}],"access_details":"","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Rienecker, M., Suarez, M., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M., Schubert, S., Takacs, L., Kim, G., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C., Reichle, R., Robertson, F., Ruddick, A., Sienkiewicz, M. and Woollen, J., 2011, MERRA: NASA's Modern-Era Retrospective Analysis for Research and Applications, Journal of Climate 24(14) pp. 3624-3648, Accessed [YYYY-MM-DD], https://journals.ametsoc.org/view/journals/clim/24/14/jcli-d-11-00015.1.xml.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"NOTE: The MERRA reanalysis dataset production was completed on February 29, 2016. MERRA-2, which incorporates various advances is currently in production.\u003cbr\u003eThe Modern-Era Retrospective analysis for Research and Applications (MERRA) dataset was released in 2009. It is based on a version of the GEOS-5 atmospheric data assimilation system that was frozen in 2008. MERRA data span the period 1979 through February 2016 and were produced on a 0.5 degree by 0.66 degree grid with 72 layers. MERRA was used to drive stand-alone reanalyses of the land surface (MERRA-Land).\u003cbr\u003eNOTE: The MERRA reanalysis dataset production was completed on February 29, 2016. MERRA-2, which incorporates various advances is currently in production.","doi_url":null,"domain":["Climate"],"draft":false,"id":"6fc82e7e-a9ad-47c3-84ca-3d643ba56c84","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://gmao.gsfc.nasa.gov/media/gmao_products/IqPeIBhIWLGrweILfVPj9Vmdoc/MERRA_File_Specification.pdf"}],"name":"MERRA: Modern-Era Retrospective analysis for Research and Applications","permalink":"/catalog/datasets/6fc82e7e-a9ad-47c3-84ca-3d643ba56c84/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"0.5 degrees by 0.667 degrees","temporal_coverage":"1979 - 2016","temporal_frequency":"hourly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Precipitation, Water vapor"],"vars":"Precipitation, Water vapor","weight":1},{"access":[{"file_format":"HDF","name":"nsidc.org","url":"https://nsidc.org/data/VNP10A1F/versions/1"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Riggs, G., D. K. Hall, and M. O. Roman, 2019, VIIRS/NPP CGF Snow Cover Daily L3 Global 375m SIN Grid, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed [YYYY-MM-DD], https://doi.org/10.5067/VIIRS/VNP10A1F.001","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data set contains daily 'cloud-free' snow cover produced from the VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid, Version 1 snow cover product. A cloud-gap-filled algorithm is utilized to replace 'cloud-covered' pixels with 'cloud-free pixels' for the purpose of estimating the snow cover that may exist under current cloud cover. The data are provided daily and mapped to a 375 m sinusoidal grid.","doi_url":"https://doi.org/10.5067/VIIRS/VNP10A1F.001","domain":["Snow"],"draft":false,"id":"6fe1f3ac-0417-4285-8965-694012bce07c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"VIIRS/NPP CGF Snow Cover Daily L3 Global 375m SIN Grid, Version 1","permalink":"/catalog/datasets/6fe1f3ac-0417-4285-8965-694012bce07c/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NSIDC","spatial_extent":"Global","spatial_resolution":"375 meter","temporal_coverage":"2012 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Snow cover"],"vars":"Snow cover","weight":1},{"access":[{"file_format":"WS; GDB; SHP; KML","name":"fema.gov","url":"https://www.fema.gov/flood-maps/national-flood-hazard-layer"}],"access_details":null,"bbox":{"east":-65,"north":72,"south":18,"west":-180},"citation":null,"creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The National Flood Hazard Layer (NFHL) dataset represents the current effective flood data for the country, where maps have been modernized. It is a compilation of effective Flood Insurance Rate Map (FIRM) databases and Letters of Map Change (LOMCs). The NFHL is updated as studies go effective.","doi_url":null,"domain":["Hydrology","Stream Characteristics"],"draft":false,"id":"716d5b10-3cd7-42f9-9802-14125ba2cc0b","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://hazards.fema.gov/femaportal/wps/portal/NFHLWMS"}],"name":"National Flood Hazard Layer","permalink":"/catalog/datasets/716d5b10-3cd7-42f9-9802-14125ba2cc0b/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"FEMA","spatial_extent":"CONUS; AK; HI","spatial_resolution":"1:50,000; 1:250,000","temporal_coverage":"2025","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["FEMA flood hazard zones","River mile markers","Cross-section and coastal transect locations","Letter of Map Revision boundaries and case numbers","FIRM boundaries/labels/effective dates","Coastal Barrier Resources System and Otherwise Protected Area units","Community boundaries and names","Levees","Hydraulic and flood control structures","Profile and coastal transect baselines","Limit of Moderate Wave Action","full list of variables in source metadata"],"vars":"FEMA flood hazard zones; River mile markers; Cross-section and coastal transect locations; Letter of Map Revision boundaries and case numbers; FIRM boundaries/labels/effective dates; Coastal Barrier Resources System and Otherwise Protected Area units; Community boundaries and names; Levees; Hydraulic and flood control structures; Profile and coastal transect baselines; Limit of Moderate Wave Action; full list of variables in source metadata","weight":1},{"access":[{"file_format":"GRIB","name":"registry.opendata.aws","url":"https://registry.opendata.aws/noaa-gefs/"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"NOAA Global Ensemble Forecast System (GEFS) was accessed on [YYYY-MM-DD] from https://registry.opendata.aws/noaa-gefs","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Global Ensemble Forecast System (GEFS), previously known as the GFS Global ENSemble (GENS), is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental Prediction (NCEP) started the GEFS to address the nature of uncertainty in weather observations, which is used to initialize weather forecast models. The GEFS attempts to quantify the amount of uncertainty in a forecast by generating an ensemble of multiple forecasts, each minutely different, or perturbed, from the original observations. With global coverage, GEFS is produced four times a day with weather forecasts going out to 16 days.","doi_url":null,"domain":["Climate"],"draft":false,"id":"7522efa9-4a91-49c2-b37b-3820cce00b2b","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://registry.opendata.aws/noaa-gefs/"}],"name":"Global Ensemble Forecast System version 12 (GEFSv12)","permalink":"/catalog/datasets/7522efa9-4a91-49c2-b37b-3820cce00b2b/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NOAA","spatial_extent":"Global","spatial_resolution":"0.25 degrees","temporal_coverage":"1992 - Present","temporal_frequency":"6 hours","update_detail":"append","update_frequency":"6 hours","update_type":"Dynamic","variables":["Global ensemble forecast parameters"],"vars":"Global ensemble forecast parameters","weight":1},{"access":[{"file_format":"TIF","name":"coast.noaa.gov","url":"https://coast.noaa.gov/digitalcoast/data/ccapregional.html"},{"file_format":"TIF","name":"coast.noaa.gov","url":"https://coast.noaa.gov/htdata/raster1/landcover/bulkdownload/30m_lc/"}],"access_details":null,"bbox":{"east":-65,"north":50,"south":24,"west":-127},"citation":"National Oceanic and Atmospheric Administration, Office for Coastal Management, [Name of Data Set], Coastal Change Analysis Program (C-CAP) Regional Land Cover, Charleston, SC: NOAA Office for Coastal Management, accessed [YYYY-MM-DD] at www.coast.noaa.gov/htdata/raster1/landcover/bulkdownload/30m_lc/","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The NOAA Coastal Change Analysis Program (C-CAP) produces national standardized land cover and change products for the coastal regions of the U.S. C-CAP products inventory coastal intertidal areas, wetlands, and adjacent uplands with the goal of monitoring changes in these habitats, on a one-to-five year repeat cycle. Coincides with NLCD releases.","doi_url":null,"domain":["Land Cover"],"draft":false,"id":"754b64d7-ee33-4141-8108-68c6416fa52a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.fisheries.noaa.gov/inport/item/48336"}],"name":"C-CAP 2016","permalink":"/catalog/datasets/754b64d7-ee33-4141-8108-68c6416fa52a/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NOAA","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"1975; 1985; 1992; 1996; 2001; 2006; 2010; 2016","temporal_frequency":"NA","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Background","Unclassified (Cloud, Shadow, etc)","High Intensity Developed","Medium Intensity Developed","Low Intensity Developed","Developed Open Space","Cultivated Land","Pasture/Hay","Grassland","Deciduous Forest","Evergreen Forest","Mixed Forest","Scrub/Shrub","Palustrine Forested Wetland","Palustrine Scrub/Shrub Wetland","Palustrine Emergent Wetland","Estuarine Forested Wetland","Estuarine Scrub/Shrub Wetland","Estuarine Emergent Wetland","Unconsolidated Shore","Bare Land","Open Water","Palustrine Aquatic Bed","Estuarine Aquatic Bed","Tundra","Snow/Ice"],"vars":"Background; Unclassified (Cloud, Shadow, etc); High Intensity Developed; Medium Intensity Developed; Low Intensity Developed; Developed Open Space; Cultivated Land; Pasture/Hay; Grassland; Deciduous Forest; Evergreen Forest; Mixed Forest; Scrub/Shrub; Palustrine Forested Wetland; Palustrine Scrub/Shrub Wetland; Palustrine Emergent Wetland; Estuarine Forested Wetland; Estuarine Scrub/Shrub Wetland; Estuarine Emergent Wetland; Unconsolidated Shore; Bare Land; Open Water; Palustrine Aquatic Bed; Estuarine Aquatic Bed; Tundra; Snow/Ice","weight":1},{"access":[{"file_format":"SHP","name":"kilthub.cmu.edu","url":"https://kilthub.cmu.edu/articles/dataset/Interbasin_Transfers_in_the_United_States/11979696/1"}],"access_details":null,"bbox":{"east":-56,"north":72,"south":22,"west":-180},"citation":"Dickson, K., Dzombak, D. (2020) Interbasin Transfers in the United States. Carnegie Mellon University. https://doi.org/10.1184/R1/11979696.​","creator":[],"creator_project":[],"date_created":"5/1/2025","date_updated":"6/3/2026","description":"The objectives of this work were to quantify the number of interbasin transfers (IBTs) that exist in the United States (U.S.) and to examine the distribution of IBTs and potential causes associated with any observed clustering of IBTs. To build a 2016 inventory of IBTs in the U.S., and to identify where they most commonly occur, the USGS National Hydrography Database (NHD) was utilized in conjunction with the Watershed Boundary Dataset (WBD). Transfers across HUC6 basin boundaries were considered interbasin. Geographical information analysis with the NHD and WBD databases revealed that there are a total of 2,161 man-made waterways crossing HUC6 basin boundaries in the U.S. and are published in shapefile format.","doi_url":"https://doi.org/10.1184/R1/11979696","domain":["Hydrology","Infrastructure","Water Use"],"draft":false,"id":"757c17fa-192a-437e-a0e0-c011ef166aad","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://onlinelibrary.wiley.com/doi/full/10.1111/1752-1688.12561"}],"name":"Interbasin Transfers in the United States","permalink":"/catalog/datasets/757c17fa-192a-437e-a0e0-c011ef166aad/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"United States","spatial_resolution":"1:24,000","temporal_coverage":"2016","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["NHDPlusHR attributes","OBJECTID","PermanentID","FDate","GNIS_Name","LengthKM","FType","FCode"],"vars":"NHDPlusHR attributes; OBJECTID; PermanentID; FDate; GNIS_Name; LengthKM; FType; FCode","weight":1},{"access":[{"file_format":"CSV; SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/64386385d34ee8d4addf0d60"}],"access_details":null,"bbox":{"east":-62,"north":72,"south":15,"west":-180},"citation":"Ritchie, A.B., Rosenberry, D.O., Marcelli, M.F., and Kelley, R.L., 2023, Compilation of the salient characteristics of numerical groundwater-flow and solute- and heat-transport models published or developed by the U.S. Geological Survey for regions in the U.S. and its territories and commonwealths, 1970 through 2022: U.S. Geological Survey data release, https://doi.org/10.5066/P9W13X0O.","creator":[],"creator_project":[],"date_created":"5/5/2024","date_updated":"6/3/2026","description":"The U.S. Geological Survey (USGS) National Extent Hydrogeologic Framework for National Water Census (NEHF) project is a multi-year effort (2022 through 2025) that will compile existing assets (approaches, data, software, etc.), develop a strategic plan, and implement an operational framework that is dynamic and multi-scale. Within the USGS, numerical groundwater-flow and solute- and heat-transport models have been created for a variety of purposes that include water-resource assessments, contaminant-transport evaluations, and water-management planning. These models are often supported by hydrogeologic-framework studies that describe the surface and subsurface distribution of geologic materials and their hydrologic properties. This digital data release was developed as part of the NEHF project to compile, in tabular and spatial form, information on the salient characteristics of numerical groundwater-flow and solute- and heat-transport models published or developed by the USGS for regions in the U.S. and its territories and commonwealths from the date of the earliest published model, 1970, through 2022.","doi_url":"https://doi.org/10.5066/P9W13X0O","domain":["Hydrogeology","Hydrology"],"draft":false,"id":"79d152e6-8a4e-40b7-b1fd-4a1c126d6ecc","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/64386385d34ee8d4addf0d60?f=__disk__5c%2F99%2F3e%2F5c993e831645067af02c567099b488cfa25932bd\u0026allowOpen=true"}],"name":"Compilation of the salient characteristics of numerical groundwater-flow and solute- and heat-transport models published or developed by the U.S. Geological Survey for regions in the U.S. and its territories and commonwealths, 1970 through 2022","permalink":"/catalog/datasets/79d152e6-8a4e-40b7-b1fd-4a1c126d6ecc/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"varies","temporal_coverage":"1970 - 2022","temporal_frequency":"NA","update_detail":"append and modify","update_frequency":"unknown","update_type":"Dynamic","variables":["Short_name","Model_name","Common_identifer","Multiple_PAs","Multiple_SHRs","Authors","Author_contact","Model_report_title","Model_report_link","Cooperator","Cooperator_short","Year_model_published","Model_polygon_source","Model_polygon_area","Primary_state","Model_software","States","Purpose","Steady_state","Historic_start_year","Historic_end_year","Hypothetical_start_year","Hypothetical_end_year","Dimension","Areal_extent","Minimum_vertical_extent","Maximum_vertical_extent","Minimum_cell_area","Maximum_cell_area","Median_cell_area","Number_of_rows","Number_of_columns","Number_of_active_cells","Number_of_layers","Model_statistics_source","Model_files_link","Framework_report_title","Framework_report_link","Model_to_hydrogeologic_framework","Heterogeneity_description","Faulting_fracturing_description","Model_bottom_description","Other_versions_model_report_title","Other_versions_model_report_link","Other_versions_description","Other_versions_model_files_link"],"vars":"Short_name; Model_name; Common_identifer; Multiple_PAs; Multiple_SHRs; Authors; Author_contact; Model_report_title; Model_report_link; Cooperator; Cooperator_short; Year_model_published; Model_polygon_source; Model_polygon_area; Primary_state; Model_software; States; Purpose; Steady_state; Historic_start_year; Historic_end_year; Hypothetical_start_year; Hypothetical_end_year; Dimension; Areal_extent; Minimum_vertical_extent; Maximum_vertical_extent; Minimum_cell_area; Maximum_cell_area; Median_cell_area; Number_of_rows; Number_of_columns; Number_of_active_cells; Number_of_layers; Model_statistics_source; Model_files_link; Framework_report_title; Framework_report_link; Model_to_hydrogeologic_framework; Heterogeneity_description; Faulting_fracturing_description; Model_bottom_description; Other_versions_model_report_title; Other_versions_model_report_link; Other_versions_description; Other_versions_model_files_link","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5d826e4de4b0c4f70d059137"}],"access_details":null,"bbox":{"east":-66.709,"north":49.0088,"south":24.4868,"west":-125.5957},"citation":"LaFontaine, J.H., Hay, L.E., and Riley, J.R., 2023, Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS), 1950-2010, Maurer Calibration: U.S. Geological Survey data release, https://doi.org/10.5066/P9CVHLMB.","creator":[],"creator_project":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"date_created":"8/2/2024","date_updated":"6/3/2026","description":"This data release contains inputs for and outputs from hydrologic simulations for the conterminous United States (CONUS) using the Precipitation Runoff Modeling System (PRMS) version 5.1.0 and the USGS National Hydrologic Model infrastructure (NHM, Regan and others, 2018). Historical simulations using the Maurer forcings (Maurer and others, 2002) were conducted for the period 1950-2010. This metadata record documents the simulation output files for simulations ran using the static parameters file. The output files are aggregated at the HUC4 level and are grouped and downloadable by HUC2 hydrologic region. Each zip folder contains identical information, just for a different region and set of hydrologic response units (HRUs) and stream segments. The specific zip folder contents include model unit information for the HRUs and stream segments, HRU-based output variables of daily actual evapotranspiration (hru_actet), runoff (hru_outflow), precipitation (hru_ppt), potential evapotranspiration (potet), minimum air temperature (tminf) and maximum air temperature (tmaxf), and the segment-based output variable of streamflow (seg_outflow).","doi_url":"https://doi.org/10.5066/P9CVHLMB","domain":["Hydrology","Climate"],"draft":false,"id":"7a17fa5b-5205-4f9d-9a89-4fce7be17e64","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/62014ab3d34e622189da06b8?f=__disk__e9%2F1c%2F08%2Fe91c08617eed770910cc5423e3816623abb83261\u0026allowOpen=true"}],"name":"Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS), 1950-2010, Maurer Calibration","permalink":"/catalog/datasets/7a17fa5b-5205-4f9d-9a89-4fce7be17e64/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1950 - 2010","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["daily actual evapotranspiration (hru_actet)","runoff (hru_outflow)","precipitation (hru_ppt)","potential evapotranspiration (potet)","minimum air temperature (tminf)","maximum air temperature (tmaxf)","streamflow (seg_outflow)"],"vars":"daily actual evapotranspiration (hru_actet); runoff (hru_outflow); precipitation (hru_ppt); potential evapotranspiration (potet); minimum air temperature (tminf); maximum air temperature (tmaxf); streamflow (seg_outflow)","weight":1},{"access":[{"file_format":"CSV; KML; SHP; JSON; GDB","name":"gbp-blm-egis.hub.arcgis.com","url":"https://gbp-blm-egis.hub.arcgis.com/pages/aim"}],"access_details":null,"bbox":{"east":-100,"north":52,"south":24,"west":-127},"citation":"U.S. Department of Interior Bureau of Land Management, BLM - Assessment, Inventory, and Monitoring (AIM) Lotic Indicators Calculated Dataset. Denver: Bureau of Land Management. [March 2025]. Accessed [Year Month Day]. https://gbp-blm-egis.hub.arcgis.com/pages/aim","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic sampling design, standard core indicators and methods, electronic data capture and management, and integration with remote sensing. Attributes include the BLM aquatic core indicators: pH, conductivity, temperature, pool depth, length, frequency, streambed particles sizes, bank stability and cover, floodplain connectivity, large woody debris, macroinvertebrate biological integrity, ocular estimates of vegetative type, cover, and structure and canopy cover. In addition, the contingent indicators of total nitrogen and phosphorous, turbidity, bank angle, thalweg depth profile and quantitative vegetation estimates (see Entity/Attribute Section for exact details on attributes). Data were collected and managed by BLM Field Offices, BLM Districts, and/or affiliated field crews with support from the BLM National Operations Center. Data are stored in a centralized database (AquADat) at the BLM National Operations Center.","doi_url":null,"domain":["Stream Characteristics"],"draft":false,"id":"7a468721-0ea4-4f07-8715-bcb9c050de28","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Assessment, Inventory, and Monitoring (AIM) - Stream and River resources","permalink":"/catalog/datasets/7a468721-0ea4-4f07-8715-bcb9c050de28/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"BLM","spatial_extent":"Western United States","spatial_resolution":"unknown","temporal_coverage":"2013 - 2020","temporal_frequency":"annual","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["BankfullDepthAvg","VegComplexityUnderstoryGround","AH_PerenForbCover","AH_AlgaeCover","PctNativeWoodySpecies","GapCover_51_100","PctEquisetumSpecies","PctNoxiousWoodySpecies","SppInv_Tree_Pct","RH_InvasivePlants","EntrenchmentRiffle1","FH_VagrLichenCover","LgWoodInChanFreq","RH_BioticIntegrity","TopReachLongitude","InstreamHabitatComplexity","HGMClass","BankfullWidthAvg","FH_TotalLitterThatchCover","SagebrushShape_All_Predominant","FieldStatus","StreamOrder","LgWoodAboveChanFreq","BLM_AdminState","Hgt_PerenForb_Avg","BankfullHeightAvg","AH_AnnualCover","OriginalDesign","AH_HydroFACCover","PctPoolTailFinesLessThan6mm","OE_MMI_ModelApplicability","Hgt_TallPerenGrass_Avg","RH_ReprodCapabilityPeren","SppInv_Perennial_Pct","ChannelIncision","WetlandIndicatorRegion","DateLoadedInDb","Hgt_PerenGrassForb_Avg","RH_Gullies","TotalFoliarCover","AH_WoodyLitterCover","WaterWithdrawals","PoolCount","H_HummockWidth_Avg","NumSpp_PreferredForb","RH_CommentsHF","FH_NoxPerenGrassCover","Hgt_Herbaceous_Cnt","ProtocolReachLength","RH_LitterAmount","FH_NoxAnnForbCover","PointID","FH_AlgaeCover","AH_PerenForbGrassCover","RH_CommentsBI","ExpectedInvertRichness","AH_NonNoxAnnForbCover","Spp_PreferredForb","ObservedInvertRichness","OriginalStratum","WS_Rhizomatous_PctQuad","Hgt_Sagebrush_Avg","AH_SaltCrustCover","DateEstablished","PctFinesLessThan6mm","SagebrushShape_Live_SpreadCount","ProtocolVersion","FH_EmbLitterCover","FH_NonNoxAnnGrassCover","PctPoolTailFinesLessThan2mm","AH_ShortPerenGrassCover","FH_NoxSubShrubCover","VisitType","Design","SoilStability_Unprotected","AH_NoxPerenGrassCover","TotalPhosphorous","BottomReachLongitude","RH_Compaction","SampledMidLongitude","PurposeFlag","WS_HgtClass6_PctQuad","Longitude_NAD83","EvaluationID","BenchDepthAvg","GapCover_200_plus","ResPoolDepthAvg","AH_SagebrushCover","RH_WaterFlowPatterns","LgWoodInChanVol","WS_DominantHgtClass","SiteName","LgWoodAboveChanVol","StateCode","AH_NoxPerenForbGrassCover","FH_DepSoilCover","HumanInfluence","WS_Mature_Cnt","BareSoilCover","ProjectName","SppInv_HydroFAC_Pct","SampledMidLatitude","SamplingApproach","OE_MMI_ModelUsed","AH_NoxAnnGrassCover","FH_DuffCover","DateVisited","Hgt_AllLitterThatch_Avg","Hgt_HerbLitter_Avg","FH_NonNoxShrubCover","BareOrganicMaterialCover","PctBankOverheadCover","PctBankStable","PctOverheadCover","H_Hummock_Pct","MMI_Macroinvertebrate","Hgt_HerbLitter_Cnt","PlotID","FH_NonNoxTreeCover","PctSlope","WS_HgtClass4_PctQuad","RH_HydrologicFunction","EntrenchmentRiffle2","Hgt_NonSagebrushShrub_Avg","WS_HgtClass2_PctQuad","H_HummockHgt_Avg","PctSedgeRushSpecies","VegComplexityWoody","FH_SagebrushCover","Hgt_Woody_Cnt","ThalwegDepthAvg","Hgt_WoodyLitter_Cnt","Spp_TallPerenGrass","SppInv_Annual_Pct","Hgt_DecidLitter_Avg","RH_CommentsSS","FH_NonNoxSucculentCover","ThalwegDepthCV","SagebrushShape_All_SpreadCount","FieldEvalDate","PlotKey","PctBankCoveredMIM","FH_RockCover","Hgt_Shrub_Avg","WS_Rhizomatous_Cnt","TurbidityAvg","PointSelectionType","Spp_ShortPerenGrass","EcoregionStreamSize","AH_NonNoxAnnForbGrassCover","D84","FH_NonNoxPerenGrassCover","PctFinesLessThan2mm","H_Hummock_Cnt","DesignFlag","WQ_Cnt","FH_LichenCover","Hgt_DecidLitter_Cnt","AH_GraminoidCover","AH_PreferredForbCover","EcotypeAlaska","AH_HydrophyteCover","OE_Macroinvertebrate","AH_NativeCover","Purpose","State","MacroinvertebrateCount","AH_RockCover","Hgt_Sagebrush_Live_Avg","PctPools","AH_NonNoxSubShrubCover","AH_NoxCover","Spp_Sagebrush","SoilStability_All","AH_PerenGrassCover","StreamName","WS_Young_Pct","CowardinAttribute","AH_SagebrushCover_Live","WS_Young_Cnt","InstantTemp","BeaverFlowMod","EcolSiteName","SppInv_Nonnative_Pct","Hgt_NonNoxPerenGrass_Avg","SppInv_Graminoid_Pct","FH_NoxShrubCover","Hgt_Woody_Avg","LgWoodInChanCount","SagebrushShape_Live_Predominant","InvasiveInvertSpecies","PctBankCoveredStableMIM","AH_NonNoxCover","PlotLayout","SppInv_Shrub_Pct","NumSpp_NoxPlant","VegComplexity","PctBankCoveredStableOld","BenchHeightAvg","AH_NoxTreeCover","AugTempAvg","RH_PlantCommunityComp","SagebrushShape_All_ColumnCount","RH_SoilSurfLossDeg","LgWoodAboveChanCount","WettedWidthAvg","AH_NoxShrubCover","SppInv_Hydrophyte_Pct","RH_SoilSurfResisErosion","AH_TallPerenGrassCover","GapCover_25_plus","EvaluationID_OLD","pH","LatitudeWGS84","GapCover_101_200","WS_Seedling_Cnt","Hgt_Herbaceous_Avg","NumSpp_NonNoxPlant","WetlandType","WS_Mature_Pct","PctBanksUndercut","AH_MossCover","AH_TotalLitterCover","SppInv_Forb_Pct","BottomReachLatitude","H_HummockVegCover_Avg","AH_NonnativeCover","AH_NoxiousCover","FH_WaterCover","AH_NonNoxPerenForbCover","GreenlineVegComposition","FloodplainConnectivity","FieldOffice","WS_Seedling_Pct","Project","PrimaryKey","WS_HgtClass1_PctQuad","PctBankCoveredOld","RH_DeadDyingPlantParts","AH_NonSagebrushShrubCover","SagebrushShape_Live_ColumnCount","TotalNitrogen","SideChannels","LongitudeWGS84","FH_WoodyLitterCover","SppInv_CValue_Avg","Hgt_NoxPerenGrass_Avg","BankAngleAvg","ViewOBJECTID","Hgt_AllLitterThatch_Cnt","AH_HerbLitterCover","FH_NoxPerenForbCover","AH_PerennialCover","AH_WaterCover","FH_NoxAnnGrassCover","Latitude_NAD83","Sinuosity","FH_NonNoxAnnForbCover","SpeciesState","GapCover_25_50","WS_HgtClass5_PctQuad","RH_PedestalsTerracettes","BeaverSigns","SpecificConductance","AH_ForbCover","AH_NonNoxPerenForbGrassCover","PoolFreq","Hgt_PerenGrass_Avg","Elevation_m","AH_NonNoxAnnGrassCover","TopReachLatitude","AdminState","AH_LichenCover","RH_FuncSructGroup","D16","FH_TotalLitterCover","RH_SoilSiteStability","PctDry","Hgt_ShortPerenGrass_Avg","FH_HerbLitterCover","CoreSubset","FH_NonNoxSubShrubCover","DBKey","AH_NonNoxSucculentCover","AH_NonNoxPerenGrassCover","Hgt_Forb_Avg","SoilStability_Protected","AH_AnnualGraminoidCover","AH_NoxPerenForbCover","AH_AnnGrassCover","H_HummockTroughWidth_Avg","County","AH_NonNoxTreeCover","FH_MossCover","Hgt_Grass_Avg","AH_BasalCover","WS_WoodySpp_PctQuad","RH_BareGround","FH_SaltCrustCover","AH_GrassCover","FH_CyanobacteriaCover","RH_WindScouredAreas","AH_NoxSucculentCover","GeometricMeanParticleDiam","SppInv_Noxious_Cnt","WS_WoodyHgtClass_Cnt","Hgt_PerenForbGrass_Avg","SppInv_Richness_Cnt","AH_NoxAnnForbCover","AH_NoxSubShrubCover","RH_LitterMovement","AH_NoxAnnForbGrassCover","RH_Rills","FH_NoxTreeCover","SppInv_Native_Pct","H_HummockSlope_Avg","District","RH_AnnualProd","Hgt_Water_Cnt","D50","AH_TreeCover","WettedWidthWithBarAvg","FH_NonNoxPerenForbCover","AH_TotalLitterThatchCover","RecordID","AH_NonNoxShrubCover","Spp_Nox","AH_ShrubCover","FH_NoxSucculentCover","Hgt_Water_Avg","ProtocolType","Hgt_WoodyLitter_Avg","WS_HgtClass3_PctQuad","EcologicalSiteId","PctNoxiousHerbSpecies"],"vars":"BankfullDepthAvg; VegComplexityUnderstoryGround; AH_PerenForbCover; AH_AlgaeCover; PctNativeWoodySpecies; GapCover_51_100; PctEquisetumSpecies; PctNoxiousWoodySpecies; SppInv_Tree_Pct; RH_InvasivePlants; EntrenchmentRiffle1; FH_VagrLichenCover; LgWoodInChanFreq; RH_BioticIntegrity; TopReachLongitude; InstreamHabitatComplexity; HGMClass; BankfullWidthAvg; FH_TotalLitterThatchCover; SagebrushShape_All_Predominant; FieldStatus; StreamOrder; LgWoodAboveChanFreq; BLM_AdminState; Hgt_PerenForb_Avg; BankfullHeightAvg; AH_AnnualCover; OriginalDesign; AH_HydroFACCover; PctPoolTailFinesLessThan6mm; OE_MMI_ModelApplicability; Hgt_TallPerenGrass_Avg; RH_ReprodCapabilityPeren; SppInv_Perennial_Pct; ChannelIncision; WetlandIndicatorRegion; DateLoadedInDb; Hgt_PerenGrassForb_Avg; RH_Gullies; TotalFoliarCover; AH_WoodyLitterCover; WaterWithdrawals; PoolCount; H_HummockWidth_Avg; NumSpp_PreferredForb; RH_CommentsHF; FH_NoxPerenGrassCover; Hgt_Herbaceous_Cnt; ProtocolReachLength; RH_LitterAmount; FH_NoxAnnForbCover; PointID; FH_AlgaeCover; AH_PerenForbGrassCover; RH_CommentsBI; ExpectedInvertRichness; AH_NonNoxAnnForbCover; Spp_PreferredForb; ObservedInvertRichness; OriginalStratum; WS_Rhizomatous_PctQuad; Hgt_Sagebrush_Avg; AH_SaltCrustCover; DateEstablished; PctFinesLessThan6mm; SagebrushShape_Live_SpreadCount; ProtocolVersion; FH_EmbLitterCover; FH_NonNoxAnnGrassCover; PctPoolTailFinesLessThan2mm; AH_ShortPerenGrassCover; FH_NoxSubShrubCover; VisitType; Design; SoilStability_Unprotected; AH_NoxPerenGrassCover; TotalPhosphorous; BottomReachLongitude; RH_Compaction; SampledMidLongitude; PurposeFlag; WS_HgtClass6_PctQuad; Longitude_NAD83; EvaluationID; BenchDepthAvg; GapCover_200_plus; ResPoolDepthAvg; AH_SagebrushCover; RH_WaterFlowPatterns; LgWoodInChanVol; WS_DominantHgtClass; SiteName; LgWoodAboveChanVol; StateCode; AH_NoxPerenForbGrassCover; FH_DepSoilCover; HumanInfluence; WS_Mature_Cnt; BareSoilCover; ProjectName; SppInv_HydroFAC_Pct; SampledMidLatitude; SamplingApproach; OE_MMI_ModelUsed; AH_NoxAnnGrassCover; FH_DuffCover; DateVisited; Hgt_AllLitterThatch_Avg; Hgt_HerbLitter_Avg; FH_NonNoxShrubCover; BareOrganicMaterialCover; PctBankOverheadCover; PctBankStable; PctOverheadCover; H_Hummock_Pct; MMI_Macroinvertebrate; Hgt_HerbLitter_Cnt; PlotID; FH_NonNoxTreeCover; PctSlope; WS_HgtClass4_PctQuad; RH_HydrologicFunction; EntrenchmentRiffle2; Hgt_NonSagebrushShrub_Avg; WS_HgtClass2_PctQuad; H_HummockHgt_Avg; PctSedgeRushSpecies; VegComplexityWoody; FH_SagebrushCover; Hgt_Woody_Cnt; ThalwegDepthAvg; Hgt_WoodyLitter_Cnt; Spp_TallPerenGrass; SppInv_Annual_Pct; Hgt_DecidLitter_Avg; RH_CommentsSS; FH_NonNoxSucculentCover; ThalwegDepthCV; SagebrushShape_All_SpreadCount; FieldEvalDate; PlotKey; PctBankCoveredMIM; FH_RockCover; Hgt_Shrub_Avg; WS_Rhizomatous_Cnt; TurbidityAvg; PointSelectionType; Spp_ShortPerenGrass; EcoregionStreamSize; AH_NonNoxAnnForbGrassCover; D84; FH_NonNoxPerenGrassCover; PctFinesLessThan2mm; H_Hummock_Cnt; DesignFlag; WQ_Cnt; FH_LichenCover; Hgt_DecidLitter_Cnt; AH_GraminoidCover; AH_PreferredForbCover; EcotypeAlaska; AH_HydrophyteCover; OE_Macroinvertebrate; AH_NativeCover; Purpose; State; MacroinvertebrateCount; AH_RockCover; Hgt_Sagebrush_Live_Avg; PctPools; AH_NonNoxSubShrubCover; AH_NoxCover; Spp_Sagebrush; SoilStability_All; AH_PerenGrassCover; StreamName; WS_Young_Pct; CowardinAttribute; AH_SagebrushCover_Live; WS_Young_Cnt; InstantTemp; BeaverFlowMod; EcolSiteName; SppInv_Nonnative_Pct; Hgt_NonNoxPerenGrass_Avg; SppInv_Graminoid_Pct; FH_NoxShrubCover; Hgt_Woody_Avg; LgWoodInChanCount; SagebrushShape_Live_Predominant; InvasiveInvertSpecies; PctBankCoveredStableMIM; AH_NonNoxCover; PlotLayout; SppInv_Shrub_Pct; NumSpp_NoxPlant; VegComplexity; PctBankCoveredStableOld; BenchHeightAvg; AH_NoxTreeCover; AugTempAvg; RH_PlantCommunityComp; SagebrushShape_All_ColumnCount; RH_SoilSurfLossDeg; LgWoodAboveChanCount; WettedWidthAvg; AH_NoxShrubCover; SppInv_Hydrophyte_Pct; RH_SoilSurfResisErosion; AH_TallPerenGrassCover; GapCover_25_plus; EvaluationID_OLD; pH; LatitudeWGS84; GapCover_101_200; WS_Seedling_Cnt; Hgt_Herbaceous_Avg; NumSpp_NonNoxPlant; WetlandType; WS_Mature_Pct; PctBanksUndercut; AH_MossCover; AH_TotalLitterCover; SppInv_Forb_Pct; BottomReachLatitude; H_HummockVegCover_Avg; AH_NonnativeCover; AH_NoxiousCover; FH_WaterCover; AH_NonNoxPerenForbCover; GreenlineVegComposition; FloodplainConnectivity; FieldOffice; WS_Seedling_Pct; Project; PrimaryKey; WS_HgtClass1_PctQuad; PctBankCoveredOld; RH_DeadDyingPlantParts; AH_NonSagebrushShrubCover; SagebrushShape_Live_ColumnCount; TotalNitrogen; SideChannels; LongitudeWGS84; FH_WoodyLitterCover; SppInv_CValue_Avg; Hgt_NoxPerenGrass_Avg; BankAngleAvg; ViewOBJECTID; Hgt_AllLitterThatch_Cnt; AH_HerbLitterCover; FH_NoxPerenForbCover; AH_PerennialCover; AH_WaterCover; FH_NoxAnnGrassCover; Latitude_NAD83; Sinuosity; FH_NonNoxAnnForbCover; SpeciesState; GapCover_25_50; WS_HgtClass5_PctQuad; RH_PedestalsTerracettes; BeaverSigns; SpecificConductance; AH_ForbCover; AH_NonNoxPerenForbGrassCover; PoolFreq; Hgt_PerenGrass_Avg; Elevation_m; AH_NonNoxAnnGrassCover; TopReachLatitude; AdminState; AH_LichenCover; RH_FuncSructGroup; D16; FH_TotalLitterCover; RH_SoilSiteStability; PctDry; Hgt_ShortPerenGrass_Avg; FH_HerbLitterCover; CoreSubset; FH_NonNoxSubShrubCover; DBKey; AH_NonNoxSucculentCover; AH_NonNoxPerenGrassCover; Hgt_Forb_Avg; SoilStability_Protected; AH_AnnualGraminoidCover; AH_NoxPerenForbCover; AH_AnnGrassCover; H_HummockTroughWidth_Avg; County; AH_NonNoxTreeCover; FH_MossCover; Hgt_Grass_Avg; AH_BasalCover; WS_WoodySpp_PctQuad; RH_BareGround; FH_SaltCrustCover; AH_GrassCover; FH_CyanobacteriaCover; RH_WindScouredAreas; AH_NoxSucculentCover; GeometricMeanParticleDiam; SppInv_Noxious_Cnt; WS_WoodyHgtClass_Cnt; Hgt_PerenForbGrass_Avg; SppInv_Richness_Cnt; AH_NoxAnnForbCover; AH_NoxSubShrubCover; RH_LitterMovement; AH_NoxAnnForbGrassCover; RH_Rills; FH_NoxTreeCover; SppInv_Native_Pct; H_HummockSlope_Avg; District; RH_AnnualProd; Hgt_Water_Cnt; D50; AH_TreeCover; WettedWidthWithBarAvg; FH_NonNoxPerenForbCover; AH_TotalLitterThatchCover; RecordID; AH_NonNoxShrubCover; Spp_Nox; AH_ShrubCover; FH_NoxSucculentCover; Hgt_Water_Avg; ProtocolType; Hgt_WoodyLitter_Avg; WS_HgtClass3_PctQuad; EcologicalSiteId; PctNoxiousHerbSpecies","weight":1},{"access":[{"file_format":"SHP","name":"podaac.jpl.nasa.gov","url":"https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_D"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Surface Water Ocean Topography (SWOT). 2025. SWOT Level 2 Lake Single-Pass Vector Data Product, Version D. Ver. D. PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at https://doi.org/10.5067/SWOT-LAKESP-D","creator":[],"creator_project":[],"date_created":"5/2/2025","date_updated":"6/3/2026","description":"The SWOT Level 2 Lake Single-Pass Vector Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, area, storage change derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025. Water surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a PLD-oriented feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.","doi_url":"https://doi.org/10.5067/SWOT-LAKESP-D","domain":["Hydrology"],"draft":false,"id":"7c8fdb83-a931-4039-a62e-c3ca23d99ede","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_D"}],"name":"SWOT Level 2 Lake Single-Pass Vector Data Product, Version D","permalink":"/catalog/datasets/7c8fdb83-a931-4039-a62e-c3ca23d99ede/","project_use_history":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"project_using":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"ref_fabric":false,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"varies - derived from points that are largely 30 m spacing","temporal_coverage":"2022 - Present","temporal_frequency":"3 weeks","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["identifier of the observed lake","lake IDs from prior database","fraction of observed lake covered by each prior lake","number of lakes in the PLD intersecting the observed lake","list of reach IDs that intersect the observed lake","time (UTC)","time (TAI)","UTC time","lake-averaged water surface elevation with respect to the geoid","total uncertainty in lake water surface elevation","random-only uncertainty in the height water surface elevation","standard deviation of pixels wse","total water area with estimate of dark water","uncertainty in total water area","area of detected water pixels","uncertainty in area of detected water","metric of layover effect","distance of lake polygon centroid to the satellite ground track","summary quality indicator for lake measurement","bitwise quality indicator for the lake","fractional area of dark water","climatological ice cover flag","dynamical ice cover flag","partially covered lake flag","quality of the cross-over calibration","geoid height","solid Earth tide height","geocentric load tide height (FES)","geocentric load tide height (GOT)","height of pole tide","dry tropospheric vertical correction to WSE","wet tropospheric vertical correction to WSE","ionospheric vertical correction to WSE","crossover calibration height correction","names of the lake","reservoir id from GRanD database"],"vars":"identifier of the observed lake; lake IDs from prior database; fraction of observed lake covered by each prior lake; number of lakes in the PLD intersecting the observed lake; list of reach IDs that intersect the observed lake; time (UTC); time (TAI); UTC time; lake-averaged water surface elevation with respect to the geoid; total uncertainty in lake water surface elevation; random-only uncertainty in the height water surface elevation; standard deviation of pixels wse; total water area with estimate of dark water; uncertainty in total water area; area of detected water pixels; uncertainty in area of detected water; metric of layover effect; distance of lake polygon centroid to the satellite ground track;  summary quality indicator for lake measurement; bitwise quality indicator for the lake; fractional area of dark water; climatological ice cover flag; dynamical ice cover flag; partially covered lake flag; quality of the cross-over calibration; geoid height; solid Earth tide height; geocentric load tide height (FES); geocentric load tide height (GOT); height of pole tide; dry tropospheric vertical correction to WSE; wet tropospheric vertical correction to WSE; ionospheric vertical correction to WSE; crossover calibration height correction; names of the lake; reservoir id from GRanD database","weight":1},{"access":[{"file_format":"CSV","name":"portal.edirepository.org","url":"https://portal.edirepository.org/nis/mapbrowse?packageid=edi.1395.1"}],"access_details":null,"bbox":{"east":-65,"north":53,"south":24,"west":-127},"citation":"Meyer, M.F., S.N. Topp, T.V. King, R. Ladwig, R.M. Pilla, H.A. Dugan, J.R. Eggleston, S.E. Hampton, D.M. Leech, I.A. Oleksy, J.C. Ross, M.R. Ross, R.I. Woolway, X. Yang, M.R. Brousil, K.C. Fickas, J.C. Padowski, A.I. Pollard, J. Ren, and J.A. Zwart. 2023. National-scale, remotely sensed lake trophic state (LTS-US) 1984-2020 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/212a3172ac36e8dc6e1862f9c2522fa4","creator":[{"creator_email":"mfmeyer@usgs.gov","creator_name":"Michael Meyer"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Lake trophic state is a key water quality property that integrates a lake's physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. Landsat surface reflectance and lake morphometric data were used to create the first compendium of lake trophic state for more than 56,000 lakes of at least 10 ha in size throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.","doi_url":"https://doi.org/10.6073/pasta/212a3172ac36e8dc6e1862f9c2522fa4","domain":["Hydrology"],"draft":false,"id":"7d2a03b6-707d-4dea-83be-b83f2c064363","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://portal.edirepository.org/nis/metadataviewer?packageid=edi.1395.1\u0026contentType=application/xml"},{"name":"Documentation","url":"https://doi.org/10.31223/X59H4W"}],"name":"Lake trophic state","permalink":"/catalog/datasets/7d2a03b6-707d-4dea-83be-b83f2c064363/","project_use_history":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"project_using":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS and parts of Canada","spatial_resolution":"100 meter","temporal_coverage":"1984 - 2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Dystrophic (dys) probability","Variance in dystrophic probability","Eutrophic or mixotrophic (eumixo) probability","Variance in eutrophic or mixotrophic probability","Oligotrophic (oligo) probability","Variance in oligotrophic probability"],"vars":"Dystrophic (dys) probability; Variance in dystrophic probability; Eutrophic or mixotrophic (eumixo) probability; Variance in eutrophic or mixotrophic probability; Oligotrophic (oligo) probability; Variance in oligotrophic probability","weight":1},{"access":[{"file_format":"SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/64510406d34eefd5da80a2c7"}],"bbox":{"east":-65.7104,"north":51.4699,"south":24.6542,"west":-127.7587},"citation":"Wieczorek, M.E., Staub, L.E., and Wnuk, K.C., Hafen, K.C., 2023, Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portions of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins (ver. 2.0, July 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P98IG8LO.","creator":[{"creator_email":"mewieczo@usgs.gov","creator_name":"Michael E. Wieczorek"}],"creator_project":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"date_created":"2/25/2026","date_updated":"6/3/2026","description":"This dataset represents 9,097 basin boundaries (rdews_gages.shp) of select U.S. Geological Survey's (USGS) active and historical stream gages derived from the published datasets of stream gage basins (Wieczorek, 2006), GAGESII (Falcone, 2011), and delineated from digital elevation models found in the NHDPlus version 1 data suite (NHDPlus, 2006). These basins were created to assist in spatial processing of model inputs for the U.S. Geological Survey's (USGS) Data-Driven Drought Prediction Project of the Drought Science Program within the Water Resources Mission Area's Water Resource Availability Program.","doi_url":"https://doi.org/10.5066/P98IG8LO","domain":["Hydrology"],"draft":false,"id":"7e18759c-b964-445d-b9d6-ac9134c922c8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/64510406d34eefd5da80a2c7?f=__disk__30%2Fa6%2Fb3%2F30a6b3499a70d857df27092a618a7088559b0a57\u0026allowOpen=true"}],"name":"Data-Driven Drought Prediction Project Spatial Processing Units: Select U.S. Geological Stream Gage Basins","permalink":"/catalog/datasets/7e18759c-b964-445d-b9d6-ac9134c922c8/","project_use_history":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"project_using":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["FID","Shape","StaID","sqm","source"],"vars":"FID; Shape; StaID; sqm; source","weight":1},{"access":[{"file_format":"GDB; SHP","name":"hydrosheds.org","url":"https://www.hydrosheds.org/hydroatlas"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V., Maxwell, S., Moidu, H., Tan, F., Thieme, M., 2019, Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution, Scientific Data 6: 283, https://doi.org/10.1038/s41597-019-0300-6","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"HydroATLAS is a comprehensive database gathering and presenting a wide range of hydro-environmental attributes from existing global datasets in a consistent and organized manner. HydroATLAS is divided in two datasets, BasinATLAS and RiverATLAS, which represent sub-basin delineations (polygons) and the river network (lines), respectively. HydroATLAS offers attributes grouped in seven categories: hydrology; physiography; climate; land cover \u0026 use; soils \u0026 geology; and anthropogenic influences. In its first version, HydroATLAS contains 56 hydro-environmental variables, partitioned into 281 individual attributes.","doi_url":null,"domain":["Hydrology","Stream Characteristics"],"draft":false,"id":"7e6e799b-b4ad-45f6-b8b6-5824317b71c4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"HydroATLAS","permalink":"/catalog/datasets/7e6e799b-b4ad-45f6-b8b6-5824317b71c4/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"Global","spatial_resolution":"15 arcsecond","temporal_coverage":"2019","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Hydro-environmental attributes","Elevation","Flow direction, Flow accumulation","Flow length"],"vars":"Hydro-environmental attributes; Elevation; Flow direction, Flow accumulation; Flow length","weight":1},{"access":[{"file_format":"SHP; CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/608035dcd34e8564d6835790"}],"bbox":{"east":-65.3917,"north":51.575,"south":22.9584,"west":-127.7858},"citation":"Buchwald, C.A., Houston, N.A., Stewart, J.S., York, B.C., and Valseth, K.J., 2022,  Public-supply water service areas within the conterminous United States, 2017, U.S. Geological Survey data release: https://doi.org/10.5066/P9I22Z24.","creator":[{"creator_email":"cabuchwa@usgs.gov","creator_name":"Cheryl A. Buchwald"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data release describes water service areas (WSA) for community water systems (CWS) within the conterminous United States, representing areas of active service between 2010 and 2020. A WSA is defined by a delineated polygon that contains all customers served by a water system. WSAs are represented by an ArcGIS shapefile. The U.S. Safe Drinking Water Act defines a CWS as a type of public-water system that serves at least 15 service connections used by year-round residents or regularly serves at least 25 year-round residents. Water may be used for several purposes (such as for commercial, industrial, and residential uses) or may be used only for one specific purpose (such as for residential use). This data release includes CWS that operate their own infrastructure and furnish water through their own water sources, purchase water from a neighboring water system, or are diversified in that they serve water from a combination of their own sources and purchases. This dataset also includes communities that do not operate a water system but receive water services by way of contract; in other words, an adjacent water system’s infrastructure extends their waterlines across boundaries from which residents connect to, are supplied, and directly billed from this neighboring water system.\u003cbr\u003e\n\u003cbr\u003e\nThis data release includes two shapefiles, five tables, a data dictionary, and four metadata documents which are described below.\u003cbr\u003e\n\u003cbr\u003e\nPublicSupplyWaterServiceAreas_metadata.xml -\u0026nbsp;Public-supply water service areas main page metadata.\u0026nbsp;\u003cbr\u003e\nv1_GU_wWS.zip\u0026nbsp; Zipped folder which contains the shapefile\u0026nbsp;of\u0026nbsp;the individual geographic units that were used to build the water service area (WSA) dataset for public-supply systems,\u0026nbsp;and the metadata document for this shapefile.\u003cbr\u003e\nWSA_v1.zip Zipped folder which contains the shapefile of the\u0026nbsp;the aggregated geographic units (GU) that were used to represent the water service areas (WSA) for public-supply water systems, and the metadata document for this shapefile.\u003cbr\u003e\nDataTables.zip - Zipped folder of all five tables used in comparisons to water service areas, the data dictionary which is a quick reference guide for all attributes in the five tables, and the metadata document for the tables\u003cbr\u003e\n\u0026nbsp;","doi_url":"https://doi.org/10.5066/P9I22Z24","domain":["Water Use"],"draft":false,"id":"7ec0a58f-ac37-4ff2-8ed4-c425daa4dcbf","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/608035dcd34e8564d6835790?f=__disk__68%2Ff1%2Ffa%2F68f1fac9507421b3848b8b18a6ca5123b681d3de\u0026allowOpen=true"}],"name":"Public-Supply Water Service Areas Within the Conterminous United States, 2017","permalink":"/catalog/datasets/7ec0a58f-ac37-4ff2-8ed4-c425daa4dcbf/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"unknown","temporal_coverage":"2017","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["NWALTclass","NWALTcode","NLUDclass","NLUDcode","WSAclass","STATE","GUWSA","NW_TURB","NW_TURBexurb","NW_GUURB","NW_GUURBexurb","NL_TURB","NL_TURBexurb","NL_GUURB","NL_GUURBexurb","State","GUWSA","ESWSA","NW_ESURB","NW_ESURBexurb","NL_ESURB","NL_ESURBexurb","MetroName","System_ID","POP_GUUSGS","GUWSA","POP_GUCEN","ESWSA","POP_ESCEN","GUPC_WSA","GUPC_CEN","WSA_AGIDF","Issue","WSA_AGIDF","POLY_COUNT","WSA_SQKM","TPOLYPOP","TPOPSRV","STATE_NAME","WSA_NAME","GU_SRC","GU_ID","STATE_NAME","PLACE_FIPS","PLACE_NAME","GU_POP","GUPOPSRC","AREASQKM","GNIS_ID","GNIS_NAME","GU_DESC","SYSINFOSRC","AGENCY_CD","USGS_T_SID","USGS_NAME","USEPA_NAME","PWS_ID","PWS_ID_MTH","OWNER_TYPE","SITE_TP","CC_TYPE","CNTY_CD","CNTY_NM","CNTY_SRC","LAT","LONG","LOC_METH","POP_SRV","POPSRV_SRC","POPSRC_YR","WTR_TYPE_G","WTR_TYPE_E","SELLER_PWS","SELLER_NM","STATUS","WSA_AGIDF","AG_METHF","SA_TYPE"],"vars":"NWALTclass; NWALTcode; NLUDclass; NLUDcode; WSAclass; STATE; GUWSA; NW_TURB; NW_TURBexurb; NW_GUURB; NW_GUURBexurb; NL_TURB; NL_TURBexurb; NL_GUURB; NL_GUURBexurb; State; GUWSA; ESWSA; NW_ESURB; NW_ESURBexurb; NL_ESURB; NL_ESURBexurb; MetroName; System_ID; POP_GUUSGS; GUWSA; POP_GUCEN; ESWSA; POP_ESCEN; GUPC_WSA; GUPC_CEN; WSA_AGIDF; Issue; WSA_AGIDF; POLY_COUNT; WSA_SQKM; TPOLYPOP; TPOPSRV; STATE_NAME; WSA_NAME; GU_SRC; GU_ID; STATE_NAME; PLACE_FIPS; PLACE_NAME; GU_POP; GUPOPSRC; AREASQKM; GNIS_ID; GNIS_NAME; GU_DESC; SYSINFOSRC; AGENCY_CD; USGS_T_SID; USGS_NAME; USEPA_NAME; PWS_ID; PWS_ID_MTH; OWNER_TYPE; SITE_TP; CC_TYPE; CNTY_CD; CNTY_NM; CNTY_SRC; LAT; LONG; LOC_METH; POP_SRV; POPSRV_SRC; POPSRC_YR; WTR_TYPE_G; WTR_TYPE_E; SELLER_PWS; SELLER_NM; STATUS; WSA_AGIDF; AG_METHF; SA_TYPE","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6023e628d34e31ed20c874e4"}],"access_details":null,"bbox":{"east":-74.1515,"north":42.564,"south":38.3889,"west":-76.5983},"citation":"Miller, M.P., Foks, S.S., Hopple, J.A., and Carlisle, D.M., 2021, Monthly estimates of natural baseflow for 15,866 stream reaches, defined by the National Hydrography Dataset Plus Version 2.0 (NHDPlusV2), in the Delaware River Basin for the period 1950-2015: U.S. Geological Survey data release, https://doi.org/10.5066/P9FZG7GZ","creator":[{"creator_email":"mamiller@usgs.gov","creator_name":"Matt Miller"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This metadata record describes monthly estimates of natural baseflow for 15,866 stream reaches, defined by the National Hydrography Dataset Plus Version 2.0 (NHDPlusV2), in the Delaware River Basin for the period 1950-2015. A statistical machine learning technique - random forest modeling (Liaw and Wiener, 2018; R Core Team, 2020) - was applied to estimate natural flows using about 150 potential predictor variables (Miller and others, 2018). Calibration data used for the random forest model are available from (Foks and others, 2020). Each model was run twice, first using all potential predictor variables, which represents a \"full\" model run, and a second time using the top 20 predictors from the original run, which represents the \"partial\" model run. Model performance of the full and partial models were compared and identified to be similar. Therefore, predictions for all NHDPlusV2 stream reaches were made using the partial model. Methods used to calibrate the random forest models, and references to predictor data sources are detailed in (Miller and others, 2018). The R scripts used and directions to run the scripts are included in this data release.\u003cbr\u003eReferences cited:\u003cbr\u003eLiaw, A., and Wiener, M., 2018, Package 'randomForest': The Comprehensive R Archive Network, https://cran.r-project.org/web/packages/randomForest/randomForest.pdf.\u003cbr\u003eMiller, M.P., Carlisle, D.M., Wolock, D.M., and Wieczorek, M., 2018, A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States: Journal of the American Water Resources Association, v. 54, no. 6, p. 1258-1269, https://doi.org/10.1111/1752-1688.12685\u003cbr\u003eFoks, S.S., Miller, M.P., and Hopple, J.A., 2020, Daily-timestep and monthly-timestep estimates of baseflow at 49 reference stream gages located within 25 miles of the Delaware River basin watershed boundary for the years 1950 through 2015: U.S. Geological Survey data release, https://doi.org/10.5066/P9XY70L4\u003cbr\u003eR Core Team, 2020, R-A language and environment for statistical computing: R Foundation for Statistical Computing, https://www.eea.europa.eu/data-and-maps/indicators/oxygen-consuming-substances-in-rivers/r-development-core-team-2006","doi_url":"https://doi.org/10.5066/P9FZG7GZ","domain":["Hydrology"],"draft":false,"id":"802f9222-f6fe-486a-ace8-e6aa78193a40","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6023e628d34e31ed20c874e4?f=__disk__8b%2Fc4%2F75%2F8bc475e5a7348cfb94b03c917393ef42a21a5752\u0026transform=1\u0026allowOpen=true"},{"name":"Documentation","url":"https://doi.org/10.5066/P9FZG7GZ"}],"name":"Monthly estimates of natural baseflow for 15,866 stream reaches, defined by the National Hydrography Dataset Plus Version 2.0 (NHDPlusV2), in the Delaware River Basin for the period 1950-2015","permalink":"/catalog/datasets/802f9222-f6fe-486a-ace8-e6aa78193a40/","project_use_history":[],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Delaware River basin","spatial_resolution":"1:100,000","temporal_coverage":"1950 - 2015","temporal_frequency":"monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Baseflow estimates","Random forest model inputs and outputs"],"vars":"Baseflow estimates; Random forest model inputs and outputs","weight":1},{"access":[{"file_format":"GRID","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/631405c5d34e36012efa3198"}],"access_details":null,"bbox":{"east":-65,"north":50,"south":24,"west":-127},"citation":"Wolock, D.M., 1997, STATSGO soil characteristics for the conterminous United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9IGBBP2.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This digital data release consists of an ARC/INFO grid and associated INFO tables. The grid is called MUID and has STATSGO (U.S. Department of Agriculture, 1994) soil mapping unit identifiers gridded on a 1-kilometer resolution for the conterminous United States. The INFO tables have soil characteristics data in them. The ITEMS in the tables are weighted average values for several soil characteristics in the STATSGO data base. The weighted average values were computed by aggregating the soil layers and components in the data base.","doi_url":"https://doi.org/10.5066/P9IGBBP2","domain":["Soils"],"draft":false,"id":"80f85d01-ce67-4040-ac60-7ad8645fa35b","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/631405c5d34e36012efa3198?f=__disk__bf%2F43%2F8c%2Fbf438ca20d4ae77f9be462aefb6a16099b119ec9\u0026allowOpen=true"}],"name":"STATSGO soil characteristics for the conterminous United States","permalink":"/catalog/datasets/80f85d01-ce67-4040-ac60-7ad8645fa35b/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1 kilometer","temporal_coverage":"1997","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["VALUE","COUNT","NUMID","MUID","MIN","MAX","MEAN","STDV","SLOPEL","SLOPEH","WTDEPL","WTDEPH","ROCKDEPL","ROCKDEPH","COMPPER","KFACT","COMPPER","PERML","PERMH","AWCL","AWCH","BDL","BDH","OML","OMH","COMPPER","TFACT","COMPPER","GOTDAT","WEG","COMPPER","GOTDAT"],"vars":"VALUE; COUNT; NUMID; MUID; MIN; MAX; MEAN; STDV; SLOPEL; SLOPEH; WTDEPL; WTDEPH; ROCKDEPL; ROCKDEPH; COMPPER; KFACT; COMPPER; PERML; PERMH; AWCL; AWCH; BDL; BDH; OML; OMH; COMPPER; TFACT; COMPPER; GOTDAT; WEG; COMPPER; GOTDAT","weight":1},{"access":[{"file_format":"GPKG","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6128fbf2d34e40dd9c061360"}],"access_details":null,"bbox":{"east":177.543721,"north":71.333289,"south":-14.360469,"west":-176.732823},"citation":"Wieferich, D.J., 2022, Database of Stream Crossings in the United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9YX6KTB","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This USGS data release is intended to provide a baselayer of information on likely stream crossings throughout the United States. The geopackage provides likely crossings of infrastructure and streams and provides observed information that helps validate modeled crossings and build knowledge about associated conditions through time (that is, crossing type, crossing condition). Stream crossings were developed by intersecting the 2020 United States Census Bureau Topologically Integrated Geographic Encoding and Referencing (TIGER) U.S. road lines with the National Hydrography Dataset High Resolution flowlines. The current version of this data release specifically focuses on road stream crossings (i.e. TIGER2020 Roads) but is designed to support additions of other crossing types that may be included in future iterations (that is, rail). In total 6,608,268 crossings are included in the dataset and 496,564 observations from the U.S. Department of Transportation, Federal Highway Administration's 2019 National Bridge Inventory (NBI) are included to help identify crossing types of bridges and culverts. This data release also contains Python code that documents methods of data development.","doi_url":"https://doi.org/10.5066/P9YX6KTB","domain":["Hydrology"],"draft":false,"id":"816cc77e-f783-43f3-84e1-28b73f32117e","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6128fbf2d34e40dd9c061360?f=__disk__74%2F71%2Fb4%2F7471b4f352dc149f81ac27764f5c381024f33e73\u0026allowOpen=true"}],"name":"Database of Stream Crossings in the United States","permalink":"/catalog/datasets/816cc77e-f783-43f3-84e1-28b73f32117e/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; AK; HI","spatial_resolution":"1:24,000","temporal_coverage":"2019 - 2021","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Road stream crossings"],"vars":"Road stream crossings","weight":1},{"access":[{"file_format":"SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5ebe92af82ce476925e44b8f"}],"access_details":null,"bbox":{"east":-67.2475,"north":49.515,"south":25.1686,"west":-124.4349},"citation":"Hayes, L., Chase, K.J., Wieczorek, M.E., and Jackson, S.E., 2021, USGS streamgages in the conterminous United States indexed to NHDPlus v2.1 flowlines to support Streamgage Watershed InforMation (SWIM), 2021: U.S. Geological Survey data release, https://doi.org/10.5066/P9J5CK2Y.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"\u003cp\u003eThis U.S. Geological Survey (USGS) data release includes locations for 12,422 USGS streamgages as indexed along the network of streams (flowlines) in NHDPlus Version 2.1 (NHDPlus v2, Moore and Dewald, 2016). The dataset is one of two datasets developed for the Streamgage Watershed InforMation (SWIM) project. This dataset, which is referred to as “SWIM streamgage locations,” was created in support of the second dataset of basin characteristics and disturbance indexes. The streamgages are located in the conterminous United States and have a minimum record length of 20 years of daily streamflow values or at least 20 years of peak flows (USGS National Water Information System (NWIS) database, U.S. Geological Survey, 2016). This dataset has a total of 13,248 streamgages, 826 of which could not be indexed to NHDPlus v2.1.\u003c/p\u003e\n\n\u003cp\u003eA custom ArcGIS tool was programmed to conduct linear referencing, which moved each point representing a streamgage to intersect with the nearest flowline and calculated the measure along the segment (expressed as a percentage from its downstream end). The tool then performed a series of automated tests to identify potentially inaccurate locations that were, in turn, individually checked. Comments collected during multiple levels of review were retained in raw form to aid future decisions about the accuracy of the streamgage locations along the medium-resolution (1:100,000-scale) NHDPlus stream segments. The results include the unique flowline identifier (COMID) and measure along the flowline, the reach code and measure along its reach (stream feature that consists of one or more flowlines), review notes, plus the latitude and longitude of the stream-referenced location for each streamgage. This designated position along the NHDPlus network may also be referred to as the hydrographic address of the streamgage.\u003c/p\u003e\n\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003eReferences:\u003c/p\u003e\n\n\u003cp\u003eFalcone, J.A., 2011, GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow: U.S. Geological Survey dataset, https://doi.org/10.3133/70046617\u003c/p\u003e\n\n\u003cp\u003eMoore, R.B., and Dewald, T.G., 2016, The Road to NHDPlus — Advancements in digital stream networks and associated catchments: Journal of the American Water Resources Association, https://doi.org/10.1111/1752-1688.12389\u003c/p\u003e\n\n\u003cp\u003eU.S. Geological Survey, 2016, USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed October 2016, at https://doi.org/10.5066/F7P55KJN\u003c/p\u003e\n","doi_url":"https://doi.org/10.5066/P9J5CK2Y","domain":["Hydrology"],"draft":false,"id":"862d3c17-01c1-48c7-88ac-dcba1e8a5b37","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5ebe92af82ce476925e44b8f?f=__disk__38%2Fc8%2Fe5%2F38c8e5d8aaa39528f7a8ae0b50cbab57e0ac3a87\u0026transform=1\u0026allowOpen=true"}],"name":"USGS Streamgages in the Conterminous United States Indexed to NHDPlus v2.1 Flowlines to Support Streamgage Watershed InforMation (SWIM), 2021","permalink":"/catalog/datasets/862d3c17-01c1-48c7-88ac-dcba1e8a5b37/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":true,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2016","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Gages"],"vars":"Gages","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6075bf7fd34e018b3201c87f"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-70.1404,"west":-178},"citation":"Williams, B.M., Gordon, S.E., Putman, A.L., and Jones, D.K., 2021, Quality assessed and modified Discharge Monitoring Report (DMR) facility and outfall locations, 2007 - 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9KUSHSE.","creator":[{"creator_email":"bmwilliams@usgs.gov","creator_name":"Brianna M Williams"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Each year the U.S. Environmental Protection Agency (EPA) reports permit, location, and discharge information for facilities across the United States and its territories through the Discharge Monitoring Report (DMR). Because these data are cataloged through a variety of systems, including self-reporting, there are discrepancies that may lead to incorrect spatial interpretation of content in the database. The processing of this quality assessed and modified dataset included steps to evaluate the accuracy and potential limitations of DMR data between 2007 and 2019. Attributes within this tabular dataset include facility and outfall locations as well as confidence ratings for those locations, and hydrologic unit code (HUC) 12 information on outfalls above a certain threshold of confidence. Discretion should be used when utilizing this dataset and users should ensure an understanding of the metadata or content in the DMR_ReadMe.docx file.","doi_url":"https://doi.org/10.5066/P9KUSHSE","domain":["Hydrology"],"draft":false,"id":"86c5e4c0-f922-4c5b-a085-ccd2cc9048dd","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6075bf7fd34e018b3201c87f?f=__disk__62%2F6e%2F99%2F626e9903da3ec5e2b4bb07e899d607e95f07e748\u0026allowOpen=true"}],"name":"Quality assessed and modified Discharge Monitoring Report (DMR) facility and outfall locations, 2007 - 2019","permalink":"/catalog/datasets/86c5e4c0-f922-4c5b-a085-ccd2cc9048dd/","project_use_history":[],"project_using":[],"ref_fabric":true,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"1:100,000","temporal_coverage":"2007 - 2019","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["NPDES_ID","FACILITY_NAME","FACILITY_LATITUDE","FACILITY_LONGITUDE","FacilityLocationQuality","FacilityLatLongSource","ICIS_FACILITY_INTEREST_ID","OUTFALL_LATITUDE","OUTFALL_LONGITUDE","OutfallLocationQuality","PERM_FEATURE_ID","PERM_FEATURE_NMBR","HUC_12","FACILITY_ADDRESS","SUPPLEMENTAL_ADDRESS","FACILITY_CITY","FACILITY_STATE","FACILITY_ZIP","OUTFALL_COUNTY","OUTFALL_STATE","IMPAIRED_WATERS"],"vars":"NPDES_ID; FACILITY_NAME; FACILITY_LATITUDE; FACILITY_LONGITUDE; FacilityLocationQuality; FacilityLatLongSource; ICIS_FACILITY_INTEREST_ID; OUTFALL_LATITUDE; OUTFALL_LONGITUDE; OutfallLocationQuality; PERM_FEATURE_ID; PERM_FEATURE_NMBR; HUC_12; FACILITY_ADDRESS; SUPPLEMENTAL_ADDRESS; FACILITY_CITY; FACILITY_STATE; FACILITY_ZIP; OUTFALL_COUNTY; OUTFALL_STATE; IMPAIRED_WATERS","weight":1},{"access":[{"file_format":"HDF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-mcd43a4-006"}],"access_details":"To access data from the earthdata.nasa.gov access url, an Earthdata Login is required before users can download data or use selected tools that comprise NASA's Earth Observing System Data and Information System (EOSDIS).","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Schaaf, C. and Wang, Z., 2015, MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted Ref Daily L3 Global - 500m V006 [Data set]: NASA EOSDIS Land Processes Distributed Active Archive Center, accessed [YYYY-MM-DD] at https://doi.org/10.5067/MODIS/MCD43A4.006","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Deprecated: this data product has been deprecated meaning it has been retired but is still discoverable for historical purposes. Users are encouraged to use the newer version of MCD43A4.\u003cbr\u003e\u003cbr\u003eThe Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.\u003cbr\u003eUsers are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide.\u003cbr\u003eThe MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.","doi_url":"https://doi.org/10.5067/MODIS/MCD43A4.006","domain":["Climate"],"draft":false,"id":"876145bc-886f-4fc9-993e-e92600828f42","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a4-nbar-product/"}],"name":"MCD43A4 v006: MODIS/Terra+Aqua Nadir BRDF-Adjusted Reflectance (NBAR) Daily L3 Global 500 m SIN Grid","permalink":"/catalog/datasets/876145bc-886f-4fc9-993e-e92600828f42/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS; NASA","spatial_extent":"Global","spatial_resolution":"500 meter","temporal_coverage":"2000 - 2023","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Albedo","Reflectance"],"vars":"Albedo; Reflectance","weight":1},{"access":[{"file_format":"GEOTIFF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/66da33ced34eef5af66d5548"}],"bbox":{"east":-66.95,"north":49.384,"south":24.515,"west":-124.733},"citation":"Sarbanes, A.A., and Jones, J.W., 2025, Collection 2 Dynamic Surface Water Extent (DSWE) Proportions: CONUS Annual Proportions 1984-Present: U.S. Geological Survey data release, https://doi.org/10.5066/P16Q2FUD.","creator":[{"creator_email":"asarbanes@usgs.gov","creator_name":"Anteneh Sarbanes"}],"creator_project":[{"id":"DJ33TU3","name":"OPERA: Observational Products for End-Users from Remote Sensing Analysis"}],"date_created":"7/2/2025","date_updated":"6/3/2026","description":"Dynamic Surface Water Extent (DSWE) Annual Proportions data summarize occurrence of water on the land surface. DSWE Annual Proportions data are statistical summaries of the USGS Landsat Collection 2 Level-3 DSWE data product (C2-DSWE), a raster dataset of per-pixel surface water inundation status based on the analysis of Landsat 4-9 satellite imagery. DSWE Annual Proportions data average occurrence of surface water extent over yearly time periods across the conterminous US in the form of GeoTIFF rasters with 30m spatial resolution. DSWE Annual Proportions raster pixel values represent the portion of cloud, cloud shadow, and snow free Landsat observations that are classified as surface water inundation in C2-DSWE Interpreted with all Mask ('INWAM') layer. Each DSWE Annual Proportions GeoTIFF contains 5 raster bands: 3 corresponding to DSWE surface water classes (i.e., open, partial, and non-water); 1 representing total surface water (partial and open values combined); and 1 providing a per pixel count of valid DSWE input values. DSWE Annual Proportions data are processed and archived to the continental [US Analysis Ready Data (ARD) tile scheme](https://landsat.usgs.gov/ard_tile).","doi_url":"https://doi.org/10.5066/P16Q2FUD","domain":["Hydrology"],"draft":false,"id":"876e7021-8b01-4213-8e2e-bd21d0fce6f9","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/66da33ced34eef5af66d5548?f=__disk__0c%2F02%2F95%2F0c029547ff9563f711e338250c85be74434f3a69\u0026allowOpen=true"}],"name":"Collection 2 Dynamic Surface Water Extent (DSWE) Proportions: CONUS Annual Proportions 1984-Present","permalink":"/catalog/datasets/876e7021-8b01-4213-8e2e-bd21d0fce6f9/","project_use_history":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"project_using":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"1984 - Present","temporal_frequency":"annual","update_detail":"append","update_frequency":"annual","update_type":"Dynamic","variables":["total_sw","open_sw","partial_sw","nonwater","num_Observations"],"vars":"total_sw; open_sw; partial_sw; nonwater; num_Observations","weight":1},{"access":[{"file_format":"SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/63125df6d34e36012efa16eb"}],"access_details":null,"bbox":{"east":-87.8555,"north":43.3075,"south":42.8783,"west":-88.2047},"citation":"Sterner, S.P., Fitzpatrick, F.A., Blount, J.D., and Stewart, J.S., 2023, Geomorphic Habitat Response Units Attributes for the Wisconsin DNR 24k Hydrography Flowline Network in the Milwaukee River Basin, Wisconsin: U.S. Geological Survey Data Release, https://doi.org/10.5066/P90S2FMB.","creator":[],"creator_project":[],"date_created":"4/23/2024","date_updated":"6/3/2026","description":"This data was collected for the purpose of developing a geomorphology and habitat-based classification system for use in stream rehabilitation planning, design, and evaluation. These classifications help to quantify the spatial extent and lateral and longitudinal linkages of potential habitat features characteristic of the historic or more natural, pre-urban conditions.\u003cbr\u003eThis data release was produced as part of the Milwaukee Area Watercourse Corridor Study in partnership with the Milwaukee Metropolitan Sewerage District (MMSD) to monitor and assess stream water quality within studies of aquatic communities, geomorphology and habitat, water and sediment, and streamflow. This dataset contains two vector datasets of stream geomorphic characteristics, pre-settlement vegetation, and geomorphic setting attributes derived for the Wisconsin Department of Natural Resources 1:24,000-scale Hydrography Dataset flowline network in the Kinnickinic River and Menomonee River sub-basins of the Milwaukee River basin in eastern Wisconsin. The attributes used in the classification of network reaches were derived from publicly available county, state, and national datasets and include channel slope, Strahler stream order, pre-settlement vegetation class, surficial geology, and current land cover category within a 30-m buffer on either side of each flowline feature.","doi_url":"https://doi.org/10.5066/P90S2FMB","domain":["Stream Characteristics"],"draft":false,"id":"8772b398-d4ca-46aa-b8c6-683754cf4507","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/63125df6d34e36012efa16eb?f=__disk__80%2F00%2F90%2F8000903cb4455ea24b4e4646048a53d0c8a9d840\u0026allowOpen=true"}],"name":"Geomorphic Habitat Response Units Attributes for the Wisconsin DNR 24k Hydrography Flowline Network in the Milwaukee River Basin","permalink":"/catalog/datasets/8772b398-d4ca-46aa-b8c6-683754cf4507/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Milwaukee River basin","spatial_resolution":"1:24,000","temporal_coverage":"2022","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["slope","stream order","pre-settlement vegetation class","surficial geology","land cover"],"vars":"slope; stream order; pre-settlement vegetation class; surficial geology; land cover","weight":1},{"access":[{"file_format":"GDB; SHP; GRID","name":"epa.gov","url":"https://www.epa.gov/waterdata/get-nhdplus-national-hydrography-dataset-plus-data"}],"access_details":null,"bbox":{"east":-56,"north":72,"south":14,"west":-180},"citation":"McKay, L., Bondelid, T., Dewald, T., Johnston, J., Moore, R., and Rea, A., 2012, NHDPlus Version 2: User Guide, https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"NHDPlus is a national hydrography dataset of surface water features, including stream segments and their associated elevation-derived catchments, that form a seamless national network of streams. In addition to surface water features, this dataset also includes mean annual and mean monthly streamflow estimates based on runoff from the USGS national flow balance model, as well as stream slope, stream order, and a group of attributes that enable rapid stream network navigation. NHDPlus catchments can be used to associate other landscape attributes, such as land cover, with stream segments.\u003cbr\u003eNHDPlus integrates stream network features from the original National Hydrography Dataset (NHD) with 10 meter gridded land surface elevation data from the National Elevation Dataset (NED), and hydrologic unit boundaries from the National Watershed Boundary Dataset (WBD). Since NHDPlus is produced from static snapshots of these three datasets, it includes the features of these ingredient datasets as well.\u003cbr\u003eThere is an updated, high-resolution version of this dataset called [NHDPlus HR](/datasets/fd9b5188-5336-4cfd-9c7b-70680001eef0) that is actively being developed and updated.","doi_url":null,"domain":["Hydrology"],"draft":false,"id":"8a60b6b4-d785-4265-af99-cd1870ea7928","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[{"id":"14b6eb88-06dc-4152-b37f-a355c850bc7e","rel_type":"IsSourceOf"}],"linked_usecases":[{"id":"6389d98c-7de8-4dc7-938c-f98da1063455","rel_type":"IsSourceOf"}],"links":[{"name":"Metadata","url":"https://www.epa.gov/waterdata/learn-more#metadata"},{"name":"Documentation","url":"https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus"}],"name":"NHDPlus v2: National Hydrography Dataset Plus version 2","permalink":"/catalog/datasets/8a60b6b4-d785-4265-af99-cd1870ea7928/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"EPA; USGS","spatial_extent":"United States","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Stream segments","Catchments","Streamflow, mean annual","Streamflow, mean monthly","Streamflow, mean annual velocity","Hydrologic sequencing","Stream order","Stream slope","Stream elevation","Cumulative drainage area","Flow withdrawals","Flow transfers","Flow augmentation"],"vars":"Stream segments; Catchments; Streamflow, mean annual; Streamflow, mean monthly; Streamflow, mean annual velocity; Hydrologic sequencing; Stream order; Stream slope; Stream elevation; Cumulative drainage area; Flow withdrawals; Flow transfers; Flow augmentation","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6675b4dbd34e6f159fd10baf"}],"access_details":null,"bbox":{"east":-65.5664,"north":49.6107,"south":24.3671,"west":-125.332},"citation":"Ator, S.W., Saad, D.A., Koenig Snyder, L.E., Johnson, Z.C., and Oelsner, G.P., 2024, Estimated seasonal nitrogen and phosphorus loads in selected streams of the conterminous United States, 1999 - 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P13YSTHM.","creator":[],"creator_project":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"date_created":"10/16/2024","date_updated":"6/3/2026","description":"Estimated seasonal total nitrogen and total phosphorus loads during 1999 through 2020 in selected streams of the conterminous United States and water-quality and stream flow data used to generate those estimates are presented in this dataset. Loads were generated as part of the [Integrated Water-Availability Assessment (IWAA) Program of the U.S. Geological Survey](www.usgs.gov/mission-areas/water-resources/science/integrated-water-availability-assessments#overview) using Fluxmaster ([Schwarz and others, 2006](https://doi.org/10.3133/tm6B3)) and Weighted Regression on Time, Discharge, and Season (WRTDS) ([Hirsch and De Cicco, 2015](https://doi.org/10.1016/j.envsoft.2015.07.017)). Loads were estimated initially for the Illinois River Basin (IRB) and later for the wider conterminous United States (including the IRB). Along with the water-quality data, streamflow data, and estimated nitrogen and phosphorus loads, this release also includes computer code that can be run to recreate the load estimates or modified for similar applications.","doi_url":"https://doi.org/10.5066/P13YSTHM","domain":["Hydrology","Water Quality"],"draft":false,"id":"8a7d3140-2272-433c-9359-30840125cd7c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6675b4dbd34e6f159fd10baf?f=__disk__df%2F35%2F1f%2Fdf351f5b95cbadc08c91bf6cd1b50ef5f374379f\u0026allowOpen=true"}],"name":"Estimated seasonal nitrogen and phosphorus loads in selected streams of the conterminous United States, 1999 - 2020","permalink":"/catalog/datasets/8a7d3140-2272-433c-9359-30840125cd7c/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1999 - 2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["station_id","station_name","flow_station_id","comid","area","flow_station_area","lat","lon","altsite_id","altsite_area","huc8","date","time","p00600","r00600","d00600","p00665","r00665","d00665","WYEARS","QUARTERS","if_flow_detrended","mndtdf","semndtdf","sddtdf","method_00600","load_00600","sload_00600","dsload_00600","method_00665","load_00665","sload_00665","dsload_00665","parm_code","b_int","b_ldflow","b_sin","b_cos","b_trend","Qdaily","lyyyyy","hyyyyy","ryyyyy","cyyyyy","INFO_station_id","INFO_station_name","INFO_flow_station_id","INFO_wq_start_date_yyyyy","INFO_wq_end_date_yyyyy","INFO_tx_years","INFO_tx_count","INFO_da_ratio","INFO_site_no","INFO_drainSqKm","INFO_shortName","INFO_staAbbrev","INFO_paramShortName","INFO_constitAbbrev","INFO_param.units","INFO_bottomLogQ","INFO_stepLogQ","INFO_nVectorLogQ","INFO_bottomYear","INFO_stepYear","INFO_nVectorYear","INFO_windowY","INFO_windowQ","INFO_windowS","INFO_minNumObs","INFO_minNumUncen","INFO_numDays","INFO_DecLow","INFO_DecHigh","INFO_edgeAdjust","Daily_Date","Daily_Q","Daily_Julian","Daily_Month","Daily_Day","Daily_DecYear","Daily_MonthSeq","Daily_waterYear","Daily_i","Daily_LogQ","Daily_Q7","Daily_Q30","Daily_yHat","Daily_SE","Daily_ConcDay","Daily_FluxDay","Daily_FNConc","Daily_FNFlux","Daily_GenFlux","Daily_GenConc","Sample_Date","Sample_Q","Sample_LogQ","Sample_ConcLow","Sample_ConcHigh","Sample_Uncen","Sample_ConcAve","Sample_Julian","Sample_Month","Sample_Day","Sample_DecYear","Sample_MonthSeq","Sample_waterYear","Sample_SinDY","Sample_CosDY","Sample_yHat","Sample_SE","Sample_ConcHat","surfaces","wq_start_date_00600","wq_end_date_00600","tn_years","tn_count","da_ratio","wrtds","wq_start_date_00665","wq_end_date_00665","tp_years","tp_count","file","summary_spreadsheet","site","constituent","model_prediction","low_thresh","high_thresh","model_category","eList","pcode","siteID","summaryChoice","notes","WQ_FLAG","FLUXM_FLAG","WRTDS_FLAG","CONUS_Load","IRB_Load","agency_cd","site_no","datetime","n_00060_0000m","n_00060_0000m_cd","cn_00060_0000m","period","nDays","fluxSeason","complete","model","drainSqKm","shortName","staAbbrev","paramShortName","constitAbbrev","param.units","bottomLogQ","stepLogQ","nVectorLogQ","bottomYear","stepYear","nVectorYear","windowY","windowQ","windowS","minNumObs","minNumUncen","numDays","DecLow","DecHigh","edgeAdjust"],"vars":"station_id; station_name; flow_station_id; comid; area; flow_station_area; lat; lon; altsite_id; altsite_area; huc8; date; time; p00600; r00600; d00600; p00665; r00665; d00665; WYEARS; QUARTERS; if_flow_detrended; mndtdf; semndtdf; sddtdf; method_00600; load_00600; sload_00600; dsload_00600; method_00665; load_00665; sload_00665; dsload_00665; parm_code; b_int; b_ldflow; b_sin; b_cos; b_trend; Qdaily; lyyyyy; hyyyyy; ryyyyy; cyyyyy; INFO$station_id; INFO$station_name; INFO$flow_station_id; INFO$wq_start_date_yyyyy; INFO$wq_end_date_yyyyy; INFO$tx_years; INFO$tx_count; INFO$da_ratio; INFO$site_no; INFO$drainSqKm; INFO$shortName; INFO$staAbbrev; INFO$paramShortName; INFO$constitAbbrev; INFO$param.units; INFO$bottomLogQ; INFO$stepLogQ; INFO$nVectorLogQ; INFO$bottomYear; INFO$stepYear; INFO$nVectorYear; INFO$windowY; INFO$windowQ; INFO$windowS; INFO$minNumObs; INFO$minNumUncen; INFO$numDays; INFO$DecLow; INFO$DecHigh; INFO$edgeAdjust; Daily$Date; Daily$Q; Daily$Julian; Daily$Month; Daily$Day; Daily$DecYear; Daily$MonthSeq; Daily$waterYear; Daily$i; Daily$LogQ; Daily$Q7; Daily$Q30; Daily$yHat; Daily$SE; Daily$ConcDay; Daily$FluxDay; Daily$FNConc; Daily$FNFlux; Daily$GenFlux; Daily$GenConc; Sample$Date; Sample$Q; Sample$LogQ; Sample$ConcLow; Sample$ConcHigh; Sample$Uncen; Sample$ConcAve; Sample$Julian; Sample$Month; Sample$Day; Sample$DecYear; Sample$MonthSeq; Sample$waterYear; Sample$SinDY; Sample$CosDY; Sample$yHat; Sample$SE; Sample$ConcHat; surfaces; wq_start_date_00600; wq_end_date_00600; tn_years; tn_count; da_ratio; wrtds; wq_start_date_00665; wq_end_date_00665; tp_years; tp_count; file; summary_spreadsheet; site; constituent; model_prediction; low_thresh; high_thresh; model_category; eList; pcode; siteID; summaryChoice; notes; WQ_FLAG; FLUXM_FLAG; WRTDS_FLAG; CONUS_Load; IRB_Load; agency_cd; site_no; datetime; n_00060_0000m; n_00060_0000m_cd; cn_00060_0000m; period; nDays; fluxSeason; complete; model; drainSqKm; shortName; staAbbrev; paramShortName; constitAbbrev; param.units; bottomLogQ; stepLogQ; nVectorLogQ; bottomYear; stepYear; nVectorYear; windowY; windowQ; windowS; minNumObs; minNumUncen; numDays; DecLow; DecHigh; edgeAdjust","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/65454423d34ee4b6e05bff1e"}],"access_details":null,"bbox":{"east":-66.7969,"north":49.4967,"south":24.687,"west":-126.7383},"citation":"Goodling, P.J., Oelsner, G.P., Hecht, J.S., Cherry, M.L., Johnson, Z.C., Koenig, L. E., and Headman, A.O., 2024, Long-term water-quality trends for rivers and streams within the contiguous United States using Weighted Regressions on Time, Discharge, and Season (WRTDS): U.S. Geological Survey data release, https://doi.org/10.5066/P914BQYS.","creator":[],"creator_project":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"date_created":"5/7/2024","date_updated":"6/3/2026","description":"The U.S. Geological Survey (USGS) Water Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand. It is also improving understanding of the natural and human factors affecting the balance between supply and demand. A key part of these national assessments is identifying long-term trends in water availability, including groundwater and surface water quantity, quality, and use. To describe the long-term trends in the surface water quality component of water availability, data from the USGS and other Federal, State, and local agencies were accessed primarily through the US EPA's Water Quality Portal (https://www.waterqualitydata.us/) in 2023 and used in trend analyses.\u003cbr\u003eThis USGS data release contains all the input and output files necessary to reproduce the results from the Weighted Regressions on Time, Discharge, and Season (WRTDS) models, using data preparation methods described in Oelsner and others, 2017 for individual monitoring locations. Models were calibrated for each combination of site and parameter using the screened input data. Models were run on Tallgrass, the USGS supercomputer, in separate run for each parameter. Once calibrated, the WRTDS models were initially evaluated using a logistic regression equation that estimated a probability of acceptance for each model (e.g., \"a good fit\") based on a set of diagnostic metrics derived from the observed, estimated, and residual values from each model and data set (Murphy and Chanat, 2023). Each WRTDS model was assigned to one of three categories: “auto-accept,” “auto-reject,” or “manual evaluation\". Models assigned to the latter category were visually evaluated for appropriate model fit using residual and diagnostic plots. Models assigned to the first two categories were automatically included or rejected from the final results, respectively. Seven water-quality parameters were assessed, including nutrients (nitrate, filtered orthophosphate, total nitrogen, and total phosphorus), salinity indicators (chloride and specific conductance), and sediment (suspended sediment concentration). Trends are reported for three trend periods: 1980-2020, 2000-2020, and the longest period of record at each site.","doi_url":"https://doi.org/10.5066/P914BQYS","domain":["Hydrology","Water Quality","Stream Characteristics"],"draft":false,"id":"8d5e4e87-9ced-47ee-9b09-c61411e79978","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/65454423d34ee4b6e05bff1e?f=__disk__8b%2F67%2Fcc%2F8b67cccfa5a8e92a8e6f622eaf8d862fe7e4ec15\u0026allowOpen=true"}],"name":"Long-term water-quality trends for rivers and streams within the contiguous United States using Weighted Regressions on Time, Discharge, and Season (WRTDS)","permalink":"/catalog/datasets/8d5e4e87-9ced-47ee-9b09-c61411e79978/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"unknown","temporal_coverage":"1900 - 2023","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["site_id","harmonized_constituent_name_id","wq_flag","initial_40_flag","has_2plusyrs_quarterly_sampling_flag","trend_atleast_10yr_flag","trend_last_5yrs_flag","overall_sampling_freq_flag","gage_match_flag","q_data_match_flag","high_flow_freq_flag","rescreen_flag","uncensored_flag","number_obs_flag","end_date_flag","all_flags_passed","trend_period_start_wy","trend_period_end_wy","MonitoringLocationIdentifier","LatitudeMeasure","LongitudeMeasure","constituent_id","config_id","model_convergence","model_prediction","low_thresh","high_thresh","model_category_id","rater","rater_selection_id","rater_comment","wrtds_run_id","timestamp","primary","description","year_start","year_end","reject_c","p_val_c","est_c","low_c_90","up_c_90","low_c_50","up_c_50","low_c_95","up_c_95","like_c_up","like_c_down","reject_f","p_val_f","est_f","low_f_90","up_f_90","low_f_50","up_f_50","low_f_95","up_f_95","like_f_up","like_f_down","base_conc","base_flux","n_boot","start_seed","block_length","n_boot_good","year","discharge_cms","quantity_id","value","units_id","ci_low","ci_hi","total_change","cqtc","qtc","x10","x11","x20","x22","year1","year2","station_id","ComID","Source","MonitoringLocationName","ComID_alt","NIDID","DAM_NAME","YEAR_COMPL","jaflat","jaflon","ComID_1","LENGTHKM","LevelPathI","TotDASqKM","natqw_site_no","MonitoringLocationID","ss_id_final","NPDES","Lat_update","Long_updat","comid_num","site_no","Date","Discharge","Cd","start_year","end_year","record_length","water_year","ts_id","ts_qc_id","drain_area_sq_mi","contrib_area_sq_mi","gages_ii_class_id","gages_ii_hdi","hcdn","station_nm_flag"],"vars":"site_id; harmonized_constituent_name_id; wq_flag; initial_40_flag; has_2plusyrs_quarterly_sampling_flag; trend_atleast_10yr_flag; trend_last_5yrs_flag; overall_sampling_freq_flag; gage_match_flag; q_data_match_flag; high_flow_freq_flag; rescreen_flag; uncensored_flag; number_obs_flag; end_date_flag; all_flags_passed; trend_period_start_wy; trend_period_end_wy; MonitoringLocationIdentifier; LatitudeMeasure; LongitudeMeasure; constituent_id; config_id; model_convergence; model_prediction; low_thresh; high_thresh; model_category_id; rater; rater_selection_id; rater_comment; wrtds_run_id; timestamp; primary; description; year_start; year_end; reject_c; p_val_c; est_c; low_c_90; up_c_90; low_c_50; up_c_50; low_c_95; up_c_95; like_c_up; like_c_down; reject_f; p_val_f; est_f; low_f_90; up_f_90; low_f_50; up_f_50; low_f_95; up_f_95; like_f_up; like_f_down; base_conc; base_flux; n_boot; start_seed; block_length; n_boot_good; year; discharge_cms; quantity_id; value; units_id; ci_low; ci_hi; total_change; cqtc; qtc; x10; x11; x20; x22; year1; year2; station_id; ComID; Source; MonitoringLocationName; ComID_alt; NIDID; DAM_NAME; YEAR_COMPL; jaflat; jaflon; ComID_1; LENGTHKM; LevelPathI; TotDASqKM; natqw_site_no; MonitoringLocationID; ss_id_final; NPDES; Lat_update; Long_updat; comid_num; site_no; Date; Discharge; Cd; start_year; end_year; record_length; water_year; ts_id; ts_qc_id; drain_area_sq_mi; contrib_area_sq_mi; gages_ii_class_id; gages_ii_hdi; hcdn; station_nm_flag","weight":1},{"access":[{"file_format":"GPKG; GDB","name":"usgs.gov","url":"https://www.usgs.gov/national-hydrography/access-national-hydrography-products"}],"access_details":"The access link goes to Access National Hydrography Products where the WBD can be accessed under the heading Watershed Boundary Dataset (WBD) where there are two options; 1. \"Download the WBD by 2-digit Hydrologic Unit (HU2)\" and 2. \"Download the WBD by the Entire Nation\"","bbox":{"east":-60,"north":72,"south":14,"west":-180},"citation":null,"creator":[],"creator_project":[],"date_created":"1/10/2025","date_updated":"6/3/2026","description":"WBD 2024 Snapshot\u003cbr\u003eThe 2024 version of the WBD includes all of the WBD updates that were ingested into the national dataset between 2012-2024. This snapshot of the WBD is the official version from the USGS WBD program.\u003cbr\u003eWhat is the Watershed Boundary Dataset (WBD)\u003cbr\u003eThe Watershed Boundary Dataset (WBD) is a seamless, national, hydrologic unit dataset that provides a standardized base for water-resources organizations to locate, store, retrieve, and exchange hydrologic data; to index and inventory hydrologic data and information; to catalog water-data acquisition activities; and to use in a variety of other applications. Hydrologic unit boundaries in the WBD are determined based on topographic, hydrologic, and other relevant landscape characteristics without regard for administrative, political, or jurisdictional boundaries.\u003cbr\u003eThe hydrologic units (HU) in the WBD are arranged in a nested, hierarchical system with each HU in the system identified using a unique hydrologic unit code (HUC). Each HU within a nested level is assigned a two-digit suffix that’s appended to the HUC of the HU in the next coarsest nesting level. Because there are eight nesting levels within WBD, the set of HUCs consists of ranges from two to sixteen digits based on the eight levels of classification in the WBD. The dataset is complete for the United States to the 12-digit hydrologic unit. The 14- and 16-digit hydrologic units have only been published for a subset of the nation. https://www.usgs.gov/media/images/watershed-boundary-dataset-structure-visualization","doi_url":null,"domain":["Hydrology"],"draft":false,"id":"8dcb8081-66d3-4aff-8795-3d67e89a3162","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://water.usgs.gov/usgs/themes-internal/hydrologic-units/"}],"name":"WBD 2024 Snapshot","permalink":"/catalog/datasets/8dcb8081-66d3-4aff-8795-3d67e89a3162/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"1:24,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["objectid","tnmid","metasourceid","sourcedatadesc","sourceoriginator","sourcefeatureid","loaddate","referencegnis_ids","areaacres","areasqkm","states","huc2","huc4","huc6","huc8","huc10","huc12","huc14","huc16","name","hutype","humod","tohuc","noncontributingareaacres","noncontributingareasqkm","globalid","shape_Length","shape_Area","hutype_description"],"vars":"objectid; tnmid; metasourceid; sourcedatadesc; sourceoriginator; sourcefeatureid; loaddate; referencegnis_ids; areaacres; areasqkm; states; huc2; huc4; huc6; huc8; huc10; huc12; huc14; huc16; name; hutype; humod; tohuc; noncontributingareaacres; noncontributingareasqkm; globalid; shape_Length; shape_Area; hutype_description","weight":1},{"access":[{"file_format":"ZARR","name":"WMA STAC","url":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/stageiv_combined"}],"bbox":{"east":-59.95136642456055,"north":57.84757995605469,"south":19.803680419921875,"west":-134.04302978515625},"citation":"Blodgett, D.L., 2013, New service interface for River Forecasting Center derived quantitative precipitation estimates: U.S. Geological Fact Sheet 2013–3035, 2 p., https://pubs.usgs.gov/fs/2013/3035.","creator":[],"creator_project":[],"date_created":"7/24/2025","date_updated":"6/3/2026","description":"For more than a decade, the National Weather Service (NWS) River Forecast Centers (RFCs) have been estimating spatially distributed rainfall by applying quality-control procedures to radar-indicated rainfall estimates in the eastern United States and other best practices in the western United States to producea national Quantitative Precipitation Estimate (QPE) (National Weather Service, 2013). The availability of archives of QPE information for analytical purposes has been limited to manual requests for access to raw binary file formats that are difficult for scientists who are not in the climatic sciences to work with. The NWS provided the QPE archives to the U.S. Geological Survey (USGS), and the contents of the real-time feed from the RFCs are being saved by the USGS for incorporation into the archives. The USGS has applied  time-series aggregation and added latitude-longitude coordinate variables to publish the RFC QPE data. Web services provide users with direct (index-based) data access, rendered visualizations of the data, and resampled raster representations of the source data in common geographic information formats.","domain":["Climate"],"draft":false,"id":"8e72f0f7-f97c-4b39-8bcf-3a7ff1d772a8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[{"id":"148a7db7-1175-47aa-8437-5ae39f62b1ce","rel_type":"IsSourceOf"}],"links":[],"name":"United States Stage IV Quantitative Precipitation Archive","permalink":"/catalog/datasets/8e72f0f7-f97c-4b39-8bcf-3a7ff1d772a8/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"4 kilometer","temporal_coverage":"2002 - Present","temporal_frequency":"hourly","update_detail":"append","update_frequency":"4 hours","update_type":"Dynamic","variables":["Total_precipitation_surface_1_Hour_Accumulation"],"vars":"Total_precipitation_surface_1_Hour_Accumulation","weight":1},{"access":[{"file_format":"TIF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-hlss30-2.0"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., and Justice, C., 2021, HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0: distributed by NASA EOSDIS Land Processes DAAC, accessed [YYYY-MM-DD] at https://doi.org/10.5067/HLS/HLSS30.002","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2 to 3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.\u003cbr\u003eThe HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A and Sentinel-2B MSI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system, and thus are stackable for time series analysis.\u003cbr\u003eThe HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 product.","doi_url":"https://doi.org/10.5067/HLS/HLSS30.002","domain":["Soils"],"draft":false,"id":"9138147d-8f8a-4a32-8353-cfa11daa5c93","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf"}],"name":"Harmonized Landsat/Sentinel (HLS) S30 v002","permalink":"/catalog/datasets/9138147d-8f8a-4a32-8353-cfa11daa5c93/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"30 meter","temporal_coverage":"2015 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Coastal Aerosol","Blue","Green","Red","Red Edge1","Red Edge2","Red Edge3","NIR Broad","NIR Narrow","Water Vapor","Cirrus","SWIR1","SWIR2","Quality Bits","Sun Zenith Angle","Sun Azimuth Angle","View Zenith Angle","View Azimuth Angle"],"vars":"Coastal Aerosol; Blue; Green; Red; Red Edge1; Red Edge2; Red Edge3; NIR Broad; NIR Narrow; Water Vapor; Cirrus; SWIR1; SWIR2; Quality Bits; Sun Zenith Angle; Sun Azimuth Angle; View Zenith Angle; View Azimuth Angle","weight":1},{"access":[{"file_format":"TXT; SHP; ASCII","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5fe122a7d34e30b9123f02d9"}],"access_details":null,"bbox":{"east":-65.3455,"north":51.5753,"south":22.8753,"west":-127.8868},"citation":"Ransom, K.M., Nolan, B.T., Stackelberg, P.E., Belitz, K., Fram, M.S., Reddy, J.E., Johnson, T.D., and Rodriguez, O., 2021, Data for Machine Learning Predictions of Nitrate in Groundwater Used for Drinking Supply in the Conterminous United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9IPKWFL.","creator":[{"creator_email":"kransom@usgs.gov","creator_name":"Kathrine Ransom"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution for two drinking water zones, each of variable depth, one for domestic supply and one for public supply. The model used measured nitrate concentrations from 12,082 wells, and included predictor variables representing well characteristics, hydrologic conditions, soil type, geology, land use, climate, and nitrogen inputs. Predictor variables derived from empirical or numerical process-based models were also included to integrate information on controlling processes and conditions. This data release documents the model and provides the model results. The model and results are discussed in the associated journal article, Ransom and others (2021).\u003cbr\u003eIncluded in this data release are, 1) a model archive of the R project: source code, input files (including model training and hold-out data, rasters of all final predictor variables, and rasters representing domestic and public supply depth zones), and output files (output files are two rasters of predicted nitrate concentration at the depth zones typical of domestic and public supply wells), 2) a read_me file describing the model archive and an explanation of its use, and 3) tables describing model variables, model fit statistics, and model results [these tables are also included in the Supporting Information published with the journal article Ransom and others (2021)].","doi_url":"https://doi.org/10.5066/P9IPKWFL","domain":["Water Quality"],"draft":false,"id":"916fd5f1-330e-4a1c-b214-0b7069cf28b4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5fe122a7d34e30b9123f02d9?f=__disk__8b%2Fb7%2F74%2F8bb7749ff28278a42c56899ea883466922176cea\u0026transform=1\u0026allowOpen=true"},{"name":"Documentation","url":"https://doi.org/10.1016/j.scitotenv.2021.151065"}],"name":"Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States","permalink":"/catalog/datasets/916fd5f1-330e-4a1c-b214-0b7069cf28b4/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1 kilometer","temporal_coverage":"1988 - 2018","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Nitrate predictions"],"vars":"Nitrate predictions","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/65f86970d34e97daac9ff4c8"}],"access_details":null,"bbox":{"east":-64.3,"north":50.2,"south":24.2,"west":-126.2},"citation":"Luukkonen, C.L., Alzraiee, A.H., Larsen, J.D., Martin, D.J., Herbert, D.M., Buchwald, C.A., Houston, N.A., Valseth, K.J., Paulinski, S., Miller, L.D., Niswonger, R.G., Stewart, J.S., Dieter, C.A., and Miller, O.L., 2023, Public supply water use reanalysis for the 2000-2020 period by HUC12, month, and year for the conterminous United States: U.S. Geological Survey data release, (ver. 2.0, August 2024): https://doi.org/10.5066/P9FUL880.","creator":[],"creator_project":[{"id":"DJ50UY1","name":"Water Use Model Development"}],"date_created":"5/10/2024","date_updated":"6/3/2026","description":"The U.S. Geological Survey is developing national water-use models to support water resources management in the United States. Model benefits include a nationally consistent estimation approach, greater temporal and spatial resolution of estimates, efficient and automated updates of results, and capabilities to forecast water use into the future and assess model uncertainty. The term “reanalysis” refers to the process of reevaluating and recalculating water-use data using updated or refined methods, data sources, models, or assumptions. In this data release, water use refers to water that is withdrawn by public and private water suppliers and includes water provided for domestic, commercial, industrial, thermoelectric power, and public water uses, as well as water that is consumed or lost within the public supply system. Consumptive use refers to water withdrawn by the public supply system that is evaporated, transpired, incorporated into products or crops, or consumed by humans or livestock.\u003cbr\u003eThis data release contains data used in a machine learning model (child item 2) to estimate monthly water use for communities that are supplied by public-supply water systems in the conterminous United States for 2000-2020. This data release also contains associated scripts used to produce input features (child items 4 - 8) as well as model water use estimates by 12-digit hydrologic unit code (HUC12) and public supply water service area (WSA). HUC12 boundaries are in child item 3. Public supply delivery and consumptive use estimates are in child items 1 and 9, respectively.","doi_url":"https://doi.org/10.5066/P9FUL880","domain":["Water Use"],"draft":false,"id":"920112b8-ceca-465f-b1aa-cbcc09e10883","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/65f86970d34e97daac9ff4c8?f=__disk__63%2F1d%2Fd0%2F631dd0ddae5069456eb96a50f603cbccc9199058\u0026allowOpen=true"}],"name":"Public supply water use reanalysis for the 2000-2020 period by HUC12, month, and year for the conterminous United States (ver. 2.0, August 2024)","permalink":"/catalog/datasets/920112b8-ceca-465f-b1aa-cbcc09e10883/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2000 - 2020","temporal_frequency":"monthly; annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Year","Month","Estimated total public supply water use","Estimated public supply surface water use","Estimated public supply groundwater use"],"vars":"Year; Month; Estimated total public supply water use; Estimated public supply surface water use; Estimated public supply groundwater use","weight":1},{"access":[{"file_format":"SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/56a7f9dce4b0b28f1184dabd"}],"access_details":null,"bbox":{"east":-67,"north":71.2955,"south":20.0273,"west":-168},"citation":"Andrea Ostroff, Daniel Wieferich, Arthur Cooper, Dana Infante, and USGS Aquatic GAP Program, 2013, 2012 National Anthropogenic Barrier Dataset (NABD):  U.S. Geological Survey - Aquatic GAP Program: Denver, CO.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The main objective of this project was to develop a dataset of large, anthropogenic barriers that are spatially linked to the National Hydrography Dataset Plus Version 1 (NHDPlusV1) for the conterminous U.S. and the high resolution National Hydrography Dataset (NHD) for Alaska (1:63,000 scale) and Hawaii (1:24,000 scale) to facilitate GIS analyses based on the NHDPlusV1/NHD and NID datasets. To meet this objective, Michigan State University conducted a spatial linkage of the point dataset of the 2009 National Inventory of Dams (NID) created by the U.S. Army Corps of Engineers (USACE) to the NHDPlusV1/NHD. The pool of dam data included were modified based on 1) dam removals that occurred after development of the 2009 NID and 2) the identification of duplicate dam records along state boundaries (cases where more than one state reported the same dam). The US Geological Survey (USGS) Aquatic GAP Program supported this work.\u003cbr\u003eDams spatially linked to the NHDPlus/NHD offer an advantage to datasets of dams not linked to the NHDPlusV1/NHD because dams represented by points are located on flowlines representing stream and reservoir waterbodies. These qualities will allow managers and researchers to perform analyses and make more informed decisions when stream connectivity is considered in management plans and/or research projects. This will be the first spatially accurate dataset of its size and extent to be available for wide-spread use.","doi_url":null,"domain":["Infrastructure"],"draft":false,"id":"922683e5-7188-41e0-8e71-e38219731d37","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/56a7f9dce4b0b28f1184dabd?f=__disk__8c%2Feb%2F39%2F8ceb39ba4597d6da5c116276831b316eb498345b\u0026allowOpen=true"}],"name":"National Anthropogenic Barrier Dataset (NABD) 2012","permalink":"/catalog/datasets/922683e5-7188-41e0-8e71-e38219731d37/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; AK; HI","spatial_resolution":"1:100,000 (CONUS); 1:63,000 (AK); 1:24,000 (HI)","temporal_coverage":"2009","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Dam locations"],"vars":"Dam locations","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/66104abcd34e63346650643d"}],"access_details":null,"bbox":{"east":-66.709,"north":52.1605,"south":24.4868,"west":-125.6836},"citation":"Markstrom, S.L., Norton, P.A., Dickinson, J.E., LaFontaine, J.H., McDonald, R.R., and Regan, R.S., 2024, Application of the National Hydrologic Model Infrastructure (NHM) with the Precipitation-Runoff Modeling System (PRMS) and Geospatial Fabric version 1.1, 1980-2021, CONUS404BA: U.S. Geological Survey data release, https://doi.org/10.5066/P148FA7G.","creator":[{"creator_email":"jlafonta@usgs.gov","creator_name":"Jacob H LaFontaine"}],"creator_project":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data release contains inputs for and outputs from hydrologic simulations for the conterminous United States (CONUS) using the Precipitation Runoff Modeling System (PRMS) version 5.2.1 and the USGS National Hydrologic Model infrastructure (NHM, Regan and others, 2018). These simulations were developed to provide estimates of the water budget for the period 1980 to 2021 for one pre-calibration and three calibration configurations: 1) calibration by hydrologic response unit (byHRU), 2) calibration by select headwaters (byHW), and 3) calibration by select headwaters with streamflow observations (byHWobs). The four versions of model parameters and associated model output included in this data release are described in Hay and others (2023). The first three years of the simulations are considered “model initialization” and should not be included in any subsequent analysis.\u003cbr\u003e\n\u0026nbsp;\u003cbr\u003e\nModel input files, located on the “Input Data for Hydrologic Simulations of the CONUS using the NHM-PRMS version 1.1, CONUS404BA Calibration” child page, include ASCII formatted PRMS input files of 1) daily time step atmospheric forcings of minimum air temperature (tmin.zip), maximum air temperature (tmax.zip), and precipitation accumulation (precip.zip), 2) four parameter files for the different calibration versions (c404BA_precal_myparam.zip, c404BA_byHRU_myparam.zip, c404BA_byHW_myparam.zip, and c404BA_byHWobs_myparam.zip), 3) a PRMS control file that provides the simulation configuration information (NHM-PRMS_data_release.control), 4) and the PRMS data file that includes time series of streamflow observations (sf_data.zip). Descriptions of model input parameters are included in the parameters_data_dictionary.csv file on this main page. Descriptions of control file parameters are included in the control_data_dictionary.csv file on this main page. Additional information about the model calibration and associated parameters is provided in Hay and others (2023). Additional information about the bias-adjusted CONUS404 (CONUS404BA) atmospheric forcings used for the model application is provided in Zhang and others (2024).\u003cbr\u003e\n\u0026nbsp;\u003cbr\u003e\nModel output files, located on the “Output Data for Hydrologic Simulations of the CONUS using the NHM-PRMS version 1.1, CONUS404BA Calibration” child page, include 18 PRMS output variables for each of the four model simulations corresponding with the four input parameter files (pre-calibration, byHRU, byHW, and byHWobs). Each NetCDF format output file contains daily time step outputs for the period 1980-2021 for each hydrologic response unit or stream segment in the model application. Descriptions of model output variables are included in the output_variables_data_dictionary.csv file on this main page.\u003cbr\u003e\n\u0026nbsp;\u003cbr\u003e\nStreamflow statistics of model performance at selected streamgages are located on the “Simulated streamflow and statistics at streamgages for NHM CONUS CONUS404BA Calibrations, 1980-2021” child page. Each of the four model simulations has an associated csv file of streamflow statistics (gage_stats_c404-bc_\u0026lt;run\u0026gt;.csv) and a NetCDF file of daily streamflow at each streamgage (NHM-PRMS_data_release.nc). Descriptions of the streamflow files are included in the simulated_streamflow_data_dictionary.csv file on this main page.","doi_url":"https://doi.org/10.5066/P148FA7G","domain":["Hydrology","Snow","Soils"],"draft":false,"id":"93537c0d-2ce7-4f61-9465-3db8ab8c72be","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"f930b17a-c6ea-4623-b89d-d7d85ac698aa","rel_type":"IsDerivedFrom"}],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/66104abcd34e63346650643d?f=__disk__a6%2F51%2Ff6%2Fa651f6a5605fbe68b44755c07bb4908e23a09a9e\u0026allowOpen=true"}],"name":"Application of the National Hydrologic Model Infrastructure (NHM) with the Precipitation-Runoff Modeling System (PRMS) and Geospatial Fabric version 1.1, 1980-2021, CONUS404BA","permalink":"/catalog/datasets/93537c0d-2ce7-4f61-9465-3db8ab8c72be/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1980 - 2021","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["variable_name","datatype","description","default","parameter_name","datatype","units","description","valid_minimum","valid_maximum","default","dimension","modules","variable_name","datatype","description","units","dimension","poi_id","ns","nslog","mon_ns","mon_nslog","bias","mon_bias","da_actual_obs","drainage_area_model","falcone_class","latitude","longitude","variable_name","datatype","long_name","units","dimensions"],"vars":"variable_name; datatype; description; default; parameter_name; datatype; units; description; valid_minimum; valid_maximum; default; dimension; modules; variable_name; datatype; description; units; dimension; poi_id; ns; nslog; mon_ns; mon_nslog; bias; mon_bias; da_actual_obs; drainage_area_model; falcone_class; latitude; longitude; variable_name; datatype; long_name; units; dimensions","weight":1},{"access":[{"file_format":"GRIB; NC","name":"confluence.ecmwf.int","url":"https://confluence.ecmwf.int/display/CKB/How+to+download+ERA5"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horsnyi, A., Munoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Holm, E., Janiskova, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., Thepaut, J., 2020, The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, v. 146, no. 730, p. 1999-2049, https://doi.org/10.1002/qj.3803","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"ERA5 provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 30km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.  Quality-assured monthly updates of ERA5 (1979 to present) are published within 3 months of real time. Preliminary daily updates of the dataset are available to users within 5 days of real time.","doi_url":"https://doi.org/10.1002/qj.3803","domain":["Climate"],"draft":false,"id":"935a940c-46bb-4d59-83d9-a542b8bceab8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation"}],"name":"ECMWF Reanalysis v5 (ERA5)","permalink":"/catalog/datasets/935a940c-46bb-4d59-83d9-a542b8bceab8/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"ECMWF","spatial_extent":"Global","spatial_resolution":"31 kilometer","temporal_coverage":"1979 - Present","temporal_frequency":"hourly","update_detail":"append","update_frequency":"hourly","update_type":"Dynamic","variables":["ERA5 climate forcings"],"vars":"ERA5 climate forcings","weight":1},{"access":[{"file_format":"GDB","name":"epa.gov","url":"https://www.epa.gov/waterdata/get-nhdplus-national-hydrography-dataset-plus-data"}],"access_details":"The epa.gov access link goes to Access National Hydrography Products where the WBD NHDPlus V2 WBD snapshot (a component of NHDPlus V2) can be accessed under the heading National Hydrography Dataset (NHD).","bbox":{"east":-62,"north":52,"south":-13,"west":-180},"citation":null,"creator":[],"creator_project":[],"date_created":"2/27/2025","date_updated":"6/3/2026","description":"NHDPlus V2 WBD Snapshot\u003cbr\u003eSeveral snapshots of the WBD were used in the production of the NHDPlus V2 data as the process took multiple years to complete. For the conterminous United States, nine different WBD snapshots were used, dated from August 2010-February 2012. Between 2012 and 2016, NHDPlus data was developed for the state of Hawaii and U.S. territories including Puerto Rico, the Virgin Islands, Guam, Saipan, and America Samoa, using WBD snapshots dated from February 2012- February 2016. NHDPlus V2 data is not available for Alaska.\u003cbr\u003eTwo key components of the WBD were used in the development of NHDPlus V2. The 4-digit hydrologic units were used as the vector processing units, or outer extent, for production of the data while the 12-digit hydrologic units were used in the pre-processing of the raster datasets as built-in walls forcing the flow across the landscape to be contained within the hydrologic units where appropriate.\u003cbr\u003eWhat is the Watershed Boundary Dataset (WBD)\u003cbr\u003eThe Watershed Boundary Dataset (WBD) is a seamless, national, hydrologic unit dataset that provides a standardized base for water-resources organizations to locate, store, retrieve, and exchange hydrologic data; to index and inventory hydrologic data and information; to catalog water-data acquisition activities; and to use in a variety of other applications. Hydrologic unit boundaries in the WBD are determined based on topographic, hydrologic, and other relevant landscape characteristics without regard for administrative, political, or jurisdictional boundaries.\u003cbr\u003eThe hydrologic units (HU) in the WBD are arranged in a nested, hierarchical system with each HU in the system identified using a unique hydrologic unit code (HUC). Each HU within a nested level is assigned a two-digit suffix that’s appended to the HUC of the HU in the next coarsest nesting level. Because there are eight nesting levels within WBD, the set of HUCs consists of ranges from two to sixteen digits based on the eight levels of classification in the WBD. The dataset is complete for the United States to the 12-digit hydrologic unit. The 14- and 16-digit hydrologic units have only been published for a subset of the nation. https://www.usgs.gov/media/images/watershed-boundary-dataset-structure-visualization","doi_url":null,"domain":["Hydrology"],"draft":false,"id":"94678add-1b4b-48b6-99f5-5557b09406d3","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://water.usgs.gov/usgs/themes-internal/hydrologic-units/"}],"name":"WBD NHDPlus V2 Snapshot","permalink":"/catalog/datasets/94678add-1b4b-48b6-99f5-5557b09406d3/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; America Samoa; Guam; Puerto Rico; Northern Mariana Islands; Virgin Islands","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["OBJECTID_1","OBJECTID","HUC_8","HUC_10","HUC_12","ACRES","NCONTRB_A","HU_10_GNIS","HU_12_GNIS","HU_10_DS","HU_10_NAME","HU_10_MOD","HU_10_TYPE","HU_12_DS","HU_12_NAME","HU_12_MOD","HU_12_TYPE","META_ID","STATES","GlobalID","SHAPE_Leng","GAZ_ID","WBD_Date","VPUID","Shape_Length","Shape_Area","AreaHUC12"],"vars":"OBJECTID_1; OBJECTID; HUC_8; HUC_10; HUC_12; ACRES; NCONTRB_A; HU_10_GNIS; HU_12_GNIS; HU_10_DS; HU_10_NAME; HU_10_MOD; HU_10_TYPE; HU_12_DS; HU_12_NAME; HU_12_MOD; HU_12_TYPE; META_ID; STATES; GlobalID; SHAPE_Leng; GAZ_ID; WBD_Date; VPUID; Shape_Length; Shape_Area; AreaHUC12","weight":1},{"access":[{"file_format":"SHP","name":"gaftp.epa.gov","url":"https://gaftp.epa.gov/EPADataCommons/ORD/Ecoregions/us/us_eco_l3.zip"}],"access_details":null,"bbox":{"east":-62,"north":72,"south":14,"west":-180},"citation":"U.S. Environmental Protection Agency, 2013, Level III and IV ecoregions of the continental United States: U.S. EPA National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:7,500,000. accessed [YYYY-MM-DD] at https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2005). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989).\u003cbr\u003e\u003cbr\u003eLiterature cited:\u003cbr\u003eCommission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p.\u003cbr\u003eGallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p.\u003cbr\u003eOmernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62\u003cbr\u003eOmernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103.\u003cbr\u003eOmernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38.\u003cbr\u003eU.S. Environmental Protection Agency. 2013. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:7,500,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states.","doi_url":null,"domain":["Ecosystems"],"draft":false,"id":"948401a6-e41f-4139-9b25-6c62f1e4a961","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://gaftp.epa.gov/EPADataCommons/ORD/Ecoregions/us/Eco_Level_III_US.html"},{"name":"Documentation","url":"https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states"}],"name":"Level III Ecoregions of the Continental United States","permalink":"/catalog/datasets/948401a6-e41f-4139-9b25-6c62f1e4a961/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"EPA","spatial_extent":"CONUS","spatial_resolution":"1:7,500,000","temporal_coverage":"2013","temporal_frequency":"NA","update_detail":"modify","update_frequency":"irregular","update_type":"Dynamic","variables":["Coast Range","Columbia Plateau","Blue Mountains","Snake River Plain","Central Basin and Range","Mojave Basin and Range","Northern Rockies","Idaho Batholith","Middle Rockies","Wyoming Basin","Wasatch and Uinta Mountains","Puget Lowland","Colorado Plateaus","Southern Rockies","Arizona/New Mexico Plateau","Arizona/New Mexico Mountains","Chihuahuan Deserts","High Plains","Southwestern Tablelands","Central Great Plains","Flint Hills","Cross Timbers","Willamette Valley","Edwards Plateau","Southern Texas Plains","Texas Blackland Prairies","East Central Texas Plains","Western Gulf Coastal Plain","South Central Plains","Ouachita Mountains","Arkansas Valley","Boston Mountains","Ozark Highlands","Cascades","Central Irregular Plains","Canadian Rockies","Northwestern Glaciated Plains","Northwestern Great Plains","Nebraska Sand Hills","Piedmont","Northern Glaciated Plains","Western Corn Belt Plains","Lake Agassiz Plain","Northern Minnesota Wetlands","Sierra Nevada","Northern Lakes and Forests","North Central Hardwood Forests","Driftless Area","Southeastern Wisconsin Till Plains","Central Corn Belt Plains","Eastern Corn Belt Plains","Southern Michigan/Northern Indiana Drift Plains","Huron/Erie Lake Plains","Northeastern Highlands","Northeastern Coastal Zone","Central California Foothills and Coastal Mountains","Northern Allegheny Plateau","Erie Drift Plain","North Central Appalachians","Middle Atlantic Coastal Plain","Northern Piedmont","Southeastern Plains","Blue Ridge","Ridge and Valley","Southwestern Appalachians","Central Appalachians","Central California Valley","Western Allegheny Plateau","Interior Plateau","Interior River Valleys and Hills","Mississippi Alluvial Plain","Mississippi Valley Loess Plains","Southern Coastal Plain","Southern Florida Coastal Plain","North Cascades","Klamath Mountains/California High North Coast Range","Madrean Archipelago","Southern California Mountains","Northern Basin and Range","Sonoran Basin and Range","Acadian Plains and Hills","Eastern Great Lakes Lowlands","Atlantic Coastal Pine Barrens","Southern California/Northern Baja Coast","Eastern Cascades Slopes and Foothills"],"vars":"Coast Range; Columbia Plateau; Blue Mountains; Snake River Plain; Central Basin and Range; Mojave Basin and Range; Northern Rockies; Idaho Batholith; Middle Rockies; Wyoming Basin; Wasatch and Uinta Mountains; Puget Lowland; Colorado Plateaus; Southern Rockies; Arizona/New Mexico Plateau; Arizona/New Mexico Mountains; Chihuahuan Deserts; High Plains; Southwestern Tablelands; Central Great Plains; Flint Hills; Cross Timbers; Willamette Valley; Edwards Plateau; Southern Texas Plains; Texas Blackland Prairies; East Central Texas Plains; Western Gulf Coastal Plain; South Central Plains; Ouachita Mountains; Arkansas Valley; Boston Mountains; Ozark Highlands; Cascades; Central Irregular Plains; Canadian Rockies; Northwestern Glaciated Plains; Northwestern Great Plains; Nebraska Sand Hills; Piedmont; Northern Glaciated Plains; Western Corn Belt Plains; Lake Agassiz Plain; Northern Minnesota Wetlands; Sierra Nevada; Northern Lakes and Forests; North Central Hardwood Forests; Driftless Area; Southeastern Wisconsin Till Plains; Central Corn Belt Plains; Eastern Corn Belt Plains; Southern Michigan/Northern Indiana Drift Plains; Huron/Erie Lake Plains; Northeastern Highlands; Northeastern Coastal Zone; Central California Foothills and Coastal Mountains; Northern Allegheny Plateau; Erie Drift Plain; North Central Appalachians; Middle Atlantic Coastal Plain; Northern Piedmont; Southeastern Plains; Blue Ridge; Ridge and Valley; Southwestern Appalachians; Central Appalachians; Central California Valley; Western Allegheny Plateau; Interior Plateau; Interior River Valleys and Hills; Mississippi Alluvial Plain; Mississippi Valley Loess Plains; Southern Coastal Plain; Southern Florida Coastal Plain; North Cascades; Klamath Mountains/California High North Coast Range; Madrean Archipelago; Southern California Mountains; Northern Basin and Range; Sonoran Basin and Range; Acadian Plains and Hills; Eastern Great Lakes Lowlands; Atlantic Coastal Pine Barrens; Southern California/Northern Baja Coast; Eastern Cascades Slopes and Foothills","weight":1},{"access":[{"file_format":"NC","name":"nsidc.org","url":"https://nsidc.org/data/nsidc-0719/versions/1#anchor-2"}],"access_details":null,"bbox":{"east":-66.5,"north":50,"south":24,"west":-127},"citation":"Broxton, P., Zeng, X., and Dawson, N., 2019. Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US, Version 1. [Indicate subset used]. Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center, https://doi.org/10.5067/0GGPB220EX6A","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data set provides daily 4 km snow water equivalent (SWE) and snow depth over the conterminous United States from 1981 to 2021, developed at the University of Arizona (UA) under the support of the NASA MAP and SMAP Programs. The data were created by assimilating in-situ snow measurements from the National Resources Conservation Service's SNOTEL network and the National Weather Service's COOP network with modeled, gridded temperature and precipitation data from PRISM.","doi_url":"https://doi.org/10.5067/0GGPB220EX6A","domain":["Snow","Hydrology"],"draft":false,"id":"94bd88a6-b4a6-4599-9a67-b7427136d430","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://nsidc.org/sites/default/files/nsidc-0719-v001-userguide_1.pdf"}],"name":"Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US, Version 1","permalink":"/catalog/datasets/94bd88a6-b4a6-4599-9a67-b7427136d430/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NSIDC","spatial_extent":"CONUS","spatial_resolution":"4 kilometer","temporal_coverage":"1981 - 2021","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Snow Depth","Snow Water Equivalent"],"vars":"Snow Depth; Snow Water Equivalent","weight":1},{"access":[{"file_format":"GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/537a6a24e4b0efa8af08154a"}],"access_details":null,"bbox":{"east":-63.21,"north":50.741871378,"south":17.1,"west":-161.31},"citation":"Viger, R.J., 2014, Geospatial Fabric Attribute Tables for PRMS Topographic Parameters based on NHD high-res(Preliminary), U.S. Geological Survey; https://dx.doi.org/doi:10.5066/F7445JHG","creator":[{"creator_email":"rviger@usgs.gov","creator_name":"Roland Viger"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset contains a set of attributes describing the \"nhru\" GIS features (Hydrologic Response Units)in the Geospatial Fabric Features dataset(http://dx.doi.org/doi:10.5066/F7542KMD) that have been developed in support of the USGS PRMS watershed model. These tables are organized according to Geospatial Fabric Region; see the thumbnail of the Geospatial Fabric Features Regions (https://www.sciencebase.gov/catalog/item/535edb4ae4b08e65d60fc837). Each table contains a key field, \"hru_id\", that can be used to relate to the nhru feature class in the Geospatial Fabric Feature dataset for the corresponding Region. The methodologies used to derive the individual attributes can be located in the Appendix of the GIS Weasel Users Manual by the name of the attribute, which is the same as the name of the corresponding PRMS parameter, in (Viger and Leavesley, 2007). The metadata for each table within the current container identifies any ancillary datasets used to produce the table fields. For nhru instances that are partially or entirely beyond the borders of the United States, supporting GIS data was generally lacking. Where the value for a field could not be determined, values derived at the border were spatially extended to these areas to support derivation of a value. Users may want to review and modify the field values for these HRUs. Viger, R.J., and Leavesley, G.H., 2007, The GIS Weasel user's manual: U.S. Geological Survey Techniques and Methods, book 6, chap. B4, 201 p.","doi_url":"https://doi.org/10.5066/F7445JHG","domain":["Hydrology"],"draft":false,"id":"951f052d-063c-442a-ae58-b0b9a93754d8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/537a6a24e4b0efa8af08154a?f=__disk__71%2F12%2Fcc%2F7112ccb4a97838f61ee6cc4608b970413d6f2fde\u0026transform=1\u0026allowOpen=true"}],"name":"Topographic data for the Geospatial Fabric v1.0","permalink":"/catalog/datasets/951f052d-063c-442a-ae58-b0b9a93754d8/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; HI; PR","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Topographic parameters"],"vars":"Topographic parameters","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/64f8d595d34ed30c20546a56"}],"access_details":null,"bbox":{"east":179.77,"north":71.3889,"south":18.4656,"west":-178.5938},"citation":"Headman, A.O. and Hecht, J.S. 2024. Identifying post-reservoir construction periods for monotonic trend analysis at streamgages in the United States (ver. 2.0, October 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P922P61Z.","creator":[],"creator_project":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"date_created":"5/8/2024","date_updated":"6/3/2026","description":"The U.S. Geological Survey (USGS) Water Resources Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand and an understanding of the natural and human factors affecting the balance between supply and demand. A key part of these national assessments is identifying long-term trends in water availability, including groundwater and surface water quantity, quality, and use. An understanding of the impacts of reservoirs on water availability is essential for this assessment.\u003cbr\u003eThis data release contains decadal time series of cumulative reservoir storage capacity upstream of U.S. Geological Survey streamgages in the National Water Information System (NWIS) conducted as part of the U.S. Geological Survey's Integrated Water Availability Assessment (IWAAs) Trends \u0026 Drivers project. These data were used to identify post-reservoir construction periods of record at these streamgages for Mann-Kendall gradual trend analyses of streamflow and stream temperature records contingent upon a subsequent screening for post-reservoir construction record completeness\u003cbr\u003eThis data release builds on a prior data release containing cumulative upstream reservoir storage capacity data and flow-regulation metrics (Wieczorek et al., 2018) (https://doi.org/10.5066/F7765D7V ), the National Inventory of Dams (for dam construction dates and other information about individual dams), and the IWAAs Trends and Drivers database. In particular, the Wieczorek et al. (2018) data release provides decadal cumulative storage capacity upstream of streamgages from 1930 to 2010 as well as additional information for 2013.\u003cbr\u003eWe combine this decadal time series information with completion dates of individual dams to identify the last year during which a major change in reservoir storage took place. Abrupt changes in reservoir storage are identified using storage ratios, which we define as the ratio of cumulative upstream reservoir normal storage capacity to mean annual streamflow between 1980 and 2020.  Decades during which the storage ratio increases by more than 10% of mean annual streamflow are identified. Next, to determine the final year of dam construction, we examine the years of completion for all dams constructed upstream of a streamgage in a given decade.  Any individual dam which increases the storage ratio by more than 5% of mean annual streamflow, then the last year in which a dam producing such an increase is considered the end of the construction period and the post-reservoir discharge record starts the following year.  Sites that did not experience an increase in storage ratio greater than 10% in a single decade but had cumulative increase above 10% were flagged but records were not truncated. Records at streamgages that already had a storage ratio above 10% in 1930, the first year for which decadal cumulative upstream storage capacity data from Wieczorek et al. (2018) are available, but did not exhibit any subsequent decadal increase in storage ratio above 0.10 were truncated to start in 1930.\u003cbr\u003eThree key assumptions underpin this analysis:\u003cbr\u003e1. Reservoirs are assumed to have been filled within a year after dam construction was completed and, thus, the start of the year of the post-reservoir construction trend period is one year after the year of completion. This assumption may not reflect the multi-year filling periods of many large reservoirs in regions with high interannual precipitation variability.\u003cbr\u003e2. Identification of a single post-reservoir construction period implicitly assumes that reservoir operating policies remained consistent throughout this period, with no subsequent changes in storage following dam construction.\u003cbr\u003e3. The flows at gages downstream of dams between 1980 and 2020 are assumed to be unaffected by any changes in streamflow due to dams or changes in climate and other environmental conditions. Given that dams tend to reduce mean annual streamflow through forced evaporation and water withdrawals, we assume dam impacts lower the naturally occurring mean annual streamflow. Consequently, such storage ratios will become higher if dam impacts lower mean annual flows, making our storage ratio estimates used to identify dam-impacted sites conservatively high.","doi_url":"https://doi.org/10.5066/P922P61Z","domain":["Hydrology","Water Quality"],"draft":false,"id":"953f3172-a3bb-4801-b491-374bc4633d87","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/64f8d595d34ed30c20546a56?f=__disk__9c%2Fa1%2F3a%2F9ca13acd59adb3b1b4040b7bd7577ae6fa215c8e\u0026allowOpen=true"}],"name":"Identifying post-reservoir construction periods for monotonic trend analysis at streamgages in the United States (ver. 2.0, October 2024)","permalink":"/catalog/datasets/953f3172-a3bb-4801-b491-374bc4633d87/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"unknown","temporal_coverage":"1930 - 2013","temporal_frequency":"10 years","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["site_id","decade","storage_ratio","cum_normal_storage","nidid","poi_type_id","year_last_big_change","start_year_mod","flag_storage_change","comid","major_dam_flag","com_dam_flag","da_nwis_sqmi","dam_name","year_complete","da_nid_sqmi","normal_storage","dam_comid","latitude","longitude"],"vars":"site_id; decade; storage_ratio; cum_normal_storage; nidid; poi_type_id; year_last_big_change; start_year_mod; flag_storage_change; comid; major_dam_flag; com_dam_flag; da_nwis_sqmi; dam_name; year_complete; da_nid_sqmi; normal_storage; dam_comid; latitude; longitude","weight":1},{"access":[{"file_format":"TXT; PARQUET","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5669a79ee4b08895842a1d47"}],"access_details":"For the sciencebase.gov access point, accessing specific attributes can be challenging due to hundreds of attributes or child items being nested under the parent data release. To address this issue, a web application was developed to surface the commonly accessed data in a more dynamic way. For more details, please see **Additional Information: Web App** below to be directed to the web application.","bbox":{"east":-65.3278,"north":51.5679,"south":23.2443,"west":-127.8904},"citation":"Wieczorek, M.E., Jackson, S.E., and Schwarz, G.E., 2018, Select Attributes for NHDPlus Version 2.1 Reach Catchments and Modified Network Routed Upstream Watersheds for the Conterminous United States (ver. 4.0, August 2023): U.S. Geological Survey data release, https://doi.org/10.5066/F7765D7V","creator":[{"creator_email":"mewieczo@usgs.gov","creator_name":"Michael Wieczorek"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This is a series of data sets of natural and anthropogenic landscape features linked to NHDPlus Version 2.1’s (NHDPlusV2) approximately 2.7 million stream segments, their associated catchments, and their upstream watersheds within the conterminous United States. The data were linked to four spatial components of NHDPlusV2: 1) individual reach catchments, 2) riparian buffer zones around individual reaches, 3) reach catchments accumulated downstream through the river network, and 4) riparian buffer zones accumulated downstream through the river network. All data can be linked to NHDPlus using the COMID field in these tables and the ComID in the flowline shapefiles or FEATUREID in the catchment ones in the NHDPlus data suite. The datasets were derived using a topologically reconditioned version of the NHDPlusv2 routing network (Schwarz and Wieczorek, 2018). This database is used for the routing of upstream watersheds only.  No cartographic changes were made to the original NHDPlusv2 in either the flowline or reach catchment line work. These data are organized under 13 themes listed as variables above.\u003cbr\u003eThese data allow researchers and managers to acquire landscape information for both catchments (for example, the nearby landscape flowing directly into streams) and full upstream watersheds of specific stream reaches anywhere in the within the conterminous United States without having to perform specialized geospatial processing. Aside from comma separated text files, parquet files with the same file structure were also added to each data file under each child item theme. This format will allow researchers to acquire all the information from this data release in an efficient and consistent manner by utilizing and thereby adhering to the FAIR guidelines outlined in Lightsom and others (USGS, 2022).","doi_url":"https://doi.org/10.5066/F7765D7V","domain":["Land Cover","Hydrology"],"draft":false,"id":"9605348d-b8ce-429e-8d8f-4465e4ad327e","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[{"id":"7fedb00b-cc7b-40f6-b835-3dc57b41488f","rel_type":"IsSourceOf"},{"id":"c7f2109c-f75e-4c98-9b1b-765923a48978","rel_type":"IsSourceOf"}],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5669a79ee4b08895842a1d47?f=__disk__16%2F43%2F54%2F16435460d128a78bad61f6ee66ed277666177836\u0026allowOpen=true"},{"name":"Web App","url":"https://water.usgs.gov/themes/catchment-attributes/"}],"name":"Select Attributes for NHDPlus Version 2.1 Reach Catchments and Modified Network Routed Upstream Watersheds for the Conterminous United States (ver. 4.0, August 2023)","permalink":"/catalog/datasets/9605348d-b8ce-429e-8d8f-4465e4ad327e/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1870 - 2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Best Management Practices","Chemical characteristics","Climate and Water Balance Model characteristics","Climate characteristics","Geologic characteristics","Hydrologic characteristics","Hydrologic Modification characteristics","Landscape characteristics","Population and infrastructure characteristics","Regional characteristics","Soil characteristics","Topographic characteristics","Water use characteristics"],"vars":"Best Management Practices; Chemical characteristics; Climate and Water Balance Model characteristics; Climate characteristics; Geologic characteristics; Hydrologic characteristics; Hydrologic Modification characteristics; Landscape characteristics; Population and infrastructure characteristics; Regional characteristics; Soil characteristics; Topographic characteristics; Water use characteristics","weight":1},{"access":[{"file_format":"IMG","name":"daac.ornl.gov","url":"https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=565"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Global Soil Data Task, 2014, Global Soil Data Products CD-ROM Contents (IGBP-DIS), [insert dataset], Available online [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A., http://dx.doi.org/10.3334/ORNLDAAC/565.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset contains global data on soil properties, global maps of soil distributions, and the SoilData System developed by the International Geosphere-Biosphere Program Data and Information System (IGBP-DIS). These data were originally distributed on CD-ROM, but are provided here as a single zip file. The SoilData System allows users to generate soil information and maps for geographic regions at soil depths and resolutions selected by the user. Derived surfaces of carbon density, nutrient status, water-holding capacity, and heat capacity are provided for modeling and inventory purposes.","doi_url":"https://doi.org/10.3334/ORNLDAAC/565","domain":["Soils"],"draft":false,"id":"980d7571-89cf-4845-9ad3-9156616fefeb","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://daac.ornl.gov/SOILS/guides/IGBP-DIS.html"}],"name":"Global Soil Data Products CD-ROM Contents (IGBP-DIS)","permalink":"/catalog/datasets/980d7571-89cf-4845-9ad3-9156616fefeb/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"250 meter","temporal_coverage":"1995 - 1996","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Available water capacity"],"vars":"Available water capacity","weight":1},{"access":[{"file_format":"NC","name":"ncei.noaa.gov","url":"https://www.ncei.noaa.gov/products/climate-data-records/avhrr-surface-reflectance"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Vermote, E., NOAA CDR Program, 2019, NOAA Climate Data Record (CDR) of AVHRR Surface Reflectance, Version 5. [indicate subset used]. NOAA National Centers for Environmental Information, accessed [YYYY-MM-DD], https://doi.org/10.7289/V53776Z4","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset contains gridded daily surface reflectance and brightness temperatures derived from the Advanced Very High Resolution Radiometer (AVHRR) sensors onboard eight NOAA polar orbiting satellites: NOAA-7, -9, -11, -14, -16, -17, -18 and -19. Surface reflectance from AVHRR channels 1 and 2 (at 640 and 860 nm) are a NOAA Climate Data Record (CDR). The dataset spans from 1981 to 10 days before the present, and was processed from the AVHRR Global Area Coverage (GAC) Level 1b dataset. AVHRR GAC observations are packaged into data arrays with latitude and longitude dimensions of 3600 x 7200 covering the globe at 0.05 degree spatial resolution. This dataset is one of the Land Surface CDR Version 5 products produced by the NASA Goddard Space Flight Center (GSFC) and the University of Maryland (UMD). Other Land Surface CDR products include the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Scientific improvements for Version 5 include updating to higher resolution ancillary data and more accurate approaches for BRDF correction, calibration, compositing, and QA. Version 5 also corrects the data for known errors in time, latitude, and longitude variables, as well as improves the global and variable attribute definitions. The dataset is in the netCDF-4 file format following ACDD and CF Conventions. The dataset is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.","doi_url":"https://doi.org/10.7289/V53776Z4","domain":["Climate"],"draft":false,"id":"9a04ec50-72ad-4903-b8ef-b7c9d1da5350","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"AVHRR v5","permalink":"/catalog/datasets/9a04ec50-72ad-4903-b8ef-b7c9d1da5350/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NOAA","spatial_extent":"Global","spatial_resolution":"0.05 degrees","temporal_coverage":"1981 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Land-surface reflectance"],"vars":"Land-surface reflectance","weight":1},{"access":[{"file_format":"CSV; NC; TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6411fd40d34eb496d1cdc99d"}],"access_details":null,"bbox":{"east":-66.4453,"north":49.3251,"south":23.9662,"west":-125.8594},"citation":"Sampson, K., Dugger, A., Rafieeinasab, A., Zhang, Y., LaFontaine, J.H., Foks, S.S., LaMotte, A.E., and Viger, R.J., 2024, Monthly twelve-digit hydrologic unit code aggregations of the WRF-Hydro modeling application with CONUS404BA Atmospheric Forcings, 2009-2021: U.S. Geological Survey data release, https://doi.org/10.5066/P13QA5KX.\n","creator":[],"creator_project":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"date_created":"5/3/2024","date_updated":"6/3/2026","description":"This data release contains key variables from the Weather Research and Forecasting Hydrological Modeling (WRF-Hydro) application forced with the CONUS404 climate forcing variable subset for hydrologic models that was downscaled to one-kilometer and bias-adjusted for precipitation and temperature (CONUS404BA) from water year 2010 through water year 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit (HUC12) code for the spatial extent of the conterminous United States.\u003cbr\u003e\n\u0026nbsp;\n\u003cp\u003eFirst release: 2024\u003c/p\u003e\n\n\u003cp\u003eRevised: July 2025 (ver. 2.0)\u003c/p\u003e\n","doi_url":"https://doi.org/10.5066/P13QA5KX","domain":["Hydrology"],"draft":false,"id":"9ceb4994-f7f4-42c1-9e2b-b3a74f5d3b58","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"f930b17a-c6ea-4623-b89d-d7d85ac698aa","rel_type":"IsDerivedFrom"}],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6411fd40d34eb496d1cdc99d?f=__disk__3a%2F5f%2F42%2F3a5f42094927c609071768e5b921efca9267944b\u0026allowOpen=true"}],"name":"Monthly twelve-digit hydrologic unit code aggregations of the WRF-Hydro modeling application with CONUS404BA Atmospheric Forcings, 2009-2021 (ver. 2.0, July 2025)","permalink":"/catalog/datasets/9ceb4994-f7f4-42c1-9e2b-b3a74f5d3b58/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2009 - 2021","temporal_frequency":"monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["huc_id","time","yrmo","CatchmentArea","LandFraction","total_gridded_area","avgSOILM_wltadj_depthmean","avgSOILSAT_wltadj_top1","Baseflow","ET","GWStore","Precip","PrecipLand","Recharge","Snowfall","SoilSat","SoilWater","Surfaceflow","SWE","Totalflow","band","x","y","band","x","y","ID","HUC12_FL","HUC12_CA","HUC12_Area","Catchment_Area","Catchment_SUM"],"vars":"huc_id; time; yrmo; CatchmentArea; LandFraction; total_gridded_area; avgSOILM_wltadj_depthmean; avgSOILSAT_wltadj_top1; Baseflow; ET; GWStore; Precip; PrecipLand; Recharge; Snowfall; SoilSat; SoilWater; Surfaceflow; SWE; Totalflow; band; x; y; band; x; y; ID; HUC12_FL; HUC12_CA; HUC12_Area; Catchment_Area; Catchment_SUM","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6659df6dd34ef3137d36a348"}],"access_details":null,"bbox":{"east":-66.0187,"north":52.8807,"south":24.3953,"west":-124.9022},"citation":"Martinez, A.J., Zemmels, J.R., and Padilla, J.A., 2024, Intersectional weights between two different 12-digit Hydrologic Unit Code 12(HUC12) boundaries: U.S. Geological Survey data release, https://doi.org/10.5066/P1UANON8.","creator":[],"creator_project":[{"id":"DJ50UY1","name":"Water Use Model Development"}],"date_created":"10/31/2024","date_updated":"6/3/2026","description":"This data release contains fractional intersectional weights used to crosswalk data from the National Water Use Program to the National Integrated Water Availability Assessment (IWAAs) projects. The Watershed Boundary Dataset (WBD; https://www.usgs.gov/national-hydrography/watershed-boundary-dataset) is a companion dataset to the National Hydrography Dataset and contains polygons that define the spatial boundaries of hydrologic units (i.e., the area of land the landscape that drains into a portion of the stream network). These are periodically updated as these boundary definitions are refined by incorporating better, more localized data. When aggregating data from multiple sources that rely on data from the WBD, a situation can occur where different datasets rely on different versions (or “snapshots”) of the WBD. This was the case for the IWAAs National Report which relied upon data using a version of the WBD found in the Mainstem Rivers data release (https://doi.org/10.5066/P92U7ZUT) as well as data that relied upon a version used by the National Water Use Program (https://doi.org/10.5066/P9FUL880). This dataset is the output of a pipeline of R code published as a software release (https://doi.org/10.5066/P1UANON8) and contains the fraction of spatial overlap (i.e., weights) between the subwatershed (HUC12) boundaries from these two versions of the WBD. These weights can be used as a crosswalk between the two snapshots of the WBD.","doi_url":"https://doi.org/10.5066/P1UANON8","domain":["Water Use","Hydrology"],"draft":false,"id":"9fb3d759-2c28-4408-b2ff-eb12461eded0","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6659df6dd34ef3137d36a348?f=__disk__2d%2F59%2F65%2F2d59651c11053ea13777a75b5b499998cb4b03b2\u0026allowOpen=true"}],"name":"Intersectional weights between two different 12-digit Hydrologic Unit Code 12(HUC12) boundaries","permalink":"/catalog/datasets/9fb3d759-2c28-4408-b2ff-eb12461eded0/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["wu_huc12","ms_huc12","weight","areasqkm"],"vars":"wu_huc12; ms_huc12; weight; areasqkm","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/55f6e42ae4b0477df11bff10"}],"access_details":null,"bbox":{"east":-78.641881702,"north":49.386795524,"south":35.252808022,"west":-105.086914166},"citation":"Nakagaki, N. and Wieczorek, M.E., 2016, Estimates of subsurface tile drainage extent for 12 Midwest states, 2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7W37TDP.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset is a 30-meter resolution raster of estimated extent of subsurface tile drains, developed from tabular data of state-level estimates of agricultural land drained by tiles combined with geospatial cropland and soils in 12 Midwest States (SD, NE, KS, MN, IA, MO, WI, IL, MI, IN, OH, and KY). This dataset was created from the following four sources: 1) state-level acreages of agricultural \"land drained by tiles\" from the 2012 Census of Agriculture; 2) the extent of cultivated cropland from the National Land Cover Dataset (NLCD) 2011; 3) the extent of poorly and moderately drained soils from the State Soil Geographic Database (STATSGO) database Version 2; and 4) state administrative boundaries. The area of drained land was evenly allocated to potentially drained land for agriculture - cropland with poorly drained soil - except in Iowa. For Iowa, because the reported area of land drained by tiles exceeded the area of cropland on poorly drained soils, the additional area of subsurface tile drains greater than the area of cropland on poorly drained soils was assigned to land characterized as cropland with moderately drained soil. The estimated extent of subsurface tile drains in each cell is expressed in square meters.","doi_url":"https://doi.org/10.5066/F7W37TDP","domain":["Hydrology"],"draft":false,"id":"a127b7fe-a027-4abd-9d86-f4f50fd35549","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/55f6e42ae4b0477df11bff10?f=__disk__7f%2F60%2Fd5%2F7f60d583544bc37e87bd37a221d5e315fa151984\u0026allowOpen=true"}],"name":"Estimates of subsurface tile drainage extent for 12 Midwest states, 2012","permalink":"/catalog/datasets/a127b7fe-a027-4abd-9d86-f4f50fd35549/","project_use_history":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"SD; NE; KS; MN; IA; MO; WI; IL; MI; IN; OH; KY","spatial_resolution":"30 meter","temporal_coverage":"2012","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Estimated area of subsurface tile drainage extent"],"vars":"Estimated area of subsurface tile drainage extent","weight":1},{"access":[],"access_details":"User will need to contact author (Gleeson, tgleeson@eos.ubc.ca)","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Gleeson, T., Smith, L., Moosdorf, N., Hartmann, J., Durr, H.H., Manning, A.H., van Beek, L.P.H., and Jellinek, A.M., 2011, Mapping permeability over the surface of the Earth, Geophys. Res. Lett., 38, L02401, https://doi.org/10.1029/2010GL045565.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Permeability, the ease of fluid flow through porous rocks and soils, is a fundamental but often poorly quantified component in the analysis of regional-scale water fluxes. Permeability is difficult to quantify because it varies over more than 13 orders of magnitude and is heterogeneous and dependent on flow direction. Indeed, at the regional scale, maps of permeability only exist for soil to depths of 1-2 m. Here an extensive compilation of results from hydrogeologic models was used to show that regional-scale (\u003e5 km) permeability of consolidated and unconsolidated geologic units below soil horizons (hydrolithologies) can be characterized in a statistically meaningful way. The representative permeabilities of these hydrolithologies are used to map the distribution of near-surface (on the order of 100 m depth) permeability globally and over North America. The distribution of each hydrolithology is generally scale independent. The near-surface mean permeability is of the order of ~5 x 10-14 m2. The results provide the first global picture of near-surface permeability and will be of particular value for evaluating global water resources and modeling the influence of climate-surface-subsurface interactions on global climate change.\u003cbr\u003eBy combining recent lithology maps with a compilation of near-surface permeability values it is possible to: 1) resolve the heterogeneity of permeability into permeability values that represent specific geological materials and 2) map permeability at new scales and to greater depths than an approach based on soil classification. The permeability maps and estimates will likely be most useful for large-scale earth system models and/or for estimation of permeability in regions. Please contact Gleeson for high-resolution data.","doi_url":"https://doi.org/10.1029/2010GL045565","domain":["Soils"],"draft":false,"id":"a18da56d-4273-45a3-a4d3-a81a9964c186","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2010GL045565"}],"name":"Near-surface permeability","permalink":"/catalog/datasets/a18da56d-4273-45a3-a4d3-a81a9964c186/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"Global","spatial_resolution":"1 kilometer","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Near surface permeability"],"vars":"Near surface permeability","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/56c49126e4b0946c65219231"}],"access_details":null,"bbox":{"east":-65,"north":50,"south":24,"west":-127},"citation":"Reitz, M., W.E. Sanford, G. Senay, and J. Cazenas, 2017, Annual estimates of recharge, quick-flow runoff, and ET for the contiguous US using empirical regression equations, 2000-2013: U.S. Geological Survey data release, https://doi.org/10.5066/F7PN93P0","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset includes 800m resolution long-term average estimates of the distribution of incoming precipitation and groundwater-sourced irrigation into contributions to the evapotranspiration (ET), quick-flow runoff, and effective recharge water budget components over the 2000-2013 time period, as well as annual estimates for the individual years. It also includes other data supporting the methods development that produced these maps, corresponding to the figures in the associated journal article. The estimates were developed with new empirical regressions for (1) quick-flow runoff data generated from a USGS-developed hydrograph separation program (PART) for 1434 gages as a function of surficial geology type (USGS), precipitation (PRISM), and soil hydraulic conductivity (STATSGO); and (2) long-term evapotranspiration from water balance estimates at 679 gages as a function of land cover (NLCD), precipitation (PRISM), and maximum, minimum, and mean daily temperature (PRISM), and a separate estimate for ET over open water. The recharge quantites close the water budget to sum to the total influx from precipitation. Irrigated water quantities reported in the 2005 USGS Water Use dataset are incorporated as effective additional precipitation. Estimates are included for both effective recharge, which reflects the quantity of water available to replenish the groundwater table, and total recharge, which also includes an estimate for the recharge quantity that was subsequently intercepted by riparian vegetation and converted to evapotranspiration. Methods are fully described in the associated accepted publication; this dataset will be updated with its complete citation when published.","doi_url":"https://doi.org/10.5066/F7PN93P0","domain":["Hydrology"],"draft":false,"id":"a2b3dd55-0422-4d6c-8df9-3fabfe8c4a46","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Annual estimates of recharge, quick-flow runoff, and ET for the contiguous US using empirical regression equations, 2000-2013","permalink":"/catalog/datasets/a2b3dd55-0422-4d6c-8df9-3fabfe8c4a46/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"800 meter","temporal_coverage":"2000 - 2013","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Recharge, annual average","Quick-flow runoff","Evapotranspiration"],"vars":"Recharge, annual average; Quick-flow runoff; Evapotranspiration","weight":1},{"access":[{"file_format":"GDB; GPKG","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/58f9243fe4b0b7ea54522a15"}],"bbox":{"east":-71.7188,"north":49.3824,"south":24.8466,"west":-125.1563},"citation":"Ierardi, M.C., 2025, Watershed Boundary Dataset 12-digit HUCs of the Regional Integrated Methods for Base Evaluation (RIMBE) of Five Integrated Water Science Study Areas (DRB, IRB, TSJRB, UCOL and WRB): U. S. Geological Survey data release, https://doi.org/10.5066/P1T3BEE2.","creator":[],"creator_project":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"date_created":"3/5/2026","date_updated":"6/3/2026","description":"These data files have information for hydrologic features contained in the Watershed Boundary Dataset (WBD) feature datasets. The datasets include information about the 12-digit HUC polygons for the USGS Regional Integrated Methods for Base Evaluation (RIMBE) of five Integrated Water Science (IWS) study areas: Delaware River Basin (DRB), Illinois River Basin (IRB), Trinity-San Jacinto River Basin (TSJRB), Upper Colorado River Basin (UCOL) and Willamette River Basin (WRB) subwatersheds for 2020 and 2025, along with their respective boundary areas. Users accessing the datasets via the geodatabase or geopackage formats can search for and compare the attributes related to each specific dataset for 2020 and 2025. The data downloads contain 2020 and 2025 versions of the five IWS study areas in geopackage and geodatabase formats with their associated watershed boundaries, incremental HUC's and overall basin polygons for comparison. Over this time period, discrepancies have been identified between the study areas showing different hydrologic attributes, AreaAcres, AreaSqKm, HUC12, names, ToHUC, and other geospatial features further identified in the Process Description of the metadata.","doi_url":"https://doi.org/10.5066/P1T3BEE2","domain":["Hydrology"],"draft":false,"id":"a43e220a-61cd-4b72-8433-07f6b0e36c42","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/58f9243fe4b0b7ea54522a15?f=__disk__00%2F12%2F4a%2F00124a897343f77ac09fa1c2d030fe7e246fae57\u0026allowOpen=true"}],"name":"Watershed Boundary Dataset 12-digit HUCs of the Regional Integrated Methods for Base Evaluation (RIMBE) of Five Integrated Water Science Study Areas (DRB, IRB, TSJRB, UCOL and WRB)","permalink":"/catalog/datasets/a43e220a-61cd-4b72-8433-07f6b0e36c42/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["ObjectID","Shape","Shape_Length","Shape_Area","TNMID","MetaSourceID","SourceDataDesc","SourceOriginator","SourceFeatureID","LoadDate","ReferenceGNIS_IDs","AreaAcres","AreaSqKm","States","HUC12","Name","HUType","HUMod","ToHUC","NonContributingAreaAcres","NonContributingAreaSqKm","GlobalIDs","ObjecID","Shape","Shape_Length","Shape_Area","TNMID","MetaSourceID","SourceDataDesc","SourceOriginator","SourceFeatureID","LoadDate","ReferenceGNIS_IDs","AreaAcres","AreaSqKm","States","HUC12","Name","HUType","HUMod","ToHUC","NonContributingAreaAcres","NonContributingAreaSqKm","Global_IDs","ObjectID","Shape","Shape_Length","Shape_Area","TNMID","MetaSourceID","SourceDataDesc","SourceOriginator","SourceFeatureID","LoadDate","ReferenceGNIS_IDs","AreaAcres","AreaSqKm","States","HUC12","Name","HUType","HUMod","ToHUC","NonContributingAreaAcres","NonContributingAreaSqKm","GlobalIDs","ObjectID","Shape","Shape_Length","Shape_Area","TNMID","MetaSourceID","SourceDataDesc","SourceOriginator","SourceFeatureID","LoadDate","ReferenceGNIS_IDs","AreaAcres","AreaSqKm","States","HUC12","Name","HUType","HUMod","ToHUC","NonContributingAreaAcres","NonContributingAreaSqKm","GlobalIDs","ObjectID","Shape","Shape_Length","Shape_Area","TNMID","MetaSourceID","SourceDataDesc","SourceOriginator","SourceFeattureID","LoadDate","ReferenceGNIS_IDs","AreaAcres","AreaSqKm","States","HUC12","Name","HUType","HUMod","ToHUC","NonContributingAreaAcres","NonContributingSqKm","GlobalIDs","OBJECTID","SHAPE","Shape_Length","Shape_Area","ID","AREAKM2","OBJECTID","Shape","Shape_Length","Shape_Area","ID","AREAKM2","OBJECTID","Shape","Shape_Length","Shape_Area","ID","AREAKM2","OBJECTID","Shape","Shape_Length","Shape_Area","ID","AREAKM2","OBJECTID","Shape","Shape_Length","Shape_Area","ID","AREAKM2","ObjectID","Shape","Shape_Length","Shape_Area","TNMID","MetaSourceID","SourceDataDesc","SourceOriginator","SourceFeatureID","LoadDate","ReferenceGNIS_IDs","AreaAcres","AreaSqKm","States","HUC12","Name","HUType","HUMod","ToHUC","NonContributingAreaAcres","NonContributingAreaSqKm","Global_IDs","ObjectID","Shape","Shape_Length","Shape_Area","TNMID","MetaSourceID","SourceDataDesc","SourceOriginator","SourceFeatureID","LoadDate","ReferenceGNIS_IDs","AreaAcres","AreaSqKm","States","HUC12","Name","HUType","HUMod","ToHUC","NonContributingAreaAcres","NonContributingAreaSqKm","Global_IDs","ObjectID","Shape","Shape_Length","Shape_Area","TNMID","MetaSourceID","SourceDataDesc","SourceOriginator","SourceFeatureID","LoadDate","ReferenceGNIS_IDs","AreaAcres","AreaSqKm","States","HUC12","Name","HUType","HUMod","ToHUC","NonContributingAreaAcres","NonContributingAreaSqKm","Global_IDs","ObjectID","Shape","Shape_Length","Shape_Area","TNMID","MetaSourceID","SourceDataDesc","SourceOriginator","SourceFeatureID","LoadDate","ReferenceGNIS_IDs","AreaAcres","AreaSqKm","States","HUC12","Name","HUType","HUMod","ToHUC","NonContributingAreaAcres","NonContributingAreaSqKm","Global_IDs","ObjectID","Shape","Shape_Length","Shape_Area","TNMID","MetaSourceID","SourceDataDesc","SourceOriginator","SourceFeatureID","LoadDate","ReferenceGNIS_IDs","AreaAcres","AreaSqKm","States","HUC12","Name","HUType","HUMod","ToHUC","NonContributingAreaAcres","NonContributingSqKm","Global_IDs","OBJECTID","Shape","Shape_Length","Shape_Area","ID","AREAKM2","OBJECTID","Shape","Shape_Length","Shape_Area","ID","AREAKM2","OBJECTID","Shape","Shape_Length","Shape_Area","ID","AREAKM2","OBJECTID","Shape","Shape_Length","Shape_Area","ID","AREAKM2","OBJECTID","Shape","Shape_Length","Shape_Area","ID","AREAKM2"],"vars":"ObjectID; Shape; Shape_Length; Shape_Area; TNMID; MetaSourceID; SourceDataDesc; SourceOriginator; SourceFeatureID; LoadDate; ReferenceGNIS_IDs; AreaAcres; AreaSqKm; States; HUC12; Name; HUType; HUMod; ToHUC; NonContributingAreaAcres; NonContributingAreaSqKm; GlobalIDs; ObjecID; Shape; Shape_Length; Shape_Area; TNMID; MetaSourceID; SourceDataDesc; SourceOriginator; SourceFeatureID; LoadDate; ReferenceGNIS_IDs; AreaAcres; AreaSqKm; States; HUC12; Name; HUType; HUMod; ToHUC; NonContributingAreaAcres; NonContributingAreaSqKm; Global_IDs; ObjectID; Shape; Shape_Length; Shape_Area; TNMID; MetaSourceID; SourceDataDesc; SourceOriginator; SourceFeatureID; LoadDate; ReferenceGNIS_IDs; AreaAcres; AreaSqKm; States; HUC12; Name; HUType; HUMod; ToHUC; NonContributingAreaAcres; NonContributingAreaSqKm; GlobalIDs; ObjectID; Shape; Shape_Length; Shape_Area; TNMID; MetaSourceID; SourceDataDesc; SourceOriginator; SourceFeatureID; LoadDate; ReferenceGNIS_IDs; AreaAcres; AreaSqKm; States; HUC12; Name; HUType; HUMod; ToHUC; NonContributingAreaAcres; NonContributingAreaSqKm; GlobalIDs; ObjectID; Shape; Shape_Length; Shape_Area; TNMID; MetaSourceID; SourceDataDesc; SourceOriginator; SourceFeattureID; LoadDate; ReferenceGNIS_IDs; AreaAcres; AreaSqKm; States; HUC12; Name; HUType; HUMod; ToHUC; NonContributingAreaAcres; NonContributingSqKm; GlobalIDs; OBJECTID; SHAPE; Shape_Length; Shape_Area; ID; AREAKM2; OBJECTID; Shape; Shape_Length; Shape_Area; ID; AREAKM2; OBJECTID; Shape; Shape_Length; Shape_Area; ID; AREAKM2; OBJECTID; Shape; Shape_Length; Shape_Area; ID; AREAKM2; OBJECTID; Shape; Shape_Length; Shape_Area; ID; AREAKM2; ObjectID; Shape; Shape_Length; Shape_Area; TNMID; MetaSourceID; SourceDataDesc; SourceOriginator; SourceFeatureID; LoadDate; ReferenceGNIS_IDs; AreaAcres; AreaSqKm; States; HUC12; Name; HUType; HUMod; ToHUC; NonContributingAreaAcres; NonContributingAreaSqKm; Global_IDs; ObjectID; Shape; Shape_Length; Shape_Area; TNMID; MetaSourceID; SourceDataDesc; SourceOriginator; SourceFeatureID; LoadDate; ReferenceGNIS_IDs; AreaAcres; AreaSqKm; States; HUC12; Name; HUType; HUMod; ToHUC; NonContributingAreaAcres; NonContributingAreaSqKm; Global_IDs; ObjectID; Shape; Shape_Length; Shape_Area; TNMID; MetaSourceID; SourceDataDesc; SourceOriginator; SourceFeatureID; LoadDate; ReferenceGNIS_IDs; AreaAcres; AreaSqKm; States; HUC12; Name; HUType; HUMod; ToHUC; NonContributingAreaAcres; NonContributingAreaSqKm; Global_IDs; ObjectID; Shape; Shape_Length; Shape_Area; TNMID; MetaSourceID; SourceDataDesc; SourceOriginator; SourceFeatureID; LoadDate; ReferenceGNIS_IDs; AreaAcres; AreaSqKm; States; HUC12; Name; HUType; HUMod; ToHUC; NonContributingAreaAcres; NonContributingAreaSqKm; Global_IDs; ObjectID; Shape; Shape_Length; Shape_Area; TNMID; MetaSourceID; SourceDataDesc; SourceOriginator; SourceFeatureID; LoadDate; ReferenceGNIS_IDs; AreaAcres; AreaSqKm; States; HUC12; Name; HUType; HUMod; ToHUC; NonContributingAreaAcres; NonContributingSqKm; Global_IDs; OBJECTID; Shape; Shape_Length; Shape_Area; ID; AREAKM2; OBJECTID; Shape; Shape_Length; Shape_Area; ID; AREAKM2; OBJECTID; Shape; Shape_Length; Shape_Area; ID; AREAKM2; OBJECTID; Shape; Shape_Length; Shape_Area; ID; AREAKM2; OBJECTID; Shape; Shape_Length; Shape_Area; ID; AREAKM2","weight":1},{"access":[{"file_format":"TXT","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/61c2096dd34e2ca389d9e704"}],"bbox":{"east":-105.6,"north":43.5,"south":35.5,"west":-112},"citation":"Miller, M.P, Buto, S.G., Lambert, P.M., and Rumsey, C.A., 2022, SPARROW model input datasets and predictions of total dissolved solids loads in streams of the Upper Colorado River Basin watershed: U.S. Geological Survey data release, https://doi.org/10.5066/P9LO3JV2.","creator":[{"creator_email":"mamiller@usgs.gov","creator_name":"Matthew P Miller"}],"creator_project":[],"date_created":"2/24/2026","date_updated":"6/3/2026","description":"This data release contains bootstrap values of mean-annual total dissolved solids (TDS) loads predicted by a SPARROW model for individual stream reaches in the Upper Colorado River Basin watershed in the predict.txt file. Also included are the input variables required to execute the model, including dissolved solids sources, landscape characteristics, and calibration data from water quality monitoring stations in the input.txt file. Further details on model construction and results are described in Miller et al. (2017, https://doi.org/10.3133/sir20175009).","doi_url":"https://doi.org/10.5066/P9LO3JV2","domain":["Hydrology","Water Quality"],"draft":false,"id":"a44cc927-0a25-4a6f-ba42-1e5022eed567","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/61c2096dd34e2ca389d9e704?f=__disk__a9%2F02%2Fe5%2Fa902e554c5d8269181d383848145e5866909bbe9\u0026allowOpen=true"}],"name":"SPARROW model input datasets and predictions of total dissolved solids loads in streams of the Upper Colorado River Basin watershed","permalink":"/catalog/datasets/a44cc927-0a25-4a6f-ba42-1e5022eed567/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"Upper Colorado River Basin watershed","spatial_resolution":"unknown","temporal_coverage":"1984 - 2012","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["HYDSEQ","WATERID","Lat","Lon","FNODE","TNODE","DEMIAREA_mi2","DEMTAREA_mi2","IFTRAN","FRAC","frac_description","Target","WT","HEADFLAG","HUC4","RCHTYPE","HLOAD","meanq_cfs","rchtot_s","USGS_STAID","STAID","Station_Name","Mean_Daily_Q_cfs","Watershed_Area_km2","TDS_Load_ton_yr","Mean_Q_adjusted_TDS_Conc_mg_L","Detrended","imports_ton","import_description","Mean_elev_ft","MeanCatchmentSlope","hzthk_AllHrz_mean_in","MEAN_FE","pctRange","Floodoth_mi2","FloodMz_mi2","Sprink_mi2","BCM_AET_in","BCM_Precip_in","BCM_Precip_AET_in","vol_crys_mi2","Cen_hi_mi2","Cen_lo_mi2","Mes_hi_mi2","Mes_lo_mi2","Paleo_hi_mi2","Paleo_lo_mi2","WATERID","PLOAD_TOTAL","PLOAD_IMPORTS_TON","PLOAD_VOL_CRYS","PLOAD_CEN_HI","PLOAD_CEN_LO","PLOAD_MES_HI","PLOAD_MES_LO","PLOAD_PALEO_HI","PLOAD_PALEO_LO","PLOAD_SPRINK_MI2","PLOAD_FLOODOTH_MI2","PLOAD_FLOODMZ_MI2","PLOAD_INC_TOTAL","PLOAD_INC_IMPORTS_TON","PLOAD_INC_VOL_CRYS","PLOAD_INC_CEN_HI","PLOAD_INC_CEN_LO","PLOAD_INC_MES_HI","PLOAD_INC_MES_LO","PLOAD_INC_PALEO_HI","PLOAD_INC_PALEO_LO","PLOAD_INC_SPRINK_MI2","PLOAD_INC_FLOODOTH_MI2","PLOAD_INC_FLOODMZ_MI2","DEL_FRAC","MEAN_PLOAD_TOTAL","SE_PLOAD_TOTAL","ci_lo_PLOAD_TOTAL","ci_hi_PLOAD_TOTAL","MEAN_PLOAD_IMPORTS_TON","SE_PLOAD_IMPORTS_TON","ci_lo_PLOAD_IMPORTS_TON","ci_hi_PLOAD_IMPORTS_TON","MEAN_PLOAD_VOL_CRYS","SE_PLOAD_VOL_CRYS","ci_lo_PLOAD_VOL_CRYS","ci_hi_PLOAD_VOL_CRYS","MEAN_PLOAD_CEN_HI","SE_PLOAD_CEN_HI","ci_lo_PLOAD_CEN_HI","ci_hi_PLOAD_CEN_HI","MEAN_PLOAD_CEN_LO","SE_PLOAD_CEN_LO","ci_lo_PLOAD_CEN_LO","ci_hi_PLOAD_CEN_LO","MEAN_PLOAD_MES_HI","SE_PLOAD_MES_HI","ci_lo_PLOAD_MES_HI","ci_hi_PLOAD_MES_HI","MEAN_PLOAD_MES_LO","SE_PLOAD_MES_LO","ci_lo_PLOAD_MES_LO","ci_hi_PLOAD_MES_LO","MEAN_PLOAD_PALEO_HI","SE_PLOAD_PALEO_HI","ci_lo_PLOAD_PALEO_HI","ci_hi_PLOAD_PALEO_HI","MEAN_PLOAD_PALEO_LO","SE_PLOAD_PALEO_LO","ci_lo_PLOAD_PALEO_LO","ci_hi_PLOAD_PALEO_LO","MEAN_PLOAD_SPRINK_MI2","SE_PLOAD_SPRINK_MI2","ci_lo_PLOAD_SPRINK_MI2","ci_hi_PLOAD_SPRINK_MI2","MEAN_PLOAD_FLOODOTH_MI2","SE_PLOAD_FLOODOTH_MI2","ci_lo_PLOAD_FLOODOTH_MI2","ci_hi_PLOAD_FLOODOTH_MI2","MEAN_PLOAD_FLOODMZ_MI2","SE_PLOAD_FLOODMZ_MI2","ci_lo_PLOAD_FLOODMZ_MI2","ci_hi_PLOAD_FLOODMZ_MI2","MEAN_PLOAD_INC_TOTAL","SE_PLOAD_INC_TOTAL","ci_lo_PLOAD_INC_TOTAL","ci_hi_PLOAD_INC_TOTAL","MEAN_PLOAD_INC_IMPORTS_TON","SE_PLOAD_INC_IMPORTS_TON","ci_lo_PLOAD_INC_IMPORTS_TON","ci_hi_PLOAD_INC_IMPORTS_TON","MEAN_PLOAD_INC_VOL_CRYS","SE_PLOAD_INC_VOL_CRYS","ci_lo_PLOAD_INC_VOL_CRYS","ci_hi_PLOAD_INC_VOL_CRYS","MEAN_PLOAD_INC_CEN_HI","SE_PLOAD_INC_CEN_HI","ci_lo_PLOAD_INC_CEN_HI","ci_hi_PLOAD_INC_CEN_HI","MEAN_PLOAD_INC_CEN_LO","SE_PLOAD_INC_CEN_LO","ci_lo_PLOAD_INC_CEN_LO","ci_hi_PLOAD_INC_CEN_LO","MEAN_PLOAD_INC_MES_HI","SE_PLOAD_INC_MES_HI","ci_lo_PLOAD_INC_MES_HI","ci_hi_PLOAD_INC_MES_HI","MEAN_PLOAD_INC_MES_LO","SE_PLOAD_INC_MES_LO","ci_lo_PLOAD_INC_MES_LO","ci_hi_PLOAD_INC_MES_LO","MEAN_PLOAD_INC_PALEO_HI","SE_PLOAD_INC_PALEO_HI","ci_lo_PLOAD_INC_PALEO_HI","ci_hi_PLOAD_INC_PALEO_HI","MEAN_PLOAD_INC_PALEO_LO","SE_PLOAD_INC_PALEO_LO","ci_lo_PLOAD_INC_PALEO_LO","ci_hi_PLOAD_INC_PALEO_LO","MEAN_PLOAD_INC_SPRINK_MI2","SE_PLOAD_INC_SPRINK_MI2","ci_lo_PLOAD_INC_SPRINK_MI2","ci_hi_PLOAD_INC_SPRINK_MI2","MEAN_PLOAD_INC_FLOODOTH_MI2","SE_PLOAD_INC_FLOODOTH_MI2","ci_lo_PLOAD_INC_FLOODOTH_MI2","ci_hi_PLOAD_INC_FLOODOTH_MI2","MEAN_PLOAD_INC_FLOODMZ_MI2","SE_PLOAD_INC_FLOODMZ_MI2","ci_lo_PLOAD_INC_FLOODMZ_MI2","ci_hi_PLOAD_INC_FLOODMZ_MI2","MEAN_DEL_FRAC","SE_DEL_FRAC","ci_lo_DEL_FRAC","ci_hi_DEL_FRAC"],"vars":"HYDSEQ; WATERID; Lat; Lon; FNODE; TNODE; DEMIAREA_mi2; DEMTAREA_mi2; IFTRAN; FRAC; frac_description; Target; WT; HEADFLAG; HUC4; RCHTYPE; HLOAD; meanq_cfs; rchtot_s; USGS_STAID; STAID; Station_Name; Mean_Daily_Q_cfs; Watershed_Area_km2; TDS_Load_ton_yr; Mean_Q_adjusted_TDS_Conc_mg_L; Detrended; imports_ton; import_description; Mean_elev_ft; MeanCatchmentSlope; hzthk_AllHrz_mean_in; MEAN_FE; pctRange; Floodoth_mi2; FloodMz_mi2; Sprink_mi2; BCM_AET_in; BCM_Precip_in; BCM_Precip_AET_in; vol_crys_mi2; Cen_hi_mi2; Cen_lo_mi2; Mes_hi_mi2; Mes_lo_mi2; Paleo_hi_mi2; Paleo_lo_mi2; WATERID; PLOAD_TOTAL; PLOAD_IMPORTS_TON; PLOAD_VOL_CRYS; PLOAD_CEN_HI; PLOAD_CEN_LO; PLOAD_MES_HI; PLOAD_MES_LO; PLOAD_PALEO_HI; PLOAD_PALEO_LO; PLOAD_SPRINK_MI2; PLOAD_FLOODOTH_MI2; PLOAD_FLOODMZ_MI2; PLOAD_INC_TOTAL; PLOAD_INC_IMPORTS_TON; PLOAD_INC_VOL_CRYS; PLOAD_INC_CEN_HI; PLOAD_INC_CEN_LO; PLOAD_INC_MES_HI; PLOAD_INC_MES_LO; PLOAD_INC_PALEO_HI; PLOAD_INC_PALEO_LO; PLOAD_INC_SPRINK_MI2; PLOAD_INC_FLOODOTH_MI2; PLOAD_INC_FLOODMZ_MI2; DEL_FRAC; MEAN_PLOAD_TOTAL; SE_PLOAD_TOTAL; ci_lo_PLOAD_TOTAL; ci_hi_PLOAD_TOTAL; MEAN_PLOAD_IMPORTS_TON; SE_PLOAD_IMPORTS_TON; ci_lo_PLOAD_IMPORTS_TON; ci_hi_PLOAD_IMPORTS_TON; MEAN_PLOAD_VOL_CRYS; SE_PLOAD_VOL_CRYS; ci_lo_PLOAD_VOL_CRYS; ci_hi_PLOAD_VOL_CRYS; MEAN_PLOAD_CEN_HI; SE_PLOAD_CEN_HI; ci_lo_PLOAD_CEN_HI; ci_hi_PLOAD_CEN_HI; MEAN_PLOAD_CEN_LO; SE_PLOAD_CEN_LO; ci_lo_PLOAD_CEN_LO; ci_hi_PLOAD_CEN_LO; MEAN_PLOAD_MES_HI; SE_PLOAD_MES_HI; ci_lo_PLOAD_MES_HI; ci_hi_PLOAD_MES_HI; MEAN_PLOAD_MES_LO; SE_PLOAD_MES_LO; ci_lo_PLOAD_MES_LO; ci_hi_PLOAD_MES_LO; MEAN_PLOAD_PALEO_HI; SE_PLOAD_PALEO_HI; ci_lo_PLOAD_PALEO_HI; ci_hi_PLOAD_PALEO_HI; MEAN_PLOAD_PALEO_LO; SE_PLOAD_PALEO_LO; ci_lo_PLOAD_PALEO_LO; ci_hi_PLOAD_PALEO_LO; MEAN_PLOAD_SPRINK_MI2; SE_PLOAD_SPRINK_MI2; ci_lo_PLOAD_SPRINK_MI2; ci_hi_PLOAD_SPRINK_MI2; MEAN_PLOAD_FLOODOTH_MI2; SE_PLOAD_FLOODOTH_MI2; ci_lo_PLOAD_FLOODOTH_MI2; ci_hi_PLOAD_FLOODOTH_MI2; MEAN_PLOAD_FLOODMZ_MI2; SE_PLOAD_FLOODMZ_MI2; ci_lo_PLOAD_FLOODMZ_MI2; ci_hi_PLOAD_FLOODMZ_MI2; MEAN_PLOAD_INC_TOTAL; SE_PLOAD_INC_TOTAL; ci_lo_PLOAD_INC_TOTAL; ci_hi_PLOAD_INC_TOTAL; MEAN_PLOAD_INC_IMPORTS_TON; SE_PLOAD_INC_IMPORTS_TON; ci_lo_PLOAD_INC_IMPORTS_TON; ci_hi_PLOAD_INC_IMPORTS_TON; MEAN_PLOAD_INC_VOL_CRYS; SE_PLOAD_INC_VOL_CRYS; ci_lo_PLOAD_INC_VOL_CRYS; ci_hi_PLOAD_INC_VOL_CRYS; MEAN_PLOAD_INC_CEN_HI; SE_PLOAD_INC_CEN_HI; ci_lo_PLOAD_INC_CEN_HI; ci_hi_PLOAD_INC_CEN_HI; MEAN_PLOAD_INC_CEN_LO; SE_PLOAD_INC_CEN_LO; ci_lo_PLOAD_INC_CEN_LO; ci_hi_PLOAD_INC_CEN_LO; MEAN_PLOAD_INC_MES_HI; SE_PLOAD_INC_MES_HI; ci_lo_PLOAD_INC_MES_HI; ci_hi_PLOAD_INC_MES_HI; MEAN_PLOAD_INC_MES_LO; SE_PLOAD_INC_MES_LO; ci_lo_PLOAD_INC_MES_LO; ci_hi_PLOAD_INC_MES_LO; MEAN_PLOAD_INC_PALEO_HI; SE_PLOAD_INC_PALEO_HI; ci_lo_PLOAD_INC_PALEO_HI; ci_hi_PLOAD_INC_PALEO_HI; MEAN_PLOAD_INC_PALEO_LO; SE_PLOAD_INC_PALEO_LO; ci_lo_PLOAD_INC_PALEO_LO; ci_hi_PLOAD_INC_PALEO_LO; MEAN_PLOAD_INC_SPRINK_MI2; SE_PLOAD_INC_SPRINK_MI2; ci_lo_PLOAD_INC_SPRINK_MI2; ci_hi_PLOAD_INC_SPRINK_MI2; MEAN_PLOAD_INC_FLOODOTH_MI2; SE_PLOAD_INC_FLOODOTH_MI2; ci_lo_PLOAD_INC_FLOODOTH_MI2; ci_hi_PLOAD_INC_FLOODOTH_MI2; MEAN_PLOAD_INC_FLOODMZ_MI2; SE_PLOAD_INC_FLOODMZ_MI2; ci_lo_PLOAD_INC_FLOODMZ_MI2; ci_hi_PLOAD_INC_FLOODMZ_MI2; MEAN_DEL_FRAC; SE_DEL_FRAC; ci_lo_DEL_FRAC; ci_hi_DEL_FRAC","weight":1},{"access":[{"file_format":"XML; TXT","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/58c301f2e4b0f37a93ed915a"}],"access_details":null,"bbox":{"east":-65.3278,"north":51.6574,"south":23.2435,"west":-127.9108},"citation":"Wieczorek, M.E., Jackson, S.E., and Schwarz, G.E., 2018, Select Attributes for NHDPlus Version 2.1 Reach Catchments and Modified Network Routed Upstream Watersheds for the Conterminous United States (ver. 3.0, January 2021): U.S. Geological Survey data release, https://doi.org/10.5066/F7765D7V","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset describes attributes of surface-water impoundments derived from the National Inventory of Dams (NID), National Land Cover Data (NLCD) and the National Hydrography Dataset Plus (NHDPlus). Reservoir surface areas were determined using National Inventory of Dams Reservoir Surface area, NHDPlus Waterbody area, and National Land Cover Data (NLCD) open water body area as data sources. National Inventory of Dams were indexed on NHDPlus V2.1 using several sources and methods.","doi_url":"https://doi.org/10.5066/F7765D7V","domain":["Infrastructure"],"draft":false,"id":"a56fd23d-57f8-46a4-b3ff-0a3099afe6a4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/58c301f2e4b0f37a93ed915a?f=__disk__99%2Fb2%2F05%2F99b205a8671692f05bcc2801f0733d6dea73ed85\u0026allowOpen=true"}],"name":"Attributes for NHDPlus Version 2.1 Reach Catchments and Modified Routed Upstream Watersheds for the Conterminous United States: National Inventory of Dams (NID) Storage and Construction by Decade, 1930 to 2010","permalink":"/catalog/datasets/a56fd23d-57f8-46a4-b3ff-0a3099afe6a4/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS; USACE","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1930 - 2013","temporal_frequency":"10 years","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Indexed dams"],"vars":"Indexed dams","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6181ac65d34e9f2789e44897"}],"access_details":null,"bbox":{"east":-67.725,"north":50,"south":26.3358,"west":-124.393},"citation":"Foks, S.S., Towler, E., Hodson, T.O., Bock, A.R., Dickinson, J.E., Dugger, A.L., Dunne, K.A., Essaid, H.I., Miles, K.A., Over, T.M., Penn, C.A., Russell, A.M., Saxe, S.W., and Simeone, C.E., 2022, Streamflow benchmark locations for conterminous United States (cobalt gages): U.S. Geological Survey data release, https://doi.org/10.5066/P972P42Z","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data release consists of 5390 streamflow gages within the conterminous United States that will serve as the initial list of sites (version 1.0) used for streamflow benchmarking of hydrologic models. Sites within this list were chosen based on their presence in the GAGES-II dataset, their availability of modeled streamflow data from the most recent version of the National Hydrologic Model application of Precipitation-Runoff Modeling System v1.0, and their availability of modeled streamflow data from the most recent version of the NOAA National Water Model application of WRF-hydro version 2.1 retrospective dataset.","doi_url":"https://doi.org/10.5066/P972P42Z","domain":["Hydrology"],"draft":false,"id":"a59c91bb-52b2-4d2c-bf41-f893e48547bb","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6181ac65d34e9f2789e44897?f=__disk__7b%2F72%2F20%2F7b7220bf1997cdd4e713fda915b2a58ac48238a2\u0026allowOpen=true"},{"name":"Documentation","url":"https://doi.org/10.5066/P972P42Z"}],"name":"Streamflow benchmark locations for hydrologic model evaluation within the conterminous United States (cobalt gages)","permalink":"/catalog/datasets/a59c91bb-52b2-4d2c-bf41-f893e48547bb/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"2022","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["site_no","dec_lat_va","dec_long_va","comid","reachcode","reach_measure","drain_sqkm","huc02","gagesII_class","aggecoregion","complete_yrs","n_days","nldi","swim","gfv1d1","camels"],"vars":"site_no; dec_lat_va; dec_long_va; comid; reachcode; reach_measure; drain_sqkm; huc02; gagesII_class; aggecoregion; complete_yrs; n_days; nldi; swim; gfv1d1; camels","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6810c1a4d4be022940554075"}],"bbox":{"east":-63.118429521429235,"north":52.921719733858104,"south":21.805095225544456,"west":-129.27731989810934},"citation":"U.S. Geological Survey (USGS), 2024, Annual NLCD Collection 1 Science Products (ver. 1.1, June 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P94UXNTS.","creator":[],"creator_project":[],"date_created":"2/24/2026","date_updated":"6/3/2026","description":"The USGS Land Cover program has combined the tried-and-true methodologies from premier land cover projects, National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP), together with modern innovations in geospatial deep learning technologies to create the next generation of land cover and land change information. The product suite is called, “Annual NLCD” and includes six annual products that represent land cover and surface change characteristics of the U.S.:\u003cbr\u003e\n\u003cbr\u003e\n1) Land Cover,\u003cbr\u003e\n2) Land Cover Change,\u003cbr\u003e\n3) Land Cover Confidence,\u003cbr\u003e\n4) Fractional Impervious Surface,\u003cbr\u003e\n5) Impervious Descriptor, and\u003cbr\u003e\n6) Spectral Change Day of Year.\u003cbr\u003e\n\u003cbr\u003e\nThese land cover science product algorithms harness the remotely sensed Landsat data record to provide state-of-the-art land surface change information needed by scientists, resource managers, and decision-makers. Annual NLCD uses a modernized, integrated approach to map, monitor, synthesize, and understand the complexities of land use, cover, and condition change. With this second release, Annual NLCD, Collection 1.1, the six products are available for the Conterminous U.S. for 1985–2024.\u003cbr\u003e\n\u003cbr\u003e\nQuestions about the Annual NLCD product suite can be directed to the Annual NLCD mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or custserv@usgs.gov. See included spatial metadata for more details.","doi_url":"https://doi.org/10.5066/P94UXNTS","domain":["Land Cover"],"draft":false,"id":"a5cb05fe-5ac5-4015-bf68-49854fdea99c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6810c1a4d4be022940554075?f=__disk__df%2F99%2F3e%2Fdf993ec42c26c7da05a6d24e83d9a311ab005b65\u0026allowOpen=true"}],"name":"Annual National Land Cover Database (NLCD) Collection 1.1 Land Cover","permalink":"/catalog/datasets/a5cb05fe-5ac5-4015-bf68-49854fdea99c/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"1985 - 2024","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Open Water","Perennial Ice/Snow","Developed, Open Space","Developed, Low Intensity","Developed, Medium Intensity","Developed, High Intensity","Barren Land (Rock/Sand/Clay)","Deciduous Forest","Evergreen Forest","Mixed Forest","Shrub/Scrub","Grassland/Herbaceous","Pasture/Hay","Cultivated Crops","Woody Wetlands","Emergent Herbaceous Wetlands"],"vars":"Open Water; Perennial Ice/Snow; Developed, Open Space; Developed, Low Intensity; Developed, Medium Intensity; Developed, High Intensity; Barren Land (Rock/Sand/Clay); Deciduous Forest; Evergreen Forest; Mixed Forest; Shrub/Scrub; Grassland/Herbaceous; Pasture/Hay; Cultivated Crops; Woody Wetlands; Emergent Herbaceous Wetlands","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5f63790982ce38aaa23a3930"}],"access_details":null,"bbox":{"east":-66.0938,"north":50,"south":24.2069,"west":-126.2109},"citation":"Hammond, J.C., 2020, Contiguous U.S. annual snow persistence and trends from 2001-2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9U7U5FP","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Snow persistence is the fraction of time that snow is present on the ground for a defined period. Trends in snow persistence can be found at the same DOI. Snow persistence (SP) or the snow cover index (SCI), is the fraction of time that snow is present on the ground for a defined period. Cloud covered index (CCI) is the fraction of time that there is no data, cloud, or sensor saturation for the same period. SP and CCI were calculated on a pixel by pixel basis using MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Collection 6 obtained from the National Snow and Ice Data Center (NSIDC). January 1 - July 3 SP was calculated for each year as the fraction of 8-day MODIS images with snow present. The selected period brackets the temporal extent of peak snow accumulation to complete snow ablation in most parts of the western United States. The July 3 date is used because the 8-day MODIS image does not fall on the first of the month in this case. This data release contains gridded 500 m MODIS snow persistence for years 2001 to 2020 along with accompanying pixel by pixel trends for the entire contiguous U.S.","doi_url":"https://doi.org/10.5066/P9U7U5FP","domain":["Snow"],"draft":false,"id":"a78c9003-99bc-482a-b620-68c0ceb92b27","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Contiguous U.S. annual snow persistence and trends from 2001-2020","permalink":"/catalog/datasets/a78c9003-99bc-482a-b620-68c0ceb92b27/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"500 meter","temporal_coverage":"2001 - 2020","temporal_frequency":"8 days","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Snow, annual persistence"],"vars":"Snow, annual persistence","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5ebaa84c82ce25b513617f9f"}],"access_details":null,"bbox":{"east":-73.7183,"north":42.6663,"south":38.3589,"west":-76.7285},"citation":"Murphy, J.C., Shoda, M.E. and Follette, D.D., 2020, Water-quality trends for rivers and streams in the Delaware River Basin using Weighted Regressions on Time, Discharge, and Season (WRTDS) models, Seasonal Kendall Trend (SKT) tests, and multisource data, Water Year 1978-2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9KMWNJ5","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data release provides water-quality trends for rivers and streams in the Delaware River Basin determined using the Weighted Regressions on Time, Discharge, and Season (WRTDS) model and the Seasonal Kendall Trend (SKT) test. Sixteen water-quality parameters were assessed, including nutrients (ammonia, nitrate, filtered orthophosphate, total nitrogen, total phosphorus, and unfiltered orthophosphate), major ions (calcium, chloride, magnesium, potassium, sodium, and sulfate), salinity indicators (total dissolved solids and specific conductance), and sediment (total suspended solids and suspended sediment concentration). The child items include the input and output data used in the modeling and testing of water-quality trends. The attached files include the scripts used in these analyses, a readMe files for these scripts and tables summarizing information about the sites used in the analysis.\u003cbr\u003eThese trends build off the national efforts of Oelsner and others (2017) and Murphy and others (2018), with some variations in data screening and processing. One major divergence from these previous efforts was that screened site-parameter combinations were screened for the longest period of record that passed various temporal and seasonal criteria (\"maximum calibration\" approach) instead of screening by pre-defined trend periods. An additional difference was that water-quality data were combined from multiple monitoring locations and collecting organizations using hierarchical clustering based on the distance between monitoring locations on the same stream reach (as determined by the National Hydrography Dataset comid). Data that were a part of these \"cluster sites\" were manually reviewed prior to running SKT and WRTDS.\u003cbr\u003eInput data for SKT includes 124 sites (including individual sites and cluster sites) and 1,208 site-parameter combinations. Input data for WRTDS, which required additional screenings beyond those used for the SKT test and a paired streamflow gage, includes 62 sites and 476 site-parameter combinations. For both methods, some site-parameter combinations were not run due to the amount of censored data, or the results were rejected due to poor model fit. Trends are reported for four trend periods (1978-2018, 1998-2018, 2003-2018, and 2008-2018), as the available screened data allow, and for the entire screened period of record for each parameter at each site. This collection of trend results leverages the monitoring efforts of many collecting organizations across the Delaware River Basin and can serve to better understand changing water-quality conditions across this basin.\u003cbr\u003eReferences Cited:\u003cbr\u003eMurphy, J.C., Farmer, W.H., Sprague, L.A., De Cicco, L.A., and Hirsch, R.M., 2018, Water-quality trends and trend component estimates for the Nation's rivers and streams using Weighted Regressions on Time, Discharge, and Season (WRTDS) models and generalized flow normalization, 1972-2012: U.S. Geological Survey data release, https://doi.org/10.5066/F7TQ5ZS3.\u003cbr\u003eOelsner, G.P., Sprague, L.A., Murphy, J.C., Zuellig, R.E., Johnson, H.M., Ryberg, K.R., Falcone, J.A., Stets, E.G., Vecchia, A.V., Riskin, M.L., De Cicco, L.A., Mills, T.J., Farmer, W.H., 2017, Water-quality trends in the Nation’s rivers and streams 1972–2012—Data preparation, statistical methods, and trend results: U.S. Geological Survey Scientific Investigations Report, http://dx.doi.org/10.3133/sir20175006.\u003cbr\u003eShoda, M.E., Murphy, J.C., Falcone, J.A., and Duris, J.W., 2019, Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin: U.S. Geological Survey data release, https://doi.org/10.5066/P9PX8LZO.\u003cbr\u003eNational Water Quality Monitoring Council, Water Quality Portal (WQP), https://www.waterqualitydata.us/. Accessed 2020-11-03.","doi_url":"https://doi.org/10.5066/P9KMWNJ5","domain":["Water Quality"],"draft":false,"id":"a7e3d4c3-6d92-4f32-b4b1-57a0d2ec8ebb","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5ebaa84c82ce25b513617f9f?f=__disk__81%2F69%2F69%2F8169696048bdbe56574c9414c439e603226cd253\u0026allowOpen=true"},{"name":"Documentation","url":"https://pubs.er.usgs.gov/publication/sir20225097"}],"name":"Water-quality trends for rivers and streams in the Delaware River Basin using Weighted Regressions on Time, Discharge, and Season (WRTDS) models, Seasonal Kendall Trend (SKT) tests, and multisource data, Water Year 1978 - 2018","permalink":"/catalog/datasets/a7e3d4c3-6d92-4f32-b4b1-57a0d2ec8ebb/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Delaware River basin","spatial_resolution":"unknown","temporal_coverage":"1978 - 2018","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["nutrients (ammonia, nitrate, filtered orthophosphate, total nitrogen, total phosphorus, and unfiltered orthophosphate)","major ions (calcium, chloride, magnesium, potassium, sodium, and sulfate)","salinity indicators (total dissolved solids and specific conductance)","sediment (total suspended solids and suspended sediment concentration)"],"vars":"nutrients (ammonia, nitrate, filtered orthophosphate, total nitrogen, total phosphorus, and unfiltered orthophosphate); major ions (calcium, chloride, magnesium, potassium, sodium, and sulfate); salinity indicators (total dissolved solids and specific conductance); sediment (total suspended solids and suspended sediment concentration)","weight":1},{"access":[{"file_format":"XLSX; SHP","name":"hydroshare.org","url":"https://www.hydroshare.org/resource/1160b46d62f1488693a4dd2447793410/"}],"access_details":null,"bbox":{"east":-56,"north":52,"south":24,"west":-127},"citation":"Siddik, M. A. B., K. E. Dickson, J. Rising, B. R. Ruddell, L. T. Marston (2022). Interbasin water transfers in the United States and Canada, HydroShare, https://doi.org/10.4211/hs.1160b46d62f1488693a4dd2447793410","creator":[],"creator_project":[],"date_created":"5/1/2025","date_updated":"6/3/2026","description":"This publication includes three connected datasets inventorying interbasin transfers (IBTs) in the United States and Canada, including their features, geospatial details, and water transfer volumes (IBT Inventory Data.xlsx, Digitized_IBT.shp, and zip file of 25 IBT Time-Series Flow xlsx tables by state/province). These IBT datasets represent all known transfers of untreated water that cross a HUC4 boundary (in the U.S.) or a subdrainage area boundary (in Canada), characterizing a total of 641 IBT projects. The datasets containing records of conveyance volumes, water use purpose(s), owner/operator, location, and other infrastructure details for IBTs in the United States and Canada.","doi_url":"https://doi.org/10.4211/hs.1160b46d62f1488693a4dd2447793410","domain":["Hydrology","Infrastructure","Water Use"],"draft":false,"id":"a9819e9e-ea2a-47c1-9889-645cb7887197","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"ae9546b9-1eda-407a-9129-8a62b43ac056","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.nature.com/articles/s41597-023-01935-4"}],"name":"Interbasin water transfers in the United States and Canada","permalink":"/catalog/datasets/a9819e9e-ea2a-47c1-9889-645cb7887197/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"CONUS; parts of Canada","spatial_resolution":"NA","temporal_coverage":"1823 - 2020","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Link_ID","Node_ID_Start","Node_ID_End","IBT_Project","Owner_operator","Year_IBT","time_series_data (available/not available)","average_water_Transfer_Rate (m3/d)","State/Province","Data Source","IBT_Contact","IBT_Contact_Agency","Date Recorded"],"vars":"Link_ID; Node_ID_Start; Node_ID_End; IBT_Project; Owner_operator; Year_IBT; time_series_data (available/not available); average_water_Transfer_Rate (m3/d); State/Province; Data Source; IBT_Contact; IBT_Contact_Agency; Date Recorded","weight":1},{"access":[{"file_format":"NC","name":"disc.gsfc.nasa.gov","url":"https://disc.gsfc.nasa.gov/datasets/LPRM_AMSRE_A_SOILM3_002/summary"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Vrije Universiteit Amsterdam (de Jeu, R.) and NASA GSFC (Owe, M.), 2011, AMSR-E/Aqua surface soil moisture (LPRM) L3 1 day 25 km x 25 km ascending V002: Edited by Goddard Earth Sciences Data and Information Services Center (GES DISC) (Teng, B.), Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed [YYYY-MM-DD], https://doi.org/10.5067/X3K5V3NNLYAV\u003cbr\u003eOwe, M., de Jeu, R., and Holmes, T., 2008, Multisensor historical climatology of satellite-derived global land surface moisture: Journal of Geophysical Research, F01002, https://doi.org/10.1029/2007JF000769","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"AMSR-E/Aqua surface soil moisture (LPRM) L3 1 day 25 km x 25 km ascending V002 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the daytime product. The data set covers the period from June 2002 to October 2011 (when the AMSR-E on the NASA EOS Aqua satellite stopped producing data due to a problem with the rotation of its antenna). The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR-E's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR-E resampled brightness temperatures (AE_L2A) product, daytime passes, as processed using LPRM (i.e., LPRM/AMSR-E/Aqua L2B product, LPRM_AMSRE_SOILM2_V002).","doi_url":"https://doi.org/10.1029/2007JF000769","domain":["Soils"],"draft":false,"id":"a9dcdc7e-93b6-4d42-b99a-a37a500f19e0","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://hydro1.gesdisc.eosdis.nasa.gov/data/WAOB/LPRM_AMSRE_A_SOILM3.002/doc/README_LPRM.pdf"}],"name":"AMSR-E/Aqua surface soil moisture (LPRM) L3 1 day 25 km x 25 km ascending V002 (LPRM_AMSRE_A_SOILM3)","permalink":"/catalog/datasets/a9dcdc7e-93b6-4d42-b99a-a37a500f19e0/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"25 kilometer","temporal_coverage":"2002 - 2011","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Soil moisture"],"vars":"Soil moisture","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/get/5b15a50ce4b092d9651e22b9"}],"access_details":null,"bbox":{"east":-65.6543,"north":49.4678,"south":24.4868,"west":-125.2441},"citation":"Bock, A.R., Falcone, J.A., Oelsner, G., and Baker, N.T., 2018, Estimates of Road Salt Application across the Conterminous United States, 1992-2019 (ver. 2.0, August 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P96IX385","creator":[{"creator_email":"abock@usgs.gov","creator_name":"Andy Bock"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Annual estimates of road salt application were initially developed for the conterminous United States for the calendar years 1992 through 2015.  As more data became available, years 2016 through 2019 were added to the dataset. The final dataset consists of 28 rasters in Geostationary Earth Orbit Tagged Image File Format (GeoTIFF), one for each year from 1992 through 2019. The final estimates (in pounds) were derived from several data sources, which include road density and proportion of developed land use, depth and spatial extent of long-term snowfall, and the production and distribution of salt sources by state.  The extent is the conterminous United States, and the spatial resolution is one-square kilometer.","doi_url":"https://doi.org/10.5066/P96IX385","domain":["Water Quality"],"draft":false,"id":"adde3501-bf38-474d-95d1-a7e5249ec550","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"553e2bbb-00ea-4052-b472-5561943d5dc6","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5b15a50ce4b092d9651e22b9?f=__disk__53%2Fe2%2Fad%2F53e2add533b503219af7e110506bdd167e37c960\u0026transform=1\u0026allowOpen=true"},{"name":"Documentation","url":"https://doi.org/10.5066/P96IX385"}],"name":"Estimates of Road Salt Application across the Conterminous U.S.","permalink":"/catalog/datasets/adde3501-bf38-474d-95d1-a7e5249ec550/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1 kilometer","temporal_coverage":"1992 - 2019","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["road salt application"],"vars":"road salt application","weight":1},{"access":[{"file_format":"TXT","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5d6fa631e4b0c4f70cf92e9e"}],"access_details":null,"bbox":{"east":-74.0254440307617,"north":42.4653854370117,"south":38.694580078125,"west":-76.388786315918},"citation":"Shoda, M.E., Murphy, J.C., Falcone, J.A., and Duris, J.W., 2019, Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin: U.S. Geological Survey data release, https://doi.org/10.5066/P9PX8LZO.","creator":[{"creator_email":"meshoda@usgs.gov","creator_name":"Meg Shoda"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Jointly managed by multiple states and the federal government, there are many ongoing efforts to characterize and understand water quality in the Delaware River Basin (DRB). Many State, Federal and non-profit organizations have collected surface-water-quality samples across the DRB for decades and many of these data are available through the National Water Quality Monitoring Council's Water Quality Portal (WQP). For this data release, WQP data in the DRB were harmonized, meaning that they were processed to create a clean and readily usable dataset. The harmonization process included the synthesis of parameter names and fractions, the condensation of remarks and other data qualifiers, the resolution of duplicate records, an initial quality control check of the data, and other steps described in this data release. The water-quality dataset provided is the harmonized discrete multisource surface-water-quality data pulled from the WQP and includes data for nutrients, sediment, salinity, major ions, bacteria, temperature, dissolved oxygen, pH, and turbidity in the DRB, for all available years. The code and supplemental information used to create the water-quality dataset is included in this data release. Also provided is the USGS streamgage match table, which pairs the monitoring sites in the water-quality dataset to USGS streamgages and lists\u0026nbsp;basic streamgage information, including distance from the water-quality site and if the streamgage was recently active. Lastly, this data release includes a 12-page summary document, which includes a detailed description of the harmonization process, basic summaries of the water-quality dataset, and the results of a screening process in which water-quality sites were assessed for recent and long-term sampling and paired with nearby and recently active USGS streamgages. Please see the read me file for more information, including the organization of this data release and its contents.\u003cbr\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\u0026nbsp;","doi_url":"https://doi.org/10.5066/P9PX8LZO","domain":["Water Quality"],"draft":false,"id":"ae648954-34c8-439f-b118-c71712210337","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin","permalink":"/catalog/datasets/ae648954-34c8-439f-b118-c71712210337/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Delaware River basin","spatial_resolution":"unknown","temporal_coverage":"1901 - 2019","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Water-quality parameters"],"vars":"Water-quality parameters","weight":1},{"access":[{"file_format":"NC; CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5d826f6ae4b0c4f70d05913f"}],"access_details":null,"bbox":{"east":-66.709,"north":49.0088,"south":24.4868,"west":-125.5957},"citation":"Hay, L.E. and LaFontaine, J.H., 2020, Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS), 1980-2016, Daymet Version 3 calibration: U.S. Geological Survey data release, https://doi.org/10.5066/P9PGZE0S","creator":[],"creator_project":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data release contains inputs and outputs for hydrologic simulations of the conterminous United States (CONUS) using the National Hydrologic Model (NHM) application of the Precipitation Runoff Modeling System (PRMS) in ASCII and binary format and explanatory graphics in pdf format. These simulations were developed to provide estimates of water availability for historical conditions for the period October 1, 1980 to September 30, 2016 for five different calibration configurations; the first three years of the simulation should be considered the initialization period and should not be used for subsequent analysis. The five versions of model parameters and associated model output included in this data release are described in table 1 and in the Supplemental Information section of this metadata record. Table 2 provides information about the baseline datasets used for model calibration for each of the five parameter configurations. Figure 1 shows a schematic of the multi-step calibration procedure used to develop the model parameters. Table 3 describes the 36 model output variables that are included in the five attached folders. Five .tar folders are named according to the simulation configuration in table 1 and include the 36-model output variable files. Table 4 provides information about the 8,274 streamgage locations that are included in the NHM-PRMS. The NHM-PRMS parameter and control files for each of the five simulations are located on the child pages associated with this data release. The PRMS climate forcing input files for the simulations are in the DAYMET_CBH.zip folder. Summary files by streamgage of measured and simulated streamflow for the byHRU, byHRU_musk, and byHRU_musk_obs simulations are in the Streamgage_location_simulations_5999.zip folder. Any time series data in the model output files prior to the October 1, 1983 start date should be considered part of the model initialization period and should not be used. Please refer to the Supplemental Information element of this metadata record for more information about the model calibration, inputs, outputs, and summaries included in this data release.","doi_url":"https://doi.org/10.5066/P9PGZE0S","domain":["Hydrology"],"draft":false,"id":"aef8c40a-c732-4629-8bcb-4bba607c6756","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5d826f6ae4b0c4f70d05913f?f=__disk__ac%2F68%2Fd4%2Fac68d422403c6fa764629b02ccccffbffc1457e1\u0026allowOpen=true"},{"name":"Documentation","url":"https://pubs.usgs.gov/publication/tm6B10"}],"name":"NHM-PRMS (Daymet v3, GF v1.0): Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS), 1980-2016, Daymet Version 3 calibration","permalink":"/catalog/datasets/aef8c40a-c732-4629-8bcb-4bba607c6756/","project_use_history":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1980 - 2016","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Streamflow","Actual Evapotranspiration","Fractional Snow-Covered Area","Snow Water Equivalent","Soil Moisture"],"vars":"Streamflow; Actual Evapotranspiration; Fractional Snow-Covered Area; Snow Water Equivalent; Soil Moisture","weight":1},{"access":[{"file_format":"NC","name":"psl.noaa.gov","url":"https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html"},{"file_format":"NC","name":"psl.noaa.gov","url":"https://psl.noaa.gov/thredds/catalog/Datasets/ncep.reanalysis/surface_gauss/catalog.html"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"E. Kalnay E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., Joseph, D., 1996, The NCEP/NCAR 40-year reanalysis project, Bulletin of the American Meteorological Society, volume 77 Issue 3, pages 437-472, https://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The NCEP/NCAR Reanalysis 1 project is using a state-of-the-art analysis/forecast system to perform data assimilation using past data from 1948 to the present. The data have 6-hour temporal resolution (0000, 0600, 1200, and 1800 UTC) and 2.5 degree spatial resolution. The NCEP/NCAR Reanalysis is an atmospheric reanalysis produced by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). It is a continually updated globally gridded data set that represents the state of the Earth's atmosphere, incorporating observations and numerical weather prediction (NWP) model output from 1948 to present.","doi_url":"https://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2","domain":["Climate"],"draft":false,"id":"b0c1a6d2-ce5b-4856-bec0-115eb77803f6","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html"}],"name":"NCEP-NCAR Reanalysis 1","permalink":"/catalog/datasets/b0c1a6d2-ce5b-4856-bec0-115eb77803f6/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NCEP; NCAR","spatial_extent":"Global","spatial_resolution":"2.5 degrees","temporal_coverage":"1948 - Present","temporal_frequency":"6 hours; daily; monthly","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Air Temperature","Best (4-layer) Lifted Index","Clear Sky Downward Longwave Flux","Clear Sky Downward Solar Flux","Clear Sky Upward Longwave Flux","Clear Sky Upward Shortwave Flux","Clear Sky Upward Solar Flux","Cloud Forcing Net Longwave Flux","Cloud Forcing Net Solar Flux","Convective Precipitation Rate","Divergence","Downward Longwave Radiation Flux","Downward Solar Radiation Flux","Geopotential height","Ground Heat Flux","Ice Concentration","Land-sea mask","Latent Heat Net Flux","Maximum Temperature","Meridional Gravity Wave Stress","Minimum Temperature","Momentum Flux, u-component","Momentum Flux, v-component","Monthly Mean Precipitation Rate","Near IR Beam Downward Solar Flux","Near IR Diffuse Downward Solar Flux","Net Longwave Radiation Flux","Net Shortwave Radiation Flux","Net Solar Radiation Flux","Omega (dp/dt)","Potential Evaporation Rate","Potential Temperature","Precipitable Water Content","Precipitation Rate","Pressure","Pressure forecast","Relative Humidity","Relative Vorticity","Relative humidity","Sea Level Pressure","Sensible Heat Net Flux","Skin Temperature","Specific Humidity","Specific humidity","Streamfunction","Surface Lifted Index","Surface Pressure","Surface Roughness","Temperature","Total cloud cover","Upward Longwave Radiation Flux","Upward Solar Radiation Flux","Velocity Potential","Virtual Temperature","Visible Beam Downward Solar Flux","Visible Diffuse Downward Solar Flux","Volumetric Soil Moisture","Vorticity","Water Equiv. of Accum. Snow Depth","Water Runoff","Wind Speed","Zonal Gravity Wave Stress","cloud layer pressure","specific humidity","thickness","u-wind","v-wind"],"vars":"Air Temperature; Best (4-layer) Lifted Index; Clear Sky Downward Longwave Flux; Clear Sky Downward Solar Flux; Clear Sky Upward Longwave Flux; Clear Sky Upward Shortwave Flux; Clear Sky Upward Solar Flux; Cloud Forcing Net Longwave Flux; Cloud Forcing Net Solar Flux; Convective Precipitation Rate; Divergence; Downward Longwave Radiation Flux; Downward Solar Radiation Flux; Geopotential height; Ground Heat Flux; Ice Concentration; Land-sea mask; Latent Heat Net Flux; Maximum Temperature; Meridional Gravity Wave Stress; Minimum Temperature; Momentum Flux, u-component; Momentum Flux, v-component; Monthly Mean Precipitation Rate; Near IR Beam Downward Solar Flux; Near IR Diffuse Downward Solar Flux; Net Longwave Radiation Flux; Net Shortwave Radiation Flux; Net Solar Radiation Flux; Omega (dp/dt); Potential Evaporation Rate; Potential Temperature; Precipitable Water Content; Precipitation Rate; Pressure; Pressure forecast; Relative Humidity; Relative Vorticity; Relative humidity; Sea Level Pressure; Sensible Heat Net Flux; Skin Temperature; Specific Humidity; Specific humidity; Streamfunction; Surface Lifted Index; Surface Pressure; Surface Roughness; Temperature; Total cloud cover; Upward Longwave Radiation Flux; Upward Solar Radiation Flux; Velocity Potential; Virtual Temperature; Visible Beam Downward Solar Flux; Visible Diffuse Downward Solar Flux; Volumetric Soil Moisture; Vorticity; Water Equiv. of Accum. Snow Depth; Water Runoff; Wind Speed; Zonal Gravity Wave Stress; cloud layer pressure; specific humidity; thickness; u-wind; v-wind","weight":1},{"access":[{"file_format":"HDF","name":"nsidc.org","url":"https://nsidc.org/data/AU_DySno/"}],"access_details":"","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Tedesco, M. and Jeyaratnam, J., 2019, AMSR-E/AMSR2 Unified L3 Global Daily 25 km EASE-Grid Snow Water Equivalent, Version 1. [Indicate subset used]. Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed [YYYY-MM-DD], https://doi.org/10.5067/8AE2ILXB5SM6","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"As of 01 September 2025, the NASA AMSR2 SIPS will stop processing and delivering updates to Advanced Microwave Scanning Radiometer Unified (AMSR Unified) data sets. For more information, see \"User notice: Updates to NASA AMSR-E/AMSR2 Unified data sets to cease on 01 September 2025\".\u003cbr\u003e\u003cbr\u003eThis AMSR-E/AMSR2 Unified Level-3 (L3) data set provides daily estimates of Snow Water Equivalent (SWE). SWE was derived from brightness temperature measurements acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on board the JAXA GCOM-W1 satellite. The SWE data is rendered to an azimuthal 25 km Equal-Area Scalable Earth Grid (EASE-Grid) for both the Northern and Southern Hemisphere. Note: This data set uses JAXA AMSR2 Level-1R (L1R) input brightness temperatures that are calibrated, or unified, across the JAXA AMSR-E and JAXA AMSR2 L1R products.","doi_url":"https://doi.org/10.5067/8AE2ILXB5SM6","domain":["Snow"],"draft":false,"id":"b0e496b9-a724-4a47-9557-37686228e595","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://nsidc.org/sites/default/files/au_dysno_v001_userguide_1.pdf"}],"name":"AMSR-E/AMSR2 Unified L3 Global Monthly 25 km EASE-Grid Snow Water Equivalent, Version 1","permalink":"/catalog/datasets/b0e496b9-a724-4a47-9557-37686228e595/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NSIDC","spatial_extent":"Global","spatial_resolution":"25 kilometer","temporal_coverage":"2012 - 2025","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Snow water equivalent"],"vars":"Snow water equivalent","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6476425ed34e4e58932da21f"}],"access_details":null,"bbox":{"east":-65,"north":50,"south":25,"west":-127},"citation":"Lindsey, B.D., Dondero, A.M., Watson, E., and Johnson, T.D., 2023, Data from Decadal Change in Groundwater Quality Web Site, 1988-2022: U.S. Geological Survey data release, https://doi.org/10.5066/P9YEB7FS.","creator":[],"creator_project":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"date_created":"5/7/2024","date_updated":"6/3/2026","description":"Evaluating Decadal Changes in Groundwater Quality: Groundwater-quality data were collected from 5,000 wells between 1988-2001 (first decadal sampling event) by the National Water-Quality Assessment Project. Samples are collected in groups of 20-30 wells with similar characteristics called networks. About 1,500 of these wells in 67 networks were sampled again approximately 10 years later between 2002-2012 (second sampling event) to evaluate decadal changes in groundwater quality. Between 2012 and 2022 (third sampling event), a subset of these networks were sampled again, allowing additional results to be displayed on the web page: Decadal changes in groundwater quality (https://nawqatrends.wim.usgs.gov/decadal/). This is the seventh iteration of data added to the website. With the additional data, it is possible to evaluate changes in water quality between the 2nd and 3rd sampling events for 76 networks, changes in water quality between the 1st and 3rd sampling events for 63 networks, and changes across all 3 sampling events for 60 networks. Samples were obtained from monitoring wells, domestic-supply wells, and some public-supply wells before any treatment on the system.\u003cbr\u003eGroundwater samples used to evaluate decadal change were collected from networks of wells with similar characteristics. Some networks, consisting of domestic or public-supply wells, were used to assess changes in the quality of groundwater used for drinking water supply. Other networks, consisting of monitoring wells, assessed changes in the quality of shallow groundwater underlying key land-use types such as agricultural or urban lands. Networks were chosen based on geographic distribution across the Nation and to represent the most important water-supply aquifers and specific land-use types.\u003cbr\u003eDecadal changes in concentrations of nutrients, metals, and pesticides and other organic contaminants in groundwater were evaluated in a total of 89 networks across the Nation by comparing changes between selected sampling events.\u003cbr\u003eDecadal changes in median concentrations for a network are classified as large, small, or no change in comparison to a benchmark concentration. For example, a large change in chloride concentrations indicates that the probability of the test is less than or equal to 0.10 and the median of all differences in concentrations in a network is greater than 5 percent of the chloride benchmark per decade. For chloride, which has a Secondary Maximum Contaminant level of 250 milligrams per liter, this would mean the change in concentration exceeded 12.5 milligrams per liter (mg/L), or 5 percent of the benchmark.\u003cbr\u003e230 networks were sampled from 1988 to 2001 to assess the status of the Nation's groundwater quality. Each dot on the map on the \"About-Learn more\" tab of the Decadal mapper website, (https://nawqatrends.wim.usgs.gov/decadal/) represents the center point (centroid) of a network of about 20 to 30 wells. Networks sampled in the first sampling event only are shown in green. There were 67 networks resampled from 2002 to 2012 to assess decadal changes in groundwater quality. Networks sampled from 2012 to 2022 and at least one previous sampling event are shown in orange and trend networks that have not yet been resampled in the third decadal sampling event are shown in blue. Networks sampled in the first and second sampling events but are no longer being sampled are shown in gray.","doi_url":"https://doi.org/10.5066/P9YEB7FS","domain":["Hydrology","Water Quality"],"draft":false,"id":"b14f90ac-e844-4204-a5bf-6467151fc0d5","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6476425ed34e4e58932da21f?f=__disk__6b%2Fd9%2Fce%2F6bd9cee4acc9b49a8396ffd6758da02c5f92e68e\u0026allowOpen=true"}],"name":"Data from Decadal Change in Groundwater Quality Web Site, 1988-2022","permalink":"/catalog/datasets/b14f90ac-e844-4204-a5bf-6467151fc0d5/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"unknown","temporal_coverage":"1988 - 2022","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Constituent short name","Long name","Preferred Join Order by Cycle","PCODE","CAL","RECODE COMM COLUMN","Network","STAID","WELL_DEPTH","CY#_DATE_Constituent","CY#_FIELD_Constituent","CY#_LAB_Constituent","CY#_COMM_Constituent","CY#_RMK_Constituent","Network Name","Network Type","Latitude NAD83 (DD)","Longitude NAD83 (DD)","Constituent p value","Constituent N","Constituent Annual Difference","Constituent Decadal Difference","Constituent Median Difference","Code_Constituent","MTBE p value","MTBE USING 4436","MTBE N","MTBE Median Difference","Code_MTBE","DBCP p value","DBCP N","DBCP Median Difference","Code_DBCP","CY#_DATE_MTBE","CY#_RMK_MTBE","CY#_LAB_MTBE","CY#_COMM_MTBE_#","CY#_DATE_DBCP","CY#_RMK_DBCP","CY#_LAB_DBCP","CY#_COMM_DBCP"],"vars":"Constituent short name;  Long name;  Preferred Join Order by Cycle;  PCODE;  CAL;  RECODE COMM COLUMN;  Network;  STAID;  WELL_DEPTH;  CY#_DATE_Constituent;  CY#_FIELD_Constituent;  CY#_LAB_Constituent;  CY#_COMM_Constituent;  CY#_RMK_Constituent;  Network Name;  Network Type;  Latitude NAD83 (DD);  Longitude NAD83 (DD);  Constituent p value;  Constituent N;  Constituent Annual Difference;  Constituent Decadal Difference;  Constituent Median Difference;  Code_Constituent;  MTBE p value;  MTBE USING 4436;  MTBE N;  MTBE Median Difference;  Code_MTBE;  DBCP p value;  DBCP N;  DBCP Median Difference;  Code_DBCP;  CY#_DATE_MTBE;  CY#_RMK_MTBE;  CY#_LAB_MTBE;  CY#_COMM_MTBE_#;  CY#_DATE_DBCP;  CY#_RMK_DBCP;  CY#_LAB_DBCP;  CY#_COMM_DBCP","weight":1},{"access":[{"file_format":"CSV; TXT; PARQUET","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/63cb311ed34e06fef14f40a3"}],"access_details":null,"bbox":{"east":-65.3906,"north":49.6107,"south":24.2069,"west":-125.5078},"citation":"Blodgett, D.L., 2023, Updated CONUS river network attributes based on the E2NHDPlusV2 and NWMv2.1 networks (ver. 2.0, February 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P976XCVT.","creator":[{"creator_email":"dblodgett@usgs.gov","creator_name":"David L Blodgett"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The comid field of these data can be used to join to the NHDPlus version 2 flowline comid or catchment featureid attributes. The included attributes follow the same data model as the NHDPlusV2 but include numerous updates and improvements to network connectivity. All attributes that depend on network connectivity have been recalculated.\u003cbr\u003e\n\u003cbr\u003e\nThese attributes are based on the National Hydrography Dataset Plus V2.1 (NHDPlusV2) network geometry and modifications retrieved from the National Water Model V2.1 (NWMv2.1) and \"E2NHDPlusV2_us: Database of Ancillary Hydrologic Attributes and Modified Routing for NHDPlus Version 2.1 Flowlines\" (E2NHDPlusV2) datasets.\u003cbr\u003e\n\u003cbr\u003e\nThese attributes are available in three formats: csv, fst, and parquet. \"fst\" is a high performance format for use with the R programming language \"fst\" package. \"parquet\" is a high performance format for use with multiple programming languages (including python) that support the Apache Arrow Parquet format.\u003cbr\u003e\n\u003cbr\u003e\nAs noted below, many of these are derived directly from the NHDPlusV2 database. Others are selectively overridden according to modifications found in the NWMv2.1 and E2NHDPlusV2, and some have been recalculated based on algorithms defined in the NHDPlusV2 user's manual and implemented in the nhdplusTools R package.\u003cbr\u003e\n\u003cbr\u003e\nLinks:\u003cbr\u003e\nNHDPlusV2: \u003ca href=\"https://www.epa.gov/waterdata/get-nhdplus-national-hydrography-dataset-plus-data\"\u003ehttps://www.epa.gov/waterdata/get-nhdplus-national-hydrography-dataset-plus-data\u003c/a\u003e\u003cbr\u003e\nnhdplusTools: \u003ca href=\"https://doi.org/10.5066/P97AS8JD\"\u003ehttps://doi.org/10.5066/P97AS8JD\u003c/a\u003e\u003cbr\u003e\nE2NHDPlusV2: \u003ca href=\"https://doi.org/10.5066/p986kzem\"\u003ehttps://doi.org/10.5066/p986kzem\u003c/a\u003e\u003cbr\u003e\nNWMv2.1: \u003ca href=\"https://www.nohrsc.noaa.gov/pub/staff/keicher/NWM_live/NWM_parameters/\"\u003ehttps://www.nohrsc.noaa.gov/pub/staff/keicher/NWM_live/NWM_parameters/\u003c/a\u003e\u003cbr\u003e\nOriginal Metadata: \u003ca href=\"https://www.sciencebase.gov/catalog/file/get/63cb311ed34e06fef14f40a3?name=enhd_nwm_network.xml\u0026amp;transform=1\u0026amp;allowOpen=true\"\u003ehttps://www.sciencebase.gov/catalog/file/get/63cb311ed34e06fef14f40a3?name=enhd_nwm_network.xml\u0026amp;transform=1\u0026amp;allowOpen=true\u003c/a\u003e\u003cbr\u003e\n\u003cbr\u003e\nThe attributes included are:\u003cbr\u003e\ncomid: integer derived from NHDPlusV2,\u003cbr\u003e\ntocomid: integer derived from tonode/fromnode topology of NHDPlusV2, E2NHDPlusV2, and tocomid attributes of the NWMv2.1 routelink file,\u003cbr\u003e\nfcode: integer derived from NHDPlusV2,\u003cbr\u003e\ngnis_id: integer GNIS_ID derived from NHDPlusV2,\u003cbr\u003e\nreachcode: character derived from NHDPlusV2,\u003cbr\u003e\nfrommeas: numeric derived from NHDPlusV2,\u003cbr\u003e\ntomeas: numeric derived from NHDPlusV2,\u003cbr\u003e\nlengthkm: numeric derived from NHDPlusV2,\u003cbr\u003e\narbolate_sum: numeric recalculated with nhdplusTools,\u003cbr\u003e\nareasqkm: numeric derived from NHDPlusV2,\u003cbr\u003e\ntotdasqkm: numeric recalculated with nhdplusTools,\u003cbr\u003e\nhydroseq: integer recalculated with nhdplusTools,\u003cbr\u003e\ndnhydroseq:\u0026nbsp; integer recalculated with nhdplusTools,\u003cbr\u003e\nlevelpathi:\u0026nbsp; integer recalculated with nhdplusTools,\u003cbr\u003e\ndnlevelpat: integer recalculated with nhdplusTools,\u003cbr\u003e\nterminalpa: integer recalculated with nhdplusTools,\u003cbr\u003e\nterminalfl: integer (1 or 0) recalculated with nhdplusTools,\u003cbr\u003e\npathlength: numeric recalculated with nhdplusTools.\u003cbr\u003e\nstreamleve: integer recalculated with nhdplusTools.\u003cbr\u003e\nstreamorde: integer recalculated with nhdplusTools.\u003cbr\u003e\nvpuin: logical derived from NHDPlusV2,\u003cbr\u003e\nvpuout: logical derived from NHDPlusV2,\u003cbr\u003e\nwbareatype: character derived from NHDPlusV2,\u003cbr\u003e\nslope: numeric derived from NHDPlusV2,\u003cbr\u003e\nslopelenkm: numeric derived from NHDPlusV2,\u003cbr\u003e\nftype: character derived from NHDPlusV2,\u003cbr\u003e\ngnis_name: character derived from NHDPlusV2,\u003cbr\u003e\ngnis_id: integer derived from NHDPlusV2,\u003cbr\u003e\nwbareacomi: integer derived from NHDPlusV2,\u003cbr\u003e\nhwnodesqkm: numeric derived from NHDPlusV2,\u003cbr\u003e\nrpuid: character derived from NHDPlusV2,\u003cbr\u003e\nvpuid: character derived from NHDPlusV2,\u003cbr\u003e\nroughness: numeric derived from NHDPlusV2","doi_url":"https://doi.org/10.5066/P976XCVT","domain":["Hydrology"],"draft":false,"id":"b281691f-610f-4807-b729-0ecf14a7230d","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[{"id":"6389d98c-7de8-4dc7-938c-f98da1063455","rel_type":"IsSourceOf"}],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/63cb311ed34e06fef14f40a3?f=__disk__7e%2Fcc%2F3f%2F7ecc3f92eaf7618ae79da15c3cbb1531bcdb9837\u0026allowOpen=true"}],"name":"Updated CONUS river network attributes based on the E2NHDPlusV2 and NWMv2.1 networks (ver. 2.0, February 2023)","permalink":"/catalog/datasets/b281691f-610f-4807-b729-0ecf14a7230d/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["comid","tocomid","fcode","gnis_id","reachcode","frommeas","tomeas","lengthkm","arbolate_su","areasqkm","totdasqkm","hydroseq","dnhydroseq","levelpathi","dnlevelpat","terminalpa","terminalfl","pathlength","streamleve","streamorde","vpuin","vpuout","wbareatype","slope","slopelenkm","ftype","gnis_name","gnis_id","wbareacomi","hwnodesqkm","rpuid","vpuid","roughness"],"vars":"comid; tocomid; fcode; gnis_id; reachcode; frommeas; tomeas; lengthkm; arbolate_su; areasqkm; totdasqkm; hydroseq; dnhydroseq; levelpathi; dnlevelpat; terminalpa; terminalfl; pathlength; streamleve; streamorde; vpuin; vpuout; wbareatype; slope; slopelenkm; ftype; gnis_name; gnis_id; wbareacomi; hwnodesqkm; rpuid; vpuid; roughness","weight":1},{"access":[{"file_format":"BIN","name":"nsidc.org","url":"https://nsidc.org/data/g02158"},{"file_format":"ZARR","name":"WMA STAC","url":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/snodas"}],"access_details":null,"bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"National Operational Hydrologic Remote Sensing Center, 2004, Snow Data Assimilation System (SNODAS) Data Products at NSIDC, Version 1. [Indicate subset used]. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center, Accessed [YYYY-MM-DD], https://doi.org/10.7265/N5TB14TC.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data set contains snow pack properties, such as depth and snow water equivalent (SWE), from the NOAA National Weather Service's National Operational Hydrologic Remote Sensing Center (NOHRSC) SNOw Data Assimilation System (SNODAS). SNODAS is a modeling and data assimilation system developed by NOHRSC to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis.","doi_url":"https://doi.org/10.7265/N5TB14TC","domain":["Climate","Snow"],"draft":false,"id":"b297648f-bd45-4453-9602-faebeb624ebe","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[{"id":"148a7db7-1175-47aa-8437-5ae39f62b1ce","rel_type":"IsSourceOf"}],"links":[],"name":"SNODAS: Snow Data Assimilation System (SNODAS) Data Products at NSIDC, Version 1","permalink":"/catalog/datasets/b297648f-bd45-4453-9602-faebeb624ebe/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NOAA","spatial_extent":"CONUS","spatial_resolution":"1 kilometer","temporal_coverage":"2003 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Liquid precipitation","Snow depth","Snow melt runoff at the base of the snow pack","Snow pack average temperature","Snow water equivalent","Solid precipitation","Sublimation from the Snow Pack","Sublimation of Blowing Snow"],"vars":"Liquid precipitation; Snow depth; Snow melt runoff at the base of the snow pack; Snow pack average temperature; Snow water equivalent; Solid precipitation; Sublimation from the Snow Pack; Sublimation of Blowing Snow","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5c585e8ce4b0708288ff26cc"}],"access_details":null,"bbox":{"east":-63.1667,"north":54.8088,"south":21.0821,"west":-129.7712},"citation":"Wieczorek, M.E., and Sabitov, T.Y., 2019, 30 year (1981 - 2010) annual average duration of consecutive dry and wet days for the Conterminous United States and District of Columbia: U.S. Geological Survey data release, https://doi.org/10.5066/P9ZWQ48W","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Annual average duration of consecutive wet and dry events during the 30-year period 1981-2010, A wet event is defined as a period when the number of consecutive days with precipitation equals or exceeds 1 millimeter. A dry event is defined as a period when the number of consecutive days with precipitation equals 0 millimeters. This dataset was derived from Daymet data.","doi_url":"https://doi.org/10.5066/P9ZWQ48W","domain":["Climate"],"draft":false,"id":"b2b4c8d0-1811-4274-a2f3-538511309509","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5c585e8ce4b0708288ff26cc?f=__disk__d7%2F6d%2F07%2Fd76d07fa14eaf120619ce6a0ac61e38d980396b8\u0026allowOpen=true"}],"name":"30 year (1981-2010) annual average duration of consecutive dry and wet days for the Conterminous United States and District of Columbia","permalink":"/catalog/datasets/b2b4c8d0-1811-4274-a2f3-538511309509/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1 kilometer","temporal_coverage":"1981 - 2010","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Precipitation, consecutive dry days","Precipitation, consecutive wet days"],"vars":"Precipitation, consecutive dry days; Precipitation, consecutive wet days","weight":1},{"access":[{"file_format":"CSV; TXT; NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/64120b48d34eb496d1cdcd40"}],"access_details":null,"bbox":{"east":-63.457,"north":49.4967,"south":23.8858,"west":-126.0352},"citation":"Foks, S.S., LaFontaine, J.H., McDonald, R.R., Snyder, A.M., Staub, L.E., Kolb, K.R., LaMotte, A.E., and Viger, R.J., 2024, Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System version 1.1 forced with CONUS404-BA, 1980-2021 (version 2.0, April 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P14C5YAP.","creator":[],"creator_project":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"date_created":"5/3/2024","date_updated":"6/3/2026","description":"\u003cp\u003eThis data release contains 15 variables from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) version 1.1 modeling application forced with CONUS404-BA (Markstrom and others, 2024) from January 1980 through September 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of the conterminous United States. The following fluxes and storages are included: total monthly precipitation, evapotranspiration, lateral flow, surface runoff, interflow, recharge, groundwater flow, and the average monthly snow water equivalent, interflow storage, groundwater storage, total storage, and soil moisture. These data can be found in the “huc12_monthly_nhmprms_conus404ba_1980_2021.nc” file.\u003cbr\u003e\n\u003cbr\u003e\nAdditionally, two supplementary files are also included in this data release. The first file (“weights_hru_to_huc12_nhmprms_conus404ba.csv”) contains the spatial weights or fraction that is used to “weight” the modeling output in the area-weighting process. The second file (“summed_weights_per_huc12_nhmprms_conus404ba.csv”) contains the total fractional area within each twelve-digit hydrologic unit code that is covered by the modeling output and is important for filtering results in the data file (where a fractional coverage may be less than one).\u003c/p\u003e\n","doi_url":"https://doi.org/10.5066/P14C5YAP","domain":["Hydrology"],"draft":false,"id":"b2fc46a1-18db-4370-8515-a5ae6912a2ba","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"f930b17a-c6ea-4623-b89d-d7d85ac698aa","rel_type":"IsDerivedFrom"}],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/64120b48d34eb496d1cdcd40?f=__disk__fe%2F0c%2F43%2Ffe0c43e2321eef016474008ffe1a4b868c1c4fcc\u0026allowOpen=true"}],"name":"Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System version 1.1 forced with CONUS404-BA, 1980-2021 (version 2.0, April 2025)","permalink":"/catalog/datasets/b2fc46a1-18db-4370-8515-a5ae6912a2ba/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1980 - 2021","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["huc_id","time","nhm_ppt_post","nhm_actet_post","nhm_potet_post","nhm_lateral_flow_post","nhm_sroff_post","nhm_ssres_flow_post","nhm_gwres_flow_post","nhm_recharge_post","nhm_pkwater_equiv_post","nhm_gwres_stor_post","nhm_ssres_stor_post","nhm_storage_post","nhm_soil_moisture_total_depth_post","nhm_soil_moisture_depth_post","nhm_soil_moisture_fraction","yrmo","nhm_quickflow","huc_id","nhru_v1_1","weight","huc_id","summed_weight"],"vars":"huc_id; time; nhm_ppt_post; nhm_actet_post; nhm_potet_post; nhm_lateral_flow_post; nhm_sroff_post; nhm_ssres_flow_post; nhm_gwres_flow_post; nhm_recharge_post; nhm_pkwater_equiv_post; nhm_gwres_stor_post; nhm_ssres_stor_post; nhm_storage_post; nhm_soil_moisture_total_depth_post; nhm_soil_moisture_depth_post; nhm_soil_moisture_fraction; yrmo; nhm_quickflow; huc_id; nhru_v1_1; weight; huc_id; summed_weight","weight":1},{"access":[{"file_format":"NC","name":"ncei.noaa.gov","url":"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332"}],"access_details":null,"bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"Vose, R.S., Applequist, S., Squires, M., Durre, I,, Menne, M.J., Williams, C.N. Jr., Fenimore, C., Gleason, K., and Arndt, D., 2014, NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid), Version 1, [indicate subset used], NOAA National Centers for Environmental Information, accessed [YYYY-MM-DD] at https://doi.org/10.7289/V5SX6B56","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. On an annual basis, approximately one year of \"final\" nClimGrid will be submitted to replace the initially supplied \"preliminary\" data for the same time period. Users should be sure to ascertain which level of data is required for their research.","doi_url":"https://doi.org/10.7289/V5SX6B56","domain":["Climate"],"draft":false,"id":"b4832129-f343-4549-a7b3-366472504070","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.ncei.noaa.gov/data/nclimgrid-daily/doc/nclimgrid-daily_v1-0-0_user-guide.pdf"}],"name":"NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid)","permalink":"/catalog/datasets/b4832129-f343-4549-a7b3-366472504070/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NOAA","spatial_extent":"CONUS","spatial_resolution":"5 kilometer","temporal_coverage":"1895 - Present","temporal_frequency":"monthly","update_detail":"append","update_frequency":"monthly","update_type":"Dynamic","variables":["Precipitation"],"vars":"Precipitation","weight":1},{"access":[{"file_format":"ZARR","name":"hytest-org.github.io","url":"https://hytest-org.github.io/hytest/dataset_access/CONUS404_ACCESS.html"},{"file_format":"NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/65ff28d3d34e64ff1548df1b"},{"file_format":"ZARR","name":"WMA STAC","url":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/conus404-pgw"}],"bbox":{"east":-63.1184,"north":52.898,"south":20.1149,"west":-131.1649},"citation":"Xue, L., Rasmussen, R.M., Chen, F., Liu, C., Ikeda, K., Prein, A., Kim, J., Schneider, T., Dai, A., Gochis, D., Dugger, A., Zhang, Y., Jaye, A., Dudhia, J., He, C., Harrold, M., Chen, S., Newman, A., Dougherty, E., Abolafia-Rozenzweig, R., Lybarger, N., Viger, R., Rasmussen, K., and Miguez-Macho, G., 2024, CONUS404 PGW: Four-kilometer long-term regional hydroclimate reanalysis perturbed with pseudo-global warming (PGW) conditions over the conterminous United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9HH85UU.","creator":[],"creator_project":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"date_created":"7/24/2025","date_updated":"6/3/2026","description":"The CONUS404 pseudo-global warming (PGW) dataset is a future-perturbed hydro-climate dataset, created as a follow on to the [CONUS404 dataset](/datasets/68d031e8-4101-4cdf-952a-657396e92a5a/). The CONUS404 PGW dataset represents the weather from 1980 to 2021 under a warmer and wetter climate environment and provides an opportunity to explore the event-based climate change impacts when used with the CONUS404 historical data.  This dataset has sufficient temporal and spatial detail to resolve probable mesoscale atmospheric states and processes in a future warmer climate, making it appropriate for forcing hydrological models and conducting meteorological analyses to study one possible scenario of climate changes and resultant hydrologic impacts over the conterminous United States (CONUS). CONUS404 PGW was produced by the National Center for Atmospheric Research (NCAR) Weather Research and Forecasting (WRF) Model simulations, forced with ERA5 reanalysis data plus LENS2 projected climate perturbations (additional details of the simulation and cross-references to the input datasets are described in the metadata).\u003cbr\u003eThis simulation was run by National Center for Atmospheric Research (NCAR) as part of a collaboration with the U.S. Geological Survey (USGS) Water Mission Area. CONUS404 PGW includes 42 years of data (water years 1980-2021, October 1, 1979 - September 30, 2021), and the spatial domain extends beyond the CONUS into Canada and Mexico, thereby capturing transboundary river basins and covering all contributing areas for the CONUS surface waters.","doi_url":"https://doi.org/10.5066/P9HH85UU","domain":["Climate"],"draft":false,"id":"b697c2e0-919d-40e8-a070-78618f5356d1","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"68d031e8-4101-4cdf-952a-657396e92a5a","rel_type":"IsVariantFormOf"},{"id":"9d0a986c-c1a6-4e21-87e7-188ea8d0a636","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[{"id":"148a7db7-1175-47aa-8437-5ae39f62b1ce","rel_type":"IsSourceOf"}],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/65ff28d3d34e64ff1548df1b?f=__disk__e6%2F57%2Ffd%2Fe657fdf62365c2fe2258bffe079e2156f0258559\u0026allowOpen=true"}],"name":"CONUS404 PGW: Four-kilometer long-term regional hydroclimate reanalysis perturbed with pseudo-global warming (PGW) conditions over the conterminous United States","permalink":"/catalog/datasets/b697c2e0-919d-40e8-a070-78618f5356d1/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_status":"Released","source":"USGS; NCAR","spatial_extent":"CONUS","spatial_resolution":"4 kilometer","temporal_coverage":"1979 - 2021","temporal_frequency":"hourly; daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Accumulated canopy dew rate","Accumulated canopy precipitation drip rate","Accumulated canopy snow drip rate","Accumulated net evaporation of canopy water (evap + sublim - dew - frost)","Accumulated net soil evaporation or snowpack sublimation (evap or sublim - dew or frost)","Accumulated energy influx from soil bottom","Accumulated total evaporation","Accumulated plant transpiration","Accumulated canopy evaporation","Accumulated latent heat flux over bare ground","Accumulated latent heat flux for canopy layer","Accumulated ground latent heat flux below canopy","Accumulated canopy frost","Accumulated refreezing of canopy liquid water","Accumulated heat flux into soil or snowpack for bare ground","Accumulated total ground heat flux into soil or snowpack","Accumulated heat flux into soil or snowpack under canopy","Accumulated ground heat flux","Accumulated upward sensible heat flux at the surface","Accumulated canopy rain interception rate","Accumulated canopy snow interception rate","Accumulated net longwave radiation for bare ground","Accumulated net longwave radiation from canopy","Accumulated net longwave radiation from ground below canopy","Accumulated upward latent heat flux at the surface","Accumulated total latent heat flux","Accumulated downwelling longwave radiation flux at bottom","Accumulated downwelling clear sky longwave radiation flux at bottom","Accumulated longwave downwelling radiation at land surface model ","Accumulated downwelling longwave radiation flux at top","Accumulated downwelling clear sky longwave radiation flux at top","Accumulated upwelling longwave radiation flux at bottom","Accumulated upwelling clear sky longwave radiation flux at bottom","Accumulated longwave upwelling radiation at land surface model","Accumulated upwelling longwave radiation flux at top","Accumulated upwelling clear sky longwave radiation flux at top","Accumulated canopy snow melt","Accumulated precipitation advected energy to bare ground","Accumulated precipitation advected energy to below canopy","Accumulated total precipitation heat flux advected to surface","Accumulated precipitation advected energy to vegetation","Accumulated surface ponding from complete pack melt","Accumulated groundwater lateral flow","Accumulated groundwater baseflow","Accumulated liquid precipitation into land surface model","Accumulated rain on snow pack","Accumulated subsurface runoff","Accumulated surface runoff","Accumulated solar radiation absorbed by bare ground","Accumulated solar radiation absorbed by vegetated ground","Accumulated solar radiation absorbed by vegetated fraction","Accumulated sensible heat flux at bare fraction","Accumulated sensible heat flux, canopy to atmosphere","Accumulated total sensible heat flux","Accumulated sensible heat flux from ground below canopy","Accumulated liquid water flux out of bottom of snowpack","Accumulated snowpack frost","Accumulated total liquid water out of the snowpack","Accumulated frozen precipitation into land surface model","Accumulated snowpack sublimation","Accumulated canopy snow sublimation","Accumulated downwelling shortwave radiation flux at bottom","Accumulated downwelling clear sky shortwave radiation flux at bottom","Accumulated shortwave radiation down at land surface model","Accumulated downwelling shortwave radiation flux at top","Accumulated downwelling clear sky shortwave radiation flux at top","Accumulated upwelling shortwave radiation flux at bottom","Accumulated upwelling clear sky shortwave radiation flux at bottom","Accumulated shortwave radiation up at land surface model","Accumulated upwelling shortwave radiation flux at top","Accumulated upwelling clear sky shortwave radiation flux at top","Accumulated canopy rain throughfall","Accumulated canopy snow throughfall","Accumulated transpiration","Background surface albedo","Surface albedo including snow effects","Full levels, bf=0 =\u003e isobaric","bf=znw =\u003e sigma","Half levels, bh=0 =\u003e isobaric","bh=znu =\u003e sigma","Full levels, c1f = d bf / d eta, using znu","Half levels, c1h = d bf / d eta, using znw","Full levels, c2f = (1-c1f)*(p0-pt)","Half levels, c2h = (1-c1h)*(p0-pt)","Full levels, c3f = bf","Half levels, c3h = bh","Full levels, c4f = (eta-bf)*(p0-pt)+pt, using znw","Half levels, c4h = (eta-bh)*(p0-pt)+pt, using znu","Canopy intercepted ice mass","Canopy intercepted water","2nd order extrapolation constant","2nd order extrapolation constant","2nd order extrapolation constant","Extrapolation constant","Extrapolation constant","Computational grid latitude, south is negative","Cloud fraction","Local cosine of map rotation","Cosine of solar zenith angle","D(eta) values between half (mass) levels","D(eta) values between full (w) levels","Thickness of soil layers","Coriolis cosine latitude term","Surface emissivity","Coriolis sine latitude term","Upper weight for vertical stretching","Lower weight for vertical stretching","Lowest model pressure into land surface model","Lowest model mixing ratio into land surface model","Lowest model temperature into land surface model","Lowest model wind speed into land surface model","Lowest model height above ground level (AGL) into land surface model","Downward long wave flux at ground surface","Accumulated total grid scale graupel","Accumulated graupel water equivalent","Ground heat flux","Maximum column-integrated graupel","Accumulated total grid scale hail","Maximum hail diameter entire column","Maximum hail diameter K=1","Upward heat flux at the surface","Single-column model (SCM) ideal surface sensible heat flux","Single-column model (SCM) ideal surface sensible heat flux tendency","Terrain Height","Array to hold seed for restart, RAND_PERT2","Array to hold seed for restart, RAND_PERT4","Array to hold seed for restart, RAND_PERT3","Array to hold seed for restart, RAND_PERT","Array to hold seed for restart, stochastic kinetic-energy backscatter scheme (SKEBS)","Array to hold seed for restart, stochastically perturbed physics tendencies (SPPT)","Dominant soil category","Daily maximum water vapor mixing ratio at 2 meters","Daily mean water vapor mixing ratio at 2 meters","Daily minimum water vapor mixing ratio at 2 meters","Daily standard deviation of water vapor mixing ratio at 2 meters","Daily maximum cumulus precipitation flux","Daily mean cumulus precipitation flux","Daily standard deviation of cumulus precipitation flux","Daily maximum grid scale precipitation flux","Daily mean grid scale precipitation flux","Daily standard deviation of grid scale precipitation","Daily maximum skin temperature","Daily mean skin temperature","Daily minimum skin temperature","Daily standard deviation of skin temperature","Daily maximum wind speed at 10 meters","Daily mean wind speed at 10 meters","Daily standard deviation of wind speed at 10 meters","Daily maximum temperature at 2 meters","Daily mean temperature at 2 meters","Daily minimum temperature at 2 meters","Daily standard deviation of temperature at 2 meters","Time of daily maximum water vapor mixing ratio at 2 meters","Time of daily minimum water vapor mixing ratio at 2 meters","Time of daily maximum cumulus precipitation flux","Time of daily maximum grid scale precipitation flux","Time of daily maximum skin temperature","Time of daily minimum skin temperature","Time of daily maximum wind speed at 10 meters","Time of daily maximum temperature at 2 meters","Time of daily minimum temperature at 2 meters","Model time in string format (YYYY-MM-DD_hh:mm:ss)","Daily maximum U-component of wind at 10 meters with respect to model grid","Daily mean U-component of wind at 10 meters with respect to model grid","Daily standard deviation of U-component of wind at 10 meters with respect to model grid","Daily maximum V-component of wind at 10 meters with respect to model grid","Daily mean V-component of wind at 10 meters with respect to model grid","Daily standard deviation of V-component of wind at 10 meters with respect to model grid","Daily maximum water vapor mixing ratio at 2 meters","Daily mean water vapor mixing ratio at 2 meters","Daily minimum water vapor mixing ratio at 2 meters","Daily standard deviation of water vapor mixing ratio at 2 meters","Daily maximum cumulus precipitation flux","Daily mean cumulus precipitation flux","Daily standard deviation of cumulus precipitation flux","Daily maximum grid scale precipitation flux","Daily mean grid scale precipitation flux","Daily standard deviation of grid scale precipitation","Daily maximum skin temperature","Daily mean skin temperature","Daily minimum skin temperature","Daily standard deviation of skin temperature","Daily maximum wind speed at 10 meters","Daily mean wind speed at 10 meters","Daily standard deviation of wind speed at 10 meters","Daily maximum temperature at 2 meters","Daily mean temperature at 2 meters","Daily minimum temperature at 2 meters","Daily standard deviation of temperature at 2 meters","Time of daily maximum water vapor mixing ratio at 2 meters","Time of daily minimum water vapor mixing ratio at 2 meters","Time of daily maximum cumulus precipitation flux","Time of daily maximum grid scale precipitation flux","Time of daily maximum skin temperature","Time of daily minimum skin temperature","Time of daily maximum wind speed at 10 meters","Time of daily maximum temperature at 2 meters","Time of daily minimum temperature at 2 meters","Model time in string format (YYYY-MM-DD_hh:mm:ss)","Daily maximum U-component of wind at 10 meters with respect to model grid","Daily mean U-component of wind at 10 meters with respect to model grid","Daily standard deviation of U-component of wind at 10 meters with respect to model grid","Daily maximum V-component of wind at 10 meters with respect to model grid","Daily mean V-component of wind at 10 meters with respect to model grid","Daily standard deviation of V-component of wind at 10 meters with respect to model grid"],"vars":"Accumulated canopy dew rate; Accumulated canopy precipitation drip rate; Accumulated canopy snow drip rate; Accumulated net evaporation of canopy water (evap + sublim - dew - frost); Accumulated net soil evaporation or snowpack sublimation (evap or sublim - dew or frost); Accumulated energy influx from soil bottom; Accumulated total evaporation; Accumulated plant transpiration; Accumulated canopy evaporation; Accumulated latent heat flux over bare ground; Accumulated latent heat flux for canopy layer; Accumulated ground latent heat flux below canopy; Accumulated canopy frost; Accumulated refreezing of canopy liquid water; Accumulated heat flux into soil or snowpack for bare ground; Accumulated total ground heat flux into soil or snowpack; Accumulated heat flux into soil or snowpack under canopy; Accumulated ground heat flux; Accumulated upward sensible heat flux at the surface; Accumulated canopy rain interception rate; Accumulated canopy snow interception rate; Accumulated net longwave radiation for bare ground; Accumulated net longwave radiation from canopy; Accumulated net longwave radiation from ground below canopy; Accumulated upward latent heat flux at the surface; Accumulated total latent heat flux; Accumulated downwelling longwave radiation flux at bottom; Accumulated downwelling clear sky longwave radiation flux at bottom; Accumulated longwave downwelling radiation at land surface model ; Accumulated downwelling longwave radiation flux at top; Accumulated downwelling clear sky longwave radiation flux at top; Accumulated upwelling longwave radiation flux at bottom; Accumulated upwelling clear sky longwave radiation flux at bottom; Accumulated longwave upwelling radiation at land surface model; Accumulated upwelling longwave radiation flux at top; Accumulated upwelling clear sky longwave radiation flux at top; Accumulated canopy snow melt; Accumulated precipitation advected energy to bare ground; Accumulated precipitation advected energy to below canopy; Accumulated total precipitation heat flux advected to surface; Accumulated precipitation advected energy to vegetation; Accumulated surface ponding from complete pack melt; Accumulated groundwater lateral flow; Accumulated groundwater baseflow; Accumulated liquid precipitation into land surface model; Accumulated rain on snow pack; Accumulated subsurface runoff; Accumulated surface runoff; Accumulated solar radiation absorbed by bare ground; Accumulated solar radiation absorbed by vegetated ground; Accumulated solar radiation absorbed by vegetated fraction; Accumulated sensible heat flux at bare fraction; Accumulated sensible heat flux, canopy to atmosphere; Accumulated total sensible heat flux; Accumulated sensible heat flux from ground below canopy; Accumulated liquid water flux out of bottom of snowpack; Accumulated snowpack frost; Accumulated total liquid water out of the snowpack; Accumulated frozen precipitation into land surface model; Accumulated snowpack sublimation; Accumulated canopy snow sublimation; Accumulated downwelling shortwave radiation flux at bottom; Accumulated downwelling clear sky shortwave radiation flux at bottom; Accumulated shortwave radiation down at land surface model; Accumulated downwelling shortwave radiation flux at top; Accumulated downwelling clear sky shortwave radiation flux at top; Accumulated upwelling shortwave radiation flux at bottom; Accumulated upwelling clear sky shortwave radiation flux at bottom; Accumulated shortwave radiation up at land surface model; Accumulated upwelling shortwave radiation flux at top; Accumulated upwelling clear sky shortwave radiation flux at top; Accumulated canopy rain throughfall; Accumulated canopy snow throughfall; Accumulated transpiration; Background surface albedo; Surface albedo including snow effects; Full levels, bf=0 =\u003e isobaric; bf=znw =\u003e sigma; Half levels, bh=0 =\u003e isobaric; bh=znu =\u003e sigma; Full levels, c1f = d bf / d eta, using znu; Half levels, c1h = d bf / d eta, using znw; Full levels, c2f = (1-c1f)*(p0-pt); Half levels, c2h = (1-c1h)*(p0-pt); Full levels, c3f = bf; Half levels, c3h = bh; Full levels, c4f = (eta-bf)*(p0-pt)+pt, using znw; Half levels, c4h = (eta-bh)*(p0-pt)+pt, using znu; Canopy intercepted ice mass; Canopy intercepted water; 2nd order extrapolation constant; 2nd order extrapolation constant; 2nd order extrapolation constant; Extrapolation constant; Extrapolation constant; Computational grid latitude, south is negative; Cloud fraction; Local cosine of map rotation; Cosine of solar zenith angle; D(eta) values between half (mass) levels; D(eta) values between full (w) levels; Thickness of soil layers; Coriolis cosine latitude term; Surface emissivity; Coriolis sine latitude term; Upper weight for vertical stretching; Lower weight for vertical stretching; Lowest model pressure into land surface model; Lowest model mixing ratio into land surface model; Lowest model temperature into land surface model; Lowest model wind speed into land surface model; Lowest model height above ground level (AGL) into land surface model; Downward long wave flux at ground surface; Accumulated total grid scale graupel; Accumulated graupel water equivalent; Ground heat flux; Maximum column-integrated graupel; Accumulated total grid scale hail; Maximum hail diameter entire column; Maximum hail diameter K=1; Upward heat flux at the surface; Single-column model (SCM) ideal surface sensible heat flux; Single-column model (SCM) ideal surface sensible heat flux tendency; Terrain Height; Array to hold seed for restart, RAND_PERT2; Array to hold seed for restart, RAND_PERT4; Array to hold seed for restart, RAND_PERT3; Array to hold seed for restart, RAND_PERT; Array to hold seed for restart, stochastic kinetic-energy backscatter scheme (SKEBS); Array to hold seed for restart, stochastically perturbed physics tendencies (SPPT); Dominant soil category; Daily maximum water vapor mixing ratio at 2 meters; Daily mean water vapor mixing ratio at 2 meters; Daily minimum water vapor mixing ratio at 2 meters; Daily standard deviation of water vapor mixing ratio at 2 meters; Daily maximum cumulus precipitation flux; Daily mean cumulus precipitation flux; Daily standard deviation of cumulus precipitation flux; Daily maximum grid scale precipitation flux; Daily mean grid scale precipitation flux; Daily standard deviation of grid scale precipitation; Daily maximum skin temperature; Daily mean skin temperature; Daily minimum skin temperature; Daily standard deviation of skin temperature; Daily maximum wind speed at 10 meters; Daily mean wind speed at 10 meters; Daily standard deviation of wind speed at 10 meters; Daily maximum temperature at 2 meters; Daily mean temperature at 2 meters; Daily minimum temperature at 2 meters; Daily standard deviation of temperature at 2 meters; Time of daily maximum water vapor mixing ratio at 2 meters; Time of daily minimum water vapor mixing ratio at 2 meters; Time of daily maximum cumulus precipitation flux; Time of daily maximum grid scale precipitation flux; Time of daily maximum skin temperature; Time of daily minimum skin temperature; Time of daily maximum wind speed at 10 meters; Time of daily maximum temperature at 2 meters; Time of daily minimum temperature at 2 meters; Model time in string format (YYYY-MM-DD_hh:mm:ss); Daily maximum U-component of wind at 10 meters with respect to model grid; Daily mean U-component of wind at 10 meters with respect to model grid; Daily standard deviation of U-component of wind at 10 meters with respect to model grid; Daily maximum V-component of wind at 10 meters with respect to model grid; Daily mean V-component of wind at 10 meters with respect to model grid; Daily standard deviation of V-component of wind at 10 meters with respect to model grid; Daily maximum water vapor mixing ratio at 2 meters; Daily mean water vapor mixing ratio at 2 meters; Daily minimum water vapor mixing ratio at 2 meters; Daily standard deviation of water vapor mixing ratio at 2 meters; Daily maximum cumulus precipitation flux; Daily mean cumulus precipitation flux; Daily standard deviation of cumulus precipitation flux; Daily maximum grid scale precipitation flux; Daily mean grid scale precipitation flux; Daily standard deviation of grid scale precipitation; Daily maximum skin temperature; Daily mean skin temperature; Daily minimum skin temperature; Daily standard deviation of skin temperature; Daily maximum wind speed at 10 meters; Daily mean wind speed at 10 meters; Daily standard deviation of wind speed at 10 meters; Daily maximum temperature at 2 meters; Daily mean temperature at 2 meters; Daily minimum temperature at 2 meters; Daily standard deviation of temperature at 2 meters; Time of daily maximum water vapor mixing ratio at 2 meters; Time of daily minimum water vapor mixing ratio at 2 meters; Time of daily maximum cumulus precipitation flux; Time of daily maximum grid scale precipitation flux; Time of daily maximum skin temperature; Time of daily minimum skin temperature; Time of daily maximum wind speed at 10 meters; Time of daily maximum temperature at 2 meters; Time of daily minimum temperature at 2 meters; Model time in string format (YYYY-MM-DD_hh:mm:ss); Daily maximum U-component of wind at 10 meters with respect to model grid; Daily mean U-component of wind at 10 meters with respect to model grid; Daily standard deviation of U-component of wind at 10 meters with respect to model grid; Daily maximum V-component of wind at 10 meters with respect to model grid; Daily mean V-component of wind at 10 meters with respect to model grid; Daily standard deviation of V-component of wind at 10 meters with respect to model grid","weight":1},{"access":[{"file_format":"GPKG; GDB; CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/63cb38b2d34e06fef14f40ad"}],"access_details":"The snapshot is available through the sciencebase.gov access url as a component of a data release which links HU12 units to the river network.","bbox":{"east":-65.2148,"north":49.838,"south":24.2069,"west":-126.2109},"citation":null,"creator":[],"creator_project":[],"date_created":"2/28/2025","date_updated":"6/3/2026","description":"National Water Availability Assessment WBD Snapshot\u003cbr\u003eIn 2020, USGS used a snapshot of the latest available version of the WBD in their hydrologic and water use models that were included in the first national water availability assessment.\u003cbr\u003eThis was done to take advantage of updates and additions that were not available in the NHDPlusV2 version of the dataset. The national water availability assessments estimate how much water is available for human and ecological needs in the United States and explores where, when, and why the Nation may have challenges meeting its demand for water. The assessments currently use HU12 units as the spatial summary layer.\u003cbr\u003eWhat is the Watershed Boundary Dataset (WBD)\u003cbr\u003eThe Watershed Boundary Dataset (WBD) is a seamless, national, hydrologic unit dataset that provides a standardized base for water-resources organizations to locate, store, retrieve, and exchange hydrologic data; to index and inventory hydrologic data and information; to catalog water-data acquisition activities; and to use in a variety of other applications. Hydrologic unit boundaries in the WBD are determined based on topographic, hydrologic, and other relevant landscape characteristics without regard for administrative, political, or jurisdictional boundaries.\u003cbr\u003eThe hydrologic units (HU) in the WBD are arranged in a nested, hierarchical system with each HU in the system identified using a unique hydrologic unit code (HUC). Each HU within a nested level is assigned a two-digit suffix that’s appended to the HUC of the HU in the next coarsest nesting level. Because there are eight nesting levels within WBD, the set of HUCs consists of ranges from two to sixteen digits based on the eight levels of classification in the WBD. The dataset is complete for the United States to the 12-digit hydrologic unit. The 14- and 16-digit hydrologic units have only been published for a subset of the nation. https://www.usgs.gov/media/images/watershed-boundary-dataset-structure-visualization","doi_url":null,"domain":["Hydrology"],"draft":false,"id":"b8598469-4e1b-4e5a-88f4-550d9a7eb494","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"ae9546b9-1eda-407a-9129-8a62b43ac056","rel_type":"IsSourceOf"},{"id":"553e2bbb-00ea-4052-b472-5561943d5dc6","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://water.usgs.gov/usgs/themes-internal/hydrologic-units/"}],"name":"WBD National IWAAs Snapshot","permalink":"/catalog/datasets/b8598469-4e1b-4e5a-88f4-550d9a7eb494/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["OBJECTID","METASOURCEID","SOURCEDATADESC","SOURCEORIGINATOR","SOURCEFEATUREID","LOADDATE","GNIS_ID","AREAACRES","AREASQKM","STATES","HUC12","NAME","HUTYPE","HUMOD","TOHUC","NONCONTRIBUTINGAREAACRES","NONCONTRIBUTINGAREASQKM"],"vars":"OBJECTID; METASOURCEID; SOURCEDATADESC; SOURCEORIGINATOR; SOURCEFEATUREID; LOADDATE; GNIS_ID; AREAACRES; AREASQKM; STATES; HUC12; NAME; HUTYPE; HUMOD; TOHUC; NONCONTRIBUTINGAREAACRES; NONCONTRIBUTINGAREASQKM","weight":1},{"access":[{"file_format":"HDF","name":"nsidc.org","url":"https://nsidc.org/data/spl3smp_e/versions/6"}],"access_details":"To access these data at the nsidc.org access link, a free NASA Earthdata Login account is required to access these data. Register for a profile here: https://urs.earthdata.nasa.gov/","bbox":{"east":180,"north":90,"south":-85.044,"west":-180},"citation":"O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., Chaubell, J., and Colliander, A., 2023, SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture. (SPL3SMP_E, Version 6). [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/M20OXIZHY3RJ. [describe subset used if applicable]. Date Accessed [YYYY-MM-DD].","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection.","doi_url":"https://doi.org/10.5067/M20OXIZHY3RJ","domain":["Soils"],"draft":false,"id":"b8605ef0-1be6-4556-8575-2d2778aa622a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://nsidc.org/data/SPL3SMP_E/versions/6"}],"name":"SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture, Version 6","permalink":"/catalog/datasets/b8605ef0-1be6-4556-8575-2d2778aa622a/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NSIDC","spatial_extent":"Global","spatial_resolution":"9 kilometer","temporal_coverage":"2015 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Soil moisture"],"vars":"Soil moisture","weight":1},{"access":[{"file_format":"TIF","name":"cec.org","url":"http://www.cec.org/north-american-environmental-atlas/land-cover-2010-landsat-30m/"}],"access_details":null,"bbox":{"east":-53,"north":72,"south":14,"west":-180},"citation":"Commission for Environmental Cooperation (CEC), 2020, 2010 Land Cover of North America at 30 meters. North American Land Change Monitoring System, Canada Centre for Remote Sensing (CCRS), U.S. Geological Survey (USGS), Comision Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO), Comision Nacional Forestal (CONAFOR), Instituto Nacional de Estadistica y Geografia (INEGI) Edition 2.0, Raster digital data [30-m], accessed [YYYY-MM-DD] at http://www.cec.org/north-american-environmental-atlas/land-cover-2010-landsat-30m/","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The 2010 North American Land Cover 30-meter dataset was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia), National Commission for the Knowledge and Use of the Biodiversity (Comision Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comision Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries.\u003cbr\u003eThe general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country's specific requirements.\u003cbr\u003eThis 30-meter dataset of North American Land Cover reflects land cover information for 2010 from Mexico and Canada and 2011 for the United States. Each country developed its own classification method to identify Land Cover classes and then provided an input layer to produce a continental Land Cover map across North America. Canada, Mexico, and the United States developed their own 30-meter land cover products; see specific sections on data generation in the metadata.\u003cbr\u003eThe main inputs for image classification were 30-meter Landsat data. Image selection processes and reduction to specific spectral bands varied among the countries due to study-site-specific requirements. While Canada selected most images from the year 2010 with a few from 2009 and 2011, the United States employed mainly images from 2011. Mexico used all available images from 2010.\u003cbr\u003eIn order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by CONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each country chose their own approaches, ancillary data, and land cover mapping methodologies to create national datasets. This North America dataset was produced by combining the national land cover datasets.","doi_url":null,"domain":["Land Cover"],"draft":false,"id":"b879ea6c-0f87-4979-a3a7-4839fb029b4c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Land cover 2010 (Landsat)","permalink":"/catalog/datasets/b879ea6c-0f87-4979-a3a7-4839fb029b4c/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"CEC","spatial_extent":"North America","spatial_resolution":"30 meter","temporal_coverage":"2010","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Temperate or sub-polar needleleaf forest","Sub-polar taiga needleleaf forest","Tropical or sub-tropical broadleaf evergreen forest","Tropical or sub-tropical broadleaf deciduous forest","Temperate or sub-polar broadleaf deciduous forest","Mixed Forest","Tropical or sub-tropical shrubland","Temperate or sub-polar shrubland","Tropical or sub-tropical grassland","Temperate or sub-polar grassland","Sub-polar or polar shrubland-lichen-moss","Sub-polar or polar grassland-lichen-moss","Sub-polar or polar barren-lichen-moss","Wetland","Cropland","Barren lands","Urban","Water","Snow and Ice"],"vars":"Temperate or sub-polar needleleaf forest; Sub-polar taiga needleleaf forest; Tropical or sub-tropical broadleaf evergreen forest; Tropical or sub-tropical broadleaf deciduous forest; Temperate or sub-polar broadleaf deciduous forest; Mixed Forest; Tropical or sub-tropical shrubland; Temperate or sub-polar shrubland; Tropical or sub-tropical grassland; Temperate or sub-polar grassland; Sub-polar or polar shrubland-lichen-moss; Sub-polar or polar grassland-lichen-moss; Sub-polar or polar barren-lichen-moss; Wetland; Cropland; Barren lands; Urban; Water; Snow and Ice","weight":1},{"access":[{"file_format":"XLSX","name":"clearroads.org","url":"https://clearroads.org/winter-maintenance-survey/"}],"access_details":null,"bbox":{},"citation":null,"creator":[],"creator_project":[],"description":"Annual survey compiling winter maintenance resources, materials, and cost data from state DOTs. Includes tons of salt applied, total winter costs, lane miles maintained, and winter severity index data. Data collected from participating states since the 2014-2015 winter season.","doi_url":null,"domain":["Infrastructure"],"draft":false,"id":"b87b9cb7-5595-4e1a-b64b-762665b99a72","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"553e2bbb-00ea-4052-b472-5561943d5dc6","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Clear Roads Annual Survey of State Winter Maintenance Data","permalink":"/catalog/datasets/b87b9cb7-5595-4e1a-b64b-762665b99a72/","project_use_history":[],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Clear Roads","spatial_extent":"United States","spatial_resolution":"NA","temporal_coverage":"2014 - Present","temporal_frequency":"annual","update_detail":"append","update_frequency":"annual","update_type":"Dynamic","variables":[],"vars":"salt applied; winter maintenance costs; lane miles; winter severity","weight":1},{"access":[{"file_format":"CSV","name":"waterqualitydata.us","url":"https://www.waterqualitydata.us/"}],"access_details":null,"bbox":{"east":-53,"north":72,"south":14,"west":-180},"citation":"General citation:\u003cbr\u003eNational Water Quality Monitoring Council, United States Geological Survey (USGS), Environmental Protection Agency (EPA), 2021, Water Quality Portal:  https://doi.org/10.5066/P9QRKUVJ\u003cbr\u003e\u003cbr\u003eCitations for specific datasets:\u003cbr\u003eNational Water Quality Monitoring Council, YYYY, Water Quality Portal: accessed [YYYY-MM-DD] [hyperlink_for_query], https://doi.org/10.5066/P9QRKUVJ","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Water Quality Portal (WQP) is the premiere source of discrete water-quality data in the United States and beyond. This cooperative service integrates publicly available water-quality data from the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and over 400 state, federal, tribal, and local agencies.","doi_url":"https://doi.org/10.5066/P9QRKUVJ","domain":["Water Quality"],"draft":false,"id":"b971c11a-0d87-496b-bb11-d78a950fcc9e","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.waterqualitydata.us/portal_userguide/"}],"name":"Water Quality Portal (WQP)","permalink":"/catalog/datasets/b971c11a-0d87-496b-bb11-d78a950fcc9e/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS; EPA","spatial_extent":"United States","spatial_resolution":"NA","temporal_coverage":"2025","temporal_frequency":"varies","update_detail":"append and modify","update_frequency":"irregular - as new data is added or data contributors make improvements to data quality","update_type":"Dynamic","variables":["Water-quality parameters"],"vars":"Water-quality parameters","weight":1},{"access":[{"file_format":"NC","name":"disc.gsfc.nasa.gov","url":"https://disc.gsfc.nasa.gov/datasets/LPRM_AMSR2_DS_D_SOILM3_001/summary"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Owe, M., de Jeu, R., and Holmes, T., 2008, Multisensor historical climatology of satellite-derived global land surface moisture, Journal of Geophysical Research, F01002, https://doi.org/10.1029/2007JF000769","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 10 km x 10 km descending V001 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the nighttime product. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present. The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, nighttime passes, as processed using LPRM (i.e., LPRM/AMSR2/GCOM-W1 Downscaled Level 2 product, LPRM_AMSR2_DS_SOILM2_V001).","doi_url":"https://doi.org/10.1029/2007JF000769","domain":["Soils"],"draft":false,"id":"b97f8410-7e5f-4060-854b-649c5f5438b8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://hydro1.gesdisc.eosdis.nasa.gov/opendap/LPRM_AMSR2_DS_D_SOILM3.001/doc/README_LPRM.pdf"}],"name":"AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 10 km x 10 km descending V001 (LPRM_AMSR2_DS_D_SOILM3)","permalink":"/catalog/datasets/b97f8410-7e5f-4060-854b-649c5f5438b8/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"10 kilometer","temporal_coverage":"2012 - 2021","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Soil moisture"],"vars":"Soil moisture","weight":1},{"access":[{"file_format":"GPKG; GDB","name":"usgs.gov","url":"https://www.usgs.gov/3d-hydrography-program/access-3dhp-data-products"}],"access_details":"3DHP products are available in two forms – web services and a downloadable product.  New Elevation-Derived Hydrography (EDH) data and attributes will be added to the services quarterly, and the downloadable product will be updated early each Fiscal Year.  The annual downloadable products will remain available for reference with Digital Object Identifiers.","bbox":{"east":-65,"north":71.7,"south":24.3,"west":-179.2311},"citation":"U.S. Geological Survey, 2025, USGS 3D Hydrography Program (3DHP) Downloadable Datasets 2024: U.S. Geological Survey, https://doi.org/10.5066/P148NT7B.","creator":[],"creator_project":[],"date_created":"1/26/2026","date_updated":"6/3/2026","description":"The 3D Hydrography Program (3DHP) data is an integrated, National, 3D-enabled hydrologic dataset derived from the USGS 3D Elevation Program (3DEP) data. For areas where Elevation-derived Hydrography (EDH) has not yet been collected, 3DHP data is supplemented by hydrologic vector data from the National Hydrography Dataset (NHD). As further EDH data is collected, it will replace the NHD data in those areas. 3DHP data ingested from EDH sources includes ‘value added’ catchments and flowline network derivative attributes. All the data is open and non-proprietary. However, users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations. This dataset is not intended to be used for site-specific regulatory determinations. 3DHP datasets include a three-dimensional (3D) hydrography network generated from, and integrated with, elevation data from the 3DEP to better represent stream gradients and channel conditions, along with waterbodies, hydrologic units, hydrologically enhanced elevation and other surfaces, and more consistent and accurate attributes. This product is new in federal fiscal year 2025 (FY25), and consists only of vector data in a series of feature classes. The product represents the 3DHP dataset and the schema in which it is contained as of September 30, 2024 Future Annual Staged Product releases will reflect the schema at the time the product is generated and include more EDH-sourced data holdings.","doi_url":"https://doi.org/10.5066/P148NT7B","domain":["Hydrology"],"draft":false,"id":"bb80499b-77c5-41f3-9877-94d2b08be3ad","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://thor-f5.er.usgs.gov/ngtoc/metadata/waf/hydrography/3dhp/annual/3dhp_all_CONUS_20250313_GPKG.xml"}],"name":"3D Hydrography Program (3DHP) 2024 Staged Product (FY25 Release)","permalink":"/catalog/datasets/bb80499b-77c5-41f3-9877-94d2b08be3ad/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"United States and territories","spatial_resolution":"varies","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["OBJECTID","id3dhp","featuredate","mainstemid","gnisid","gnisidlabel","featuretype","featuretypelabel","lengthkm","waterbodyid3dhp","flowdirection","flowdirectionlabel","onsurface","onsurfacelabel","catchmentid3dhp","flowpathid3dhp","streamlevel","startflag","terminalflag","streamorder","streamcalculator","hydrosequence","dnhydrosequence","uphydrosequence","dnlevelpath","uplevelpath","pathlength","arbolatesum","divergence","divergencelabel","rtrndivergence","levelpath","terminalpath","workunitid","shape_Length"],"vars":"OBJECTID; id3dhp; featuredate; mainstemid; gnisid; gnisidlabel; featuretype; featuretypelabel; lengthkm; waterbodyid3dhp; flowdirection; flowdirectionlabel; onsurface; onsurfacelabel; catchmentid3dhp; flowpathid3dhp; streamlevel; startflag; terminalflag; streamorder; streamcalculator; hydrosequence; dnhydrosequence; uphydrosequence; dnlevelpath; uplevelpath; pathlength; arbolatesum; divergence; divergencelabel; rtrndivergence; levelpath; terminalpath; workunitid; shape_Length","weight":1},{"access":[{"file_format":"CSV; NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/647a4b69d34eac007b521f89"}],"bbox":{"east":-180,"north":90,"south":-90,"west":179.9999},"citation":"Koczot, K.M., Markstrom, S.L., and McDonald, R.R., 2025, Mean-monthly global gridded calibration targets and parameter values derived from monthly-mean Global Circulation Model (GCM) simulations, January 1980 - November 2022, U.S. Geological Science Base data release, https://doi.org/10.5066/P9U1HPWK.","creator":[],"creator_project":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"date_created":"1/14/2026","date_updated":"6/3/2026","description":"This work contributes to an understanding of the global hydrologic cycle. Global Circulation Model (GCM) data from National Aeronautics and Space Administration (NASA) were processed into 10 mean-monthly global datasets stored in NetCDF formatrepresenting 12 mean-monthly layers ( .nc file extension; https://www.unidata.ucar.edu/software/netcdf/ ). These NetCDF files are used as calibration targets and (or) parameter values for Precipitation Runoff Modeling System (PRMS; pubs.usgs.gov/tm/6b7/pdf/tm6-b7.pdf; https://doi.org/10.5066/P9LVUWDC) National Hydrologic Modeling (NHM) applications ( https://www.usgs.gov/mission-areas/water-resources/science/integrated-water-availability-assessments..Integrated Water Availability Assessments | U.S. Geological Survey (usgs.gov). PRMS is a deterministic, distributed-parameter, physical-process-based modeling system developed to evaluate the response of streamflow and general basin hydrology to various combinations of climate and land use.\u003cbr\u003e\n\u0026nbsp;\u003cbr\u003e\n\u0026nbsp;\u003cbr\u003e\n\u003cstrong\u003eDATA TYPE DESIGNATION\u003c/strong\u003e\u003cbr\u003e\nData layers are labeled according to type, as shown below:\u003cbr\u003e\n.\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; ID\u0026nbsp;\u0026nbsp; Data Type\n\u003col\u003e\n\t\u003cli\u003eAET\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Actual evapotranspiration\u003c/li\u003e\n\t\u003cli\u003eGW\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Groundwater (really baseflow) component of total runoff.\u003c/li\u003e\n\t\u003cli\u003ePET\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Potential evapotranspiration\u003c/li\u003e\n\t\u003cli\u003eSCA\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Snow-cover area (fraction of cell area) from NASA FLDAS.\u003c/li\u003e\n\t\u003cli\u003eSM\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Soil moisture\u003c/li\u003e\n\t\u003cli\u003eSR\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Solar radiation\u003c/li\u003e\n\t\u003cli\u003eSRO\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Surface runoff\u003c/li\u003e\n\t\u003cli\u003eSSR\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Subsurface runoff.\u003c/li\u003e\n\t\u003cli\u003eSWE\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Snow-water equivalent\u003c/li\u003e\n\t\u003cli\u003eSWI\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Soil-water infiltration\u003c/li\u003e\n\u003c/ol\u003e\n\u0026nbsp;\u003cbr\u003e\n\u003cstrong\u003eCONTENTS OF THIS DATA RELEASE:\u003c/strong\u003e\u003cbr\u003e\n\u0026nbsp;\n\u003col\u003e\n\t\u003cli\u003e10 NetCDF Files (1 for each data type).\n\t\u003col\u003e\n\t\t\u003cli\u003eAET_FLDAS_NOAH01_C_GL_M_001_Evap_tavg_monthly.nc\u003c/li\u003e\n\t\t\u003cli\u003eGW_GLDAS_NOAH025_M_2_1_Qsb_acc_monthly.nc\u003c/li\u003e\n\t\t\u003cli\u003ePET_GLDAS_NOAH025_M_2_1_PotEvap_tavg_monthly.nc\u003c/li\u003e\n\t\t\u003cli\u003eSCA_FLDAS_NOAH01_C_GL_M_001_SnowCover_inst_monthly.nc\u003c/li\u003e\n\t\t\u003cli\u003eSM_GLDAS_NOAH025_M_2_1_RootMoist_inst_monthly.nc\u003c/li\u003e\n\t\t\u003cli\u003eSR_M2TMNXRAD_5_12_4_SWGDN_monthly.nc\u003c/li\u003e\n\t\t\u003cli\u003eSRO_FLDAS_NOAH01_C_GL_M_001_Qs_tavg_monthly.nc\u003c/li\u003e\n\t\t\u003cli\u003eSSR_FLDAS_NOAH01_C_GL_M_001_Qsb_tavg_monthly.nc\u003c/li\u003e\n\t\t\u003cli\u003eSWE_FLDAS_NOAH01_C_GL_M_001_SWE_inst_monthly.nc\u003c/li\u003e\n\t\t\u003cli\u003eSWI_M2TMNXLND_5_12_4_QINFIL_monthly.nc\u003c/li\u003e\n\t\u003c/ol\u003e\n\t\u003c/li\u003e\n\t\u003cli\u003eTable_1\u0026nbsp;\u0026nbsp;= metadata details about NASA source data, attributes and period-of-record used to make each mean-monthly\u0026nbsp;NetCDF...\u003c/li\u003e\n\t\u003cli\u003eTable_2\u0026nbsp;\u0026nbsp; = NASA source data references.\u003c/li\u003e\n\u003c/ol\u003e\n","doi_url":"https://doi.org/10.5066/P9U1HPWK","domain":["Climate","Snow","Soils","Hydrology"],"draft":false,"id":"bb91bc3d-8719-42b6-b645-eedc410988aa","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/647a4b69d34eac007b521f89?f=__disk__9b%2F04%2F34%2F9b04349b464627c1996099a1d495ab7a28a7f754\u0026allowOpen=true"}],"name":"Mean-monthly global gridded calibration targets and parameter values derived from monthly-mean Global Circulation Model (GCM) simulations, January 1980 - November 2022","permalink":"/catalog/datasets/bb91bc3d-8719-42b6-b645-eedc410988aa/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"Global","spatial_resolution":"0.5 degrees","temporal_coverage":"1980 - 2022","temporal_frequency":"monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Value","Count","ID","Data type","Domain","File type","Units","Temporal res.","Spatial res, cell size","Model","Period of record (POR)","File  Count"],"vars":"Value; Count; ID; Data type; Domain; File type; Units; Temporal res.; Spatial res, cell size; Model; Period of record (POR); File  Count","weight":1},{"access":[{"file_format":"GRB","name":"iridl.ldeo.columbia.edu","url":"https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/.NCEP-CFSv2/.FORECAST/.PENTAD_SAMPLES/.MONTHLY/datasetdatafiles.html"}],"access_details":"The first time a user tries to access the iridl.ldeo.columbia.edu access point, there is a bug that will prompt the user for a login, but if the user re-copies and pastes the url in their browser, the login screen will disappear and they will be on the access page. Once there, a user can select their desired variable (e.g. Total Precipitation) which will take the user to a page for download options. Select OPeNDAP and the resulting site will provide the user with a url that can be used with xarray to open the data. Please make note that the Data Library page includes a warning that it is being shut down. However, the NMME is part of a small subset of data that will remain accessible through this access point.","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Kirtman, B., Min, D., Infanti, J., Kinter, J., Paolino, D., Zhang, Q., van den Dool, H., Saha, S., Mendez, M., Becker, E., Peng, P., Tripp, P., Huang, J., DeWitt, D., Tippett, M., Barnston, A., Li, S., Rosati, A., Schubert, S., Rienecker, M., Suarez, M., Li, Z., Marshak, J., Lim, Y., Tribbia, J., Pegion, K., Merryfield, W., Denis, B. and Wood, E., 2014, The North American Multimodel Ensemble Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction: Bulletin of the American Meteorological Society v. 95, no. 4, p. 585-601, accessed at https://doi.org/10.1175/BAMS-D-12-00050.1","creator":[],"creator_project":[],"date_created":"4/22/2026","date_updated":"6/8/2026","description":"The National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) is initialized four times per day (0000, 0600, 1200, and 1800 UTC). NCEP upgraded their operational CFS to version 2 on March 30, 2011. This is the same model that was used to create the NCEP Climate Forecast System Reanalysis (CFSR). CFSv2 monthly atmospheric, oceanic and land surface output products are available at 0.3, 0.5, 1.0, 1.9, and 2.5 degree horizontal resolutions as 6-hourly diurnal monthly means (0000, 0600, 1200, and 1800 UTC) and regular full monthly means. For more information about CFS, please see http://cfs.ncep.noaa.gov/.","doi_url":"https://doi.org/10.1175/BAMS-D-12-00050.1","domain":["Climate"],"draft":false,"id":"bc1f3366-3ee2-48c0-9ced-ac48213fd1e4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.1175/BAMS-D-12-00050.1"}],"name":"NCEP-CFSv2 from Models NMME: North American Multi-Model Ensemble (NMME)","permalink":"/catalog/datasets/bc1f3366-3ee2-48c0-9ced-ac48213fd1e4/","project_use_history":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"project_using":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"ref_fabric":false,"release_status":"Released","source":"NOAA","spatial_extent":"Global","spatial_resolution":"1.0 degree","temporal_coverage":"2011 - Present","temporal_frequency":"6 hours","update_detail":"append","update_frequency":"7 days","update_type":"Dynamic","variables":["Temperature","Total precipitation","Geopotential Height at 200 hPa","Sea Surface Tempurature","Forecast Time"],"vars":"Temperature; Total precipitation; Geopotential Height at 200 hPa; Sea Surface Tempurature; Forecast Time","weight":1},{"access":[{"file_format":"CSV","name":"portal.edirepository.org","url":"https://portal.edirepository.org/nis/mapbrowse?packageid=edi.394.6"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Meyer, M.F., M.R. Brousil, S.G. Virdis, X. Yang, A.N. Cramer, P.K. Das, R.P. McClure, S.L. Katz, and S.E. Hampton. 2024. The Extended Global Lake area, Climate, and Population Dataset (GLCP) ver 6. Environmental Data Initiative, Accessed [YYYY-MM-DD] at https://doi.org/10.6073/pasta/e0bf4571ca6cbfb81c3ed7caefc85fc6.","creator":[],"creator_project":[],"date_created":"4/9/2025","date_updated":"6/3/2026","description":"A changing climate and increasing human population necessitate understanding global freshwater availability and temporal variability. To examine lake freshwater availability from local-to-global and monthly-to-decadal scales, we created the Global Lake area, Climate, and Population (GLCP) dataset, which contains annual lake surface area for 1.42 million lakes with paired annual basin-level climate and population data. Building off an existing data product infrastructure, the next generation of the GLCP includes monthly lake ice area, snow basin area, and more climate variables including specific humidity, longwave and shortwave radiation, as well as cloud cover. The new generation of the GLCP continues previous FAIR data efforts by expanding its scripting repository and maintaining unique relational keys for merging with external data products. Compared to the original version, the new GLCP contains an even richer suite of variables capable of addressing disparate analyses of lake water trends at wide spatial and temporal scales.","doi_url":"https://doi.org/10.6073/pasta/e0bf4571ca6cbfb81c3ed7caefc85fc6","domain":["Hydrology"],"draft":false,"id":"c1f272bf-97e2-485d-9dee-83cb90d77300","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://portal.edirepository.org/nis/metadataviewer?packageid=edi.394.6"}],"name":"The Extended Global Lake area, Climate, and Population Dataset (GLCP)","permalink":"/catalog/datasets/c1f272bf-97e2-485d-9dee-83cb90d77300/","project_use_history":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"project_using":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS; Academic Institution(s)","spatial_extent":"Global","spatial_resolution":"unknown","temporal_coverage":"1995 - 2020","temporal_frequency":"monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Unique basin identifier from Hydrobasins","Year of observation","Unique lake identifier from Hydrolakes","Numerical month of observation","Annual seasonal lake surface area","Annual permanent lake surface area","Country","Continent","Lake centroid latitude","Lake centroid longitude","Mean basin-level specific humidity","Mean basin-level precipitation","Mean basin-level temperature","Mean basin-level cloud cover","Mean basin-level shortwave radiation","Mean basin-level longwave radiation","Surface area measurement for a given lake in specific month-year combination","Number of people living within a basin","Minimum percentage of lake ice cover","Maximum percentage of lake ice cover","Mean percentage of lake ice cover","Median percentage of lake ice cover","Minimum percentage of lake ice cover where pixels with less than 15% cover are removed","Maximum percentage of lake ice cover where pixels with less than 15% cover are removed","Number of images","Minimum percentage of basin snow area cover","Mean percentage of basin snow area cover","Maximum percentage of basin snow area cover","Number of valid snow observations","Number of invalid snow observations","Area of a basin covered with snow"],"vars":"Unique basin identifier from Hydrobasins; Year of observation; Unique lake identifier from Hydrolakes; Numerical month of observation; Annual seasonal lake surface area; Annual permanent lake surface area; Country; Continent; Lake centroid latitude; Lake centroid longitude; Mean basin-level specific humidity; Mean basin-level precipitation; Mean basin-level temperature; Mean basin-level cloud cover; Mean basin-level shortwave radiation; Mean basin-level longwave radiation; Surface area measurement for a given lake in specific month-year combination; Number of people living within a basin; Minimum percentage of lake ice cover; Maximum percentage of lake ice cover; Mean percentage of lake ice cover; Median percentage of lake ice cover; Minimum percentage of lake ice cover where pixels with less than 15% cover are removed; Maximum percentage of lake ice cover where pixels with less than 15% cover are removed; Number of images; Minimum percentage of basin snow area cover; Mean percentage of basin snow area cover; Maximum percentage of basin snow area cover; Number of valid snow observations; Number of invalid snow observations; Area of a basin covered with snow","weight":1},{"access":[{"file_format":"NC","name":"psl.noaa.gov","url":"https://psl.noaa.gov/data/gridded/data.livneh.html"}],"access_details":null,"bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"Livneh B., Rosenberg, E.A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K.M., Maurer, E.P., and Lettenmaier, D.P., 2013, A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions, Journal of Climate, 26, pp 9384-9392, https://doi.org/10.1175/JCLI-D-12-00508.1","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"These meteorological data represent approximately 20,000 NCDC stations across CONUS, (plus a smaller number of Enviro-Can stations for the Canadian portion of the Columbia river) gridded to 211687 points at a 1/16 degree spatial resolution, running from 1 Jan 1915 - 31 Dec 2011","doi_url":"https://doi.org/10.1175/JCLI-D-12-00508.1","domain":["Climate"],"draft":false,"id":"c27bc7c2-5dbc-480f-96e6-887ebb406b6d","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://journals.ametsoc.org/view/journals/clim/26/23/jcli-d-12-00508.1.xml"}],"name":"A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions","permalink":"/catalog/datasets/c27bc7c2-5dbc-480f-96e6-887ebb406b6d/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"CONUS","spatial_resolution":"0.0625 degrees","temporal_coverage":"1915 - 2011","temporal_frequency":"daily; monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Precipitation","Temperature","Wind speed"],"vars":"Precipitation; Temperature; Wind speed","weight":1},{"access":[{"file_format":"SHP","name":"nrcs.app.box.com","url":"https://nrcs.app.box.com/v/soils/folder/20890497120"},{"file_format":"SHP","name":"websoilsurvey.nrcs.usda.gov","url":"https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx"}],"access_details":null,"bbox":{"east":-56,"north":72,"south":14,"west":-180},"citation":"change citation to Soil Survey Staff, Web Soil Survey: Natural Resources Conservation Service, United States Department of Agriculture, accessed [YYYY-MM-DD-] at http://websoilsurvey.nrcs.usda.gov/.","creator":[{"creator_email":"Soils-Webmaster@usda.gov","creator_name":"Soils Webmaster"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Digital General Soil Map of the United States or STATSGO2 is a broad-based inventory of soils and non-soil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped of 1:250,000 in the continental U.S., Hawaii, Puerto Rico, and the Virgin Islands and 1:1,000,000 in Alaska. The level of mapping is designed for broad planning and management uses covering state, regional, and multi-state areas. The U.S. General Soil Map is comprised of general soil association units and is maintained and distributed as a spatial and tabular dataset.\u003cbr\u003eThe U.S. General Soil Map was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset.","doi_url":null,"domain":["Soils"],"draft":false,"id":"c33ccf12-aede-4c2f-9a46-147cbf0e2ab8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.nrcs.usda.gov/resources/data-and-reports/ssurgo/stats2go-metadata"}],"name":"STATSGO2: General Soil Map of the United States","permalink":"/catalog/datasets/c33ccf12-aede-4c2f-9a46-147cbf0e2ab8/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NRCS","spatial_extent":"CONUS; AK; HI; PR; VI","spatial_resolution":"1:250,000; 1:1,000,000","temporal_coverage":"2016","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["STATSGO2 soil characteristics"],"vars":"STATSGO2 soil characteristics","weight":1},{"access":[{"file_format":"NC","name":"thredds.daac.ornl.gov","url":"https://thredds.daac.ornl.gov/thredds-daymet/dodsC/daymet-v3-agg/na.ncml.html"}],"access_details":null,"bbox":{"east":-63,"north":72,"south":15,"west":-180},"citation":"Thornton, P.E., Thornton, M.M., Mayer, B.W., Wei, Y., Devarakonda, R., Vose, R.S., and Cook, R.B., 2016, Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3: ORNL DAAC, accessed [YYYY-MM-DD] at https://doi.org/10.3334/ORNLDAAC/1328","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Superseded by Version 4.\u003cbr\u003eThis dataset provides Daymet Version 3 data as gridded estimates of daily weather parameters for North America, Hawaii, and Puerto Rico. Daymet variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset covers the period from January 1, 1980, to December 31 (or December 30 in leap years) of the most recent full calendar year for the Continental North America and Hawaii spatial regions. Data for Puerto Rico is available starting in 1950. Each subsequent year is processed individually at the close of a calendar year. Daymet variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Areas of Hawaii and Puerto Rico are available as files separate from the continental North America. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant netCDF file format.","doi_url":"https://doi.org/10.3334/ORNLDAAC/1328","domain":["Climate"],"draft":false,"id":"c3efd868-ba04-4e6f-8915-6484a7ab0c97","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://daac.ornl.gov/DAYMET/guides/Daymet_V3_CFMosaics.html"}],"name":"Daymet v3","permalink":"/catalog/datasets/c3efd868-ba04-4e6f-8915-6484a7ab0c97/","project_use_history":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"ORNL","spatial_extent":"North America; HI; PR","spatial_resolution":"1 kilometer","temporal_coverage":"1980 - 2019","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Temperature, minimum","Temperature, maximum","Precipitation","Snow water equivalent","Short-wave radiation","Vapor pressure","Length of day"],"vars":"Temperature, minimum; Temperature, maximum; Precipitation; Snow water equivalent; Short-wave radiation; Vapor pressure; Length of day","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/610ac6bfd34ef8d7056893a6"}],"access_details":null,"bbox":{"east":-66.6211,"north":49.4967,"south":24.5271,"west":-125.1563},"citation":"Cooper, A.R. and Infante, D.M., 2022, Dam Metrics Representing Stream Fragmentation and Flow Alteration for the Conterminous United States Linked to the NHDPLUSV2.1: U.S. Geological Survey data release, https://doi.org/10.5066/P94JQOFU","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This USGS data release includes a comma separated value (CSV) file that contains 19 reach-based dam metrics representing stream fragmentation and flow alteration for nearly 2.3 million stream reaches in the conterminous United States. Dam metrics fall into three main categories: count and density, distance-based, and cumulative reservoir storage (described below).  These data were developed using spatially verified large dam locations (n=49,140) primarily from the National Anthropogenic Barrier Dataset (NABD) 2012 that were spatially linked to the National Hydrography Dataset Plus version 2.1 (NHDPlusV2.1). These dam metrics have been summarized using the unique identifier field native to the NHDPlusV2.1 (COMID) which can be used to join this table to spatial layers and data tables of the NHDPlusV2.1.  Non-fluvial features representing lakes and reservoirs in the NHDPlusV2.1 are included (~300,000 features), however coastlines are excluded.\u003cbr\u003eLarge dam metrics fall into three main categories: count and density, distance-based, and cumulative reservoir storage","doi_url":null,"domain":["Hydrology","Infrastructure"],"draft":false,"id":"c67218a8-b77b-4d87-b49e-021f5e07b83f","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/610ac6bfd34ef8d7056893a6?f=__disk__df%2F47%2F71%2Fdf4771a9acf939b02da8c39d0331e8ce9004208a\u0026transform=1\u0026allowOpen=true#Spatial%20Data%20Organization%20Information"},{"name":"Documentation","url":"https://doi.org/10.1016/j.scitotenv.2017.02.067"}],"name":"Dam Metrics Representing Stream Fragmentation and Flow Alteration","permalink":"/catalog/datasets/c67218a8-b77b-4d87-b49e-021f5e07b83f/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2022","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Upstream mainstem dam count","Upstream mainstem dam density","Total upstream dam count","Upstream network dam density","Downstream mainstem dam count","Downstream mainstem dam density","Total mainstem dam count","Total mainstem dam density","Distance to upstream mainstem dam","Percentage of open upstream mainstem","Distance to downstream mainstem dam","Percentage of open downstream mainstem","Total mainstem distance between upstream and (or) downstream mainstem dams","Total percentage of open mainstem","Total upstream reservoir storage","Percentage of estimated annual stream discharge stored in upstream reservoirs"],"vars":"Upstream mainstem dam count; Upstream mainstem dam density; Total upstream dam count; Upstream network dam density; Downstream mainstem dam count; Downstream mainstem dam density; Total mainstem dam count; Total mainstem dam density; Distance to upstream mainstem dam; Percentage of open upstream mainstem; Distance to downstream mainstem dam; Percentage of open downstream mainstem; Total mainstem distance between upstream and (or) downstream mainstem dams; Total percentage of open mainstem; Total upstream reservoir storage; Percentage of estimated annual stream discharge stored in upstream reservoirs","weight":1},{"access":[{"file_format":"GDB; SHP; TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/588a4fe4e4b0ba3b075e9798"}],"access_details":null,"bbox":{"east":-105.078046,"north":43.811891,"south":35.147735,"west":-113.28064},"citation":"Buto, S.G., Spangler, L.E., Flint, A.L., and Flint, L.E., 2017, Catchment-flowline network and selected model inputs for an enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin: U.S. Geological Survey data release, https://doi.org/10.5066/F76T0JT4","creator":[{"creator_email":"mamiller@usgs.gov","creator_name":"Matt Miller"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This USGS data release consists of the synthetic stream network and associated catchments used to develop spatially referenced regressions on watershed attributes (SPARROW) model of dissolved-solids sources and transport in the Upper Colorado River Basin as well as geology and selected Basin Characterization Model (BCM) data used as input to the model.","doi_url":"https://doi.org/10.5066/F76T0JT4","domain":["Water Quality"],"draft":false,"id":"c6b657b4-f029-43af-a4e5-e114bcec1283","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/588a4fe4e4b0ba3b075e9798?f=__disk__04%2Ff2%2Ff5%2F04f2f5fd8f64eb9d43b81e846944dde429bcef64\u0026transform=1\u0026allowOpen=true"},{"name":"Documentation","url":"http://doi.org/10.3133/sir20175009"}],"name":"Catchment-flowline network and selected model inputs for an enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin","permalink":"/catalog/datasets/c6b657b4-f029-43af-a4e5-e114bcec1283/","project_use_history":[],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Upper Colorado River basin","spatial_resolution":"1:100,000; 1:500,000; 270 meter","temporal_coverage":"1985 - 2012; 2017","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Generalized geology","Mean total annual excess water","Mean total annual snowmelt","Mean total annual snowpack","Mean total annual potential evapotranspiration","Mean total annual precipitation","Mean total annual recharge","Mean total annual runoff","Mean total annual sublimation","Mean total annual snowfall","Mean total annual soil water storage","mean total annual actual evapotranspiration","Mean total annual climatic water deficit","SPARROW model catchments","SPARROW synthetic stream network"],"vars":"Generalized geology; Mean total annual excess water; Mean total annual snowmelt; Mean total annual snowpack; Mean total annual potential evapotranspiration; Mean total annual precipitation; Mean total annual recharge; Mean total annual runoff; Mean total annual sublimation; Mean total annual snowfall; Mean total annual soil water storage; mean total annual actual evapotranspiration; Mean total annual climatic water deficit; SPARROW model catchments; SPARROW synthetic stream network","weight":1},{"access":[{"file_format":"GPKG","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/61b897c9d34e9e224ac120de"}],"access_details":null,"bbox":{"east":-56,"north":51,"south":24,"west":-127},"citation":null,"creator":[{"creator_email":"bsperl@usgs.gov","creator_name":"Benjamin Sperl"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The National Water-Well Database (NWWDB) is a compilation of water-well records from state-managed databases that have been standardized to a common format for consistency across state and administrative boundaries. Water-well completion reports that are submitted to permitting state agencies by licensed drillers constitute a large source of hydrogeologic information, including the locations and distribution of water wells, construction materials and completion depths, lithologic logs, groundwater levels, and the results of pumping or aquifer tests. Standardization was performed based on a U.S. Geological Survey (USGS) profile of the Open Geospatial Consortium (OGC) GroundWaterML2 (GWML2) standard (https://docs.ogc.org/is/19-013/19-013.html). Two datasets are available for each state: 1) original water-well records, provided as received from host repositories at the state level except for standardization by the USGS per OGC GWML2, and 2) water-well records which have received additional processing by the USGS involving the application of quality-assurance rules, harmonization of lithologic descriptions around a common codeset, and estimates of transmissivity for the water-bearing intervals of water wells based on specific-capacity data. Statewide downloads are provided in the OGC GeoPackage (.gpkg) file format.\u003cbr\u003eThis dataset is being released on a state-by-state basis. Only a subset of the states may currently be available for download, but the dataset will continue to be updated with new states over time.","doi_url":null,"domain":["Hydrogeology"],"draft":false,"id":"c6ffbe42-268d-48b5-b05f-7cd3b1afc7cc","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[{"id":"3448d133-9bc3-4df1-9437-1d2768deadf8","rel_type":"IsSourceOf"},{"id":"98df9290-b0c6-4c33-a306-45a5aee3f116","rel_type":"IsSourceOf"}],"linked_usecases":[],"links":[],"name":"National Water-Well Database (NWWDB)","permalink":"/catalog/datasets/c6ffbe42-268d-48b5-b05f-7cd3b1afc7cc/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1800 - 2023","temporal_frequency":"NA","update_detail":"append","update_frequency":"irregular","update_type":"Dynamic","variables":["water-well completion reports","derived aquifer properties","harmonized lithologic logs"],"vars":"water-well completion reports; derived aquifer properties; harmonized lithologic logs","weight":1},{"access":[{"file_format":"TIF","name":"globalchange.bnu.edu.cn","url":"http://globalchange.bnu.edu.cn/research/dtb.jsp"},{"file_format":"TIF","name":"files.isric.org","url":"https://files.isric.org/soilgrids/former/2017-03-10/data/BDTICM_M_250m_ll.tif"}],"access_details":"The files.isric.org access point is a direct download of the data file.","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Shangguan, W., Hengl, T., de Jesus, J.M., Yuan, H. and Dai, Y., 2017, Mapping the global depth to bedrock for land surface modeling: Journal of Advances in Modeling Earth Systems, v. 9, no. 1, p. 65-88, https://doi.org/10.1002/2016MS000686","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This is a global depth to bedrock dataset (DTB) for use in Earth System Models and other applications as well. It provides three variables, the absolute DTB in cm, the censored DTB in cm within 0–200 cm (here values equal to 200 cm indicate “deep as or deeper than”), and the occurrence of R horizon (bedrock) within 0–200 cm expressed as 0–1 probability values. This product is developed under an automated soil mapping framework as part of the SoilGrids system (Hengl, T. et al., 2017). This dataset is based on Observations were extracted from a global compilation of soil profile data (approximately 1,30,000 locations) and borehole data (approximately 1.6 million locations). Additional pseudo-observations generated by expert knowledge were added to fill in large sampling gaps. The model training points were then overlaid on a stack of 155 covariates including DEM-based hydrological and morphological derivatives, lithologic units, MODIS surface reflectance bands and vegetation indices derived from the MODIS land products. Global spatial prediction models were developed using random forest and Gradient Boosting Tree algorithms. The final predictions were generated at the spatial resolution of 250 m as an ensemble prediction of the two independently fitted models. The dataset can be also aggregate to a lower resolution (1km and 10km).","doi_url":"https://doi.org/10.1002/2016MS000686","domain":["Soils"],"draft":false,"id":"c78e0e59-3e05-4776-b73f-a3283356d46c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"http://globalchange.bnu.edu.cn/download/doc/DTB/readme.pdf"},{"name":"Metadata","url":"https://files.isric.org/soilgrids/former/2017-03-10/data/BDTICM_M_250m_ll.tif.xml"}],"name":"A Global Depth to Bedrock Dataset for Earth System Modeling","permalink":"/catalog/datasets/c78e0e59-3e05-4776-b73f-a3283356d46c/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"ISRIC; Academic Institution(s)","spatial_extent":"Global","spatial_resolution":"250 meter","temporal_coverage":"2017","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Absolute depth to bedrock","Censored depth to bedrock","Occurrence of R horizon bedrock"],"vars":"Absolute depth to bedrock; Censored depth to bedrock; Occurrence of R horizon bedrock","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6113056cd34ed11898f70523"}],"bbox":{"east":-111.27777099501,"north":49.219105326292,"south":32.11245169843,"west":-127.71331786936},"citation":"Wise, D.R., Johnson, H.M., and Stonewall, A.J., 2021, Surface-water transfers and removals in the Pacific drainages of the United States: U.S. Geological Survey data release, https://doi.org/10.5066/P94XV0J3.","creator":[{"creator_email":"dawise@usgs.gov","creator_name":"Daniel Wise"}],"creator_project":[],"date_created":"4/14/2026","date_updated":"6/3/2026","description":"This data release contains a comprehensive, spatially referenced database of surface-water transfer and removal events in the Pacific drainages of the United States, which include the Columbia River basin, the Puget Sound basin, the coastal drainages of Washington, Oregon, and California, and the Central Valley of California. The database also includes mean annual estimates of the water diverted at each event. These estimates were mostly based on information available from the U.S. Geological Survey and the U.S. Bureau of Reclamation but also, for some events, from state and local water management agencies. While these estimates generally represent conditions between 1974 and 2014, the database includes information that can be used to obtain more recent estimates for most of the events. The database includes 77 transfers for power generation and other uses (totaling 27,383,000 acre-ft per year), 262 removals for irrigation and removals associated with in-stream loss to groundwater (totaling 31,454,000 acre-ft per year), and 129 removals for municipal water supply (totaling 9,986,000 acre-ft per year).","doi_url":"https://doi.org/10.5066/P94XV0J3","domain":["Water Use"],"draft":false,"id":"c7cafd67-735a-4640-8ae9-58ec93cc7936","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"ae9546b9-1eda-407a-9129-8a62b43ac056","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6113056cd34ed11898f70523?f=__disk__ec%2F60%2Fd7%2Fec60d74248c9585b202684817312f0f88b0eb5ce\u0026allowOpen=true"}],"name":"Surface-water transfers and removals in the Pacific drainages of the United States","permalink":"/catalog/datasets/c7cafd67-735a-4640-8ae9-58ec93cc7936/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CA; ID; MT; OR; WA","spatial_resolution":"NA","temporal_coverage":"1949 - 2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Obs_num","Transfer_Description","Transfer_Quantity","Transfer_Quantity_Method","Transfer_Type","Removal_Region","Removal_Comid","Removal_Stream_Name","Q_Upstream","Q_Upstream_Method","Removal_HUC12","Delivered_Region","Delivered_Comid","Delivered_Stream_Name","Delivered_HUC12","Transfer_Notes","Obs_num","Removal_Description","Removal_Quantity","Removal_Quantity_Method","Removal_Type","Removal_Region","Removal_Comid","Removal_Stream_Name","Q_Upstream","Q_Upstream_Method","Removal_HUC12","Removal_Notes","Obs_num","System_Owner","System_Facility","Removal_Quantity","Removal_Quantity_Method","Removal_Region","Removal_Comid","Removal_Stream_Name","Q_Upstream","Q_Upstream_Method","Removal_HUC12","Removal_Notes"],"vars":"Obs_num; Transfer_Description; Transfer_Quantity; Transfer_Quantity_Method; Transfer_Type; Removal_Region; Removal_Comid; Removal_Stream_Name; Q_Upstream; Q_Upstream_Method; Removal_HUC12; Delivered_Region; Delivered_Comid; Delivered_Stream_Name; Delivered_HUC12; Transfer_Notes; Obs_num; Removal_Description; Removal_Quantity; Removal_Quantity_Method; Removal_Type; Removal_Region; Removal_Comid; Removal_Stream_Name; Q_Upstream; Q_Upstream_Method; Removal_HUC12; Removal_Notes; Obs_num; System_Owner; System_Facility; Removal_Quantity; Removal_Quantity_Method; Removal_Region; Removal_Comid; Removal_Stream_Name; Q_Upstream; Q_Upstream_Method; Removal_HUC12; Removal_Notes","weight":1},{"access":[{"file_format":"NC; SHP","name":"zenodo.org","url":"https://zenodo.org/record/5643392"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Elizabeth H. Altenau, Tamlin M. Pavelsky, Michael T. Durand, Xiao Yang, Renato P. d. M. Frasson, \u0026 Liam Bendezu. (2021). SWOT River Database (SWORD) (Version v2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5643392","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The SWOt River Database (SWORD) combines multiple global river- and satellite-related datasets to define the nodes and reaches that will constitute SWOT river vector data products. SWORD provides high-resolution river nodes (200 m) and reaches (~10 km) in shapefile and netCDF formats with attached hydrologic variables (WSE, width, slope, etc.) as well as a consistent topological system for global rivers 30 m wide and greater.","doi_url":"https://doi.org/10.5281/zenodo.5643392","domain":["Hydrology","Stream Characteristics"],"draft":false,"id":"c82bb2b9-fa2a-4ab2-a2cc-2fc7b5aaf969","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.1029/2021WR030054"}],"name":"SWOT River Database (SWORD), v2","permalink":"/catalog/datasets/c82bb2b9-fa2a-4ab2-a2cc-2fc7b5aaf969/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA; Academic institution(s)","spatial_extent":"Global","spatial_resolution":"30 meter","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["River width","Elevation","Flow accumulation","Number of channels","Lake/reservoir flags","SWOT orbits","Number of SWOT passes","Dam locations","Reach definition","Discharge variables","30-meter centerline locations"],"vars":"River width; Elevation; Flow accumulation; Number of channels; Lake/reservoir flags; SWOT orbits; Number of SWOT passes; Dam locations; Reach definition; Discharge variables; 30-meter centerline locations","weight":1},{"access":[{"file_format":"NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/55fc3f98e4b05d6c4e5029a1"}],"access_details":null,"bbox":{"east":-65.348336,"north":51.651713,"south":22.946853,"west":-127.904201},"citation":"Bock, A.R., Hay, L.E., Markstrom, S.L., and Atkinson, R.D., 2018, Monthly Water Balance Model Futures (ver. 3.0, September 2018): U.S. Geological Survey data release, https://doi.org/10.5066/F7VD6WJQ","creator":[{"creator_email":"abock@usgs.gov","creator_name":"Andy Bock"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"A monthly water balance model (MWBM) was driven with precipitation and temperature using a station-based dataset for current conditions (1949 to 2010) and selected statistically-downscaled general circulation models (GCMs) for current and future conditions (1950 to 2099) across the conterminous United States (CONUS) using hydrologic response units from the Geospatial Fabric for National Hydrologic Modeling (Viger and Bock, 2014). Six MWBM output variables (actual evapotranspiration (AET), potential evapotranspiration (PET), runoff (RO), streamflow (STRM), soil moisture storage (SOIL), and snow water equivalent (SWE)) and the two MWBM input variables (atmospheric temperature (TAVE) and precipitation (PPT)) were summarized for hydrologic response units and aggregated at points of interest on a stream network. Results were organized into the Monthly Water Balance Model Futures database, and accessed through the Monthly Water Balance Model Futures Portal.","doi_url":"https://doi.org/10.5066/F7VD6WJQ","domain":["Hydrology"],"draft":false,"id":"c8a2a373-64f3-4f39-a7b3-4872ee1b3748","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/55fc3f98e4b05d6c4e5029a1?f=__disk__bb%2Fe0%2F4c%2Fbbe04ce9e3c9d6b284b61479ced024a1d5610618\u0026transform=1\u0026allowOpen=true"},{"name":"Documentation","url":"https://doi.org/10.3133/ofr20161212"}],"name":"Monthly Water Balance Model Futures","permalink":"/catalog/datasets/c8a2a373-64f3-4f39-a7b3-4872ee1b3748/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1949 - 2099","temporal_frequency":"monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Actual evapotranspiration (AET)","Potential evapotranspiration (PET)","Runoff (RO)","Streamflow (STRM)","Soil moisture storage (SOIL)","Snow water equivalent (SWE))"],"vars":"Actual evapotranspiration (AET); Potential evapotranspiration (PET); Runoff (RO); Streamflow (STRM); Soil moisture storage (SOIL); Snow water equivalent (SWE))","weight":1},{"access":[{"file_format":"TXT; CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5fe39194d34ea5387deb4933"}],"access_details":null,"bbox":{"east":-70.0927734375,"north":49.0954521625348,"south":29.5352295629485,"west":-111.2255859375},"citation":"Groten, J.T., Wood, M.S., and Leroy, J.Z., 2021, The Vigil Network: long-term, broad spectrum data collected to observe landscape change in drainage basins (ver 2.0, August 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P9V0R02R.","creator":[],"creator_project":[{"id":"DJ60TRJ","name":"NHGF: National Hydrologic Geospatial Fabric"}],"date_created":"6/7/2024","date_updated":"6/3/2026","description":"Long-term monitoring data of geomorphic, hydrological, and biological characteristics of landscapes. This information provides an effective means of relating observed change to possible causes of the change. Identification of changes in basin characteristics, especially in arid areas where the response to altered climate or land use is generally rapid and readily apparent, might provide the initial direct indications that factors such as global warming and cultural impacts have affected the environment. The Vigil Network provides an opportunity for earth and life scientists to participate in a systematic monitoring effort to detect landscape changes over time, and to relate such changes to possible causes. This data release includes 70 sites and basins used to monitor landscape features. This data release includes information for Vigil Network sites monitored in the United States. The data and information in this data release are historical and were obtained from original documents.\u003cbr\u003eThis data release has been updated to include a table of summary characteristics.","doi_url":"https://doi.org/10.5066/P9V0R02R","domain":["Hydrology","Stream Characteristics"],"draft":false,"id":"c925daa8-40de-4618-bcb3-0f2b1c70ad33","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5fe39194d34ea5387deb4933?f=__disk__f1%2F3e%2Ffe%2Ff13efe29b0a8e9135c2756d2e9f42e94b8dd8b81\u0026allowOpen=true"}],"name":"The Vigil Network: long-term, broad spectrum data collected to observe landscape change in drainage basins (ver 2.0, August 2021)","permalink":"/catalog/datasets/c925daa8-40de-4618-bcb3-0f2b1c70ad33/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CO; GA; MD; MS; MT; NM; OH; PA; WI; WY","spatial_resolution":"unknown","temporal_coverage":"1937 - 1989","temporal_frequency":"irregular","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["site_name","state","country","sbid","lat","lon","location","pi","site_est_date","submis_date","purposes","precip_mm","elev_m","da_sqkm","geology","hydrology","vegetation","bench_marks","photography","usgs_maps","hillslopes_data","stream_channels_data","vegetation_data"],"vars":"site_name; state; country; sbid; lat; lon; location; pi; site_est_date; submis_date; purposes; precip_mm; elev_m; da_sqkm; geology; hydrology; vegetation; bench_marks; photography; usgs_maps; hillslopes_data; stream_channels_data; vegetation_data","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6488734cd34ef77fcafe347a"}],"access_details":null,"bbox":{"east":-66.5332,"north":49.2104,"south":24.687,"west":-125.1563},"citation":"Haynes, J.V., Read, A.L, Chan, A.Y., Martin, D.J., Regan, R.S., Henson, W.R., Niswonger, R.G., and Stewart, J.S., 2023, Monthly crop irrigation withdrawals and efficiencies by HUC12 watershed for years 2000-2020 within the conterminous United States (ver. 2.0, September 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P9LGISUM","creator":[],"creator_project":[{"id":"DJ50UY1","name":"Water Use Model Development"}],"date_created":"5/10/2024","date_updated":"6/3/2026","description":"The USGS has published United States water-use data every five years since 1950. To increase the temporal and spatial availability of water use estimates using nationally consistent methods, the USGS is developing national water-use models for each major water-use category. This data release publishes crop irrigation withdrawals for the conterminous United States (CONUS) that are calculated using modeled irrigation consumptive use (Martin and others, 2024), irrigation efficiencies, and source-water proportions (Dieter and others, 2018). Crop irrigation withdrawals and irrigation consumptive use refer to water removed and consumed, respectively, from a groundwater or surface-water source to produce agricultural crops. Monthly withdrawals provided include groundwater, surface water, and the combined total withdrawal for areas contained in the twelve-digit watershed boundary (HUC12) dataset during the reanalysis period, 2000-2020. HUC12 annual 2000-2020 irrigation efficiencies included in this data release combine efficiencies from irrigation system types (accounting for water lost during application to crops) and conveyances (accounting for water lost during transmission through canals and pipes). Irrigated crops were mapped using the Landsat-based Irrigation Dataset (LANID; Xie and Lark, 2021; Martin and others, 2024) and the Cropland Data Layer (USDA NASS, 2022) that were linked to irrigation system types (USDA NASS, 2014) to estimate irrigation system efficiencies for each HUC12 in the CONUS (Howell, 2003 and FAO, 1989). Conveyance loss volumes (USDA NASS, 2020) were used to estimate and map surface-water conveyance efficiencies. Total efficiencies were calculated for HUC12 units by combining irrigation system and conveyance efficiencies. Irrigation withdrawals and efficiencies were produced using published data sources to provide these estimates in a timely manner. On-going work to develop dynamic maps of irrigation system type and other datasets for the CONUS will be used in the future to refine the estimates provided here. Estimation of irrigation withdrawals using irrigation consumptive use and efficiencies neglects some components of water use for crops, including water used for frost protection, salt leaching, harvesting, and other non-consumptive-use based treatments. For this reason, irrigation withdrawals provided here may under-estimate total withdrawals where non-consumptive treatments are significant.","doi_url":"https://doi.org/10.5066/P9LGISUM","domain":["Water Use"],"draft":false,"id":"c9651560-6bcd-4eb2-a53b-3228adcc41f4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6488734cd34ef77fcafe347a?f=__disk__ba%2F8a%2Fbc%2Fba8abc1351dfaaf42ff777817d2732d1835b15eb\u0026allowOpen=true"}],"name":"Monthly crop irrigation withdrawals and efficiencies by HUC12 watershed for years 2000-2020 within the conterminous United States (ver. 2.0, September 2024)","permalink":"/catalog/datasets/c9651560-6bcd-4eb2-a53b-3228adcc41f4/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2000 - 2020","temporal_frequency":"monthly; annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Year","Month","HUC12 watershed number","CDL_value","CDL_class_name","FRIS2013_crop","State","Crop","Gravfrac","Sprkfrac","Lowffrac","GravEff","SprkEff","LowEff"],"vars":"Year; Month; HUC12 watershed number; CDL_value; CDL_class_name; FRIS2013_crop; State; Crop; Gravfrac; Sprkfrac; Lowffrac; GravEff; SprkEff; LowEff","weight":1},{"access":[{"file_format":"HDF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-mod16a2-006"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Running, S., Mu, Q., Zhao, M., 2017, MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006, distributed by NASA EOSDIS Land Processes DAAC, accessed [YYYY-MM-DD], https://doi.org/10.5067/MODIS/MOD16A2.006","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The MOD16A2 Version 6 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year.","doi_url":"https://doi.org/10.5067/MODIS/MOD16A2.006","domain":["Hydrology","Climate"],"draft":false,"id":"cb752609-9d56-41a9-8f00-3cd85b723140","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/documents/494/MOD16_User_Guide_V6.pdf"}],"name":"MOD16A2 v006: MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid","permalink":"/catalog/datasets/cb752609-9d56-41a9-8f00-3cd85b723140/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"500 meter","temporal_coverage":"2001 - Present","temporal_frequency":"8 days","update_detail":"append","update_frequency":"8 days","update_type":"Dynamic","variables":["Total evapotranspiration (ET)","Average latent heat flux (LE)","Total potential evapotranspiration (PET)","Average potential latent heat flux (PLE)","Evapotranspiration quality control flags (ET_QC)"],"vars":"Total evapotranspiration (ET); Average latent heat flux (LE); Total potential evapotranspiration (PET); Average potential latent heat flux (PLE); Evapotranspiration quality control flags (ET_QC)","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/605c987fd34ec5fa65eb6a74"}],"access_details":null,"bbox":{"east":-74.318181,"north":42.470214,"south":38.661852,"west":-76.910652},"citation":"Dornbierer, J.M., Wika, S., Robison, C.J., Rouze, G.S., and Sohl, T.L., 2021, Long-term database of historical, current, and future land cover for the Delaware River Basin (1680 through 2100): U.S. Geological Survey data release, https://doi.org/10.5066/P93J4Z2W.","creator":[{"creator_email":"sohl@usgs.gov","creator_name":"Terry Sohl"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The USGS’s FORE-SCE model was used to produce a long-term landscape dataset for the Delaware River Basin (DRB). Using historical landscape reconstruction and scenario-based future projections, the data provided land-use and land-cover (LULC) data for the DRB from year 1680 through 2100, with future projections from 2020-2100 modeled for 7 different socioeconomic-based scenarios, and 3 climate realizations for each socioeconomic scenario (21 scenario combinations in total). The projections are characterized by 1) high spatial resolution (30-meter cells), 2) high thematic resolution (20 land use and land cover classes), 3) broad spatial extent (covering the entirety of the Delaware River basin, corresponding to USGS HUC codes 020401 and 020402), 4) use of real land ownership boundaries to ensure realistic representation of landscape patterns, and 5) representation of both anthropogenic land use and natural vegetation change that respond to projected climate change. Data are provided in 10-year time steps from 1680 through 2100 (43 individual dates). Historical landscape data is provided in one downloadable zip file, containing 34 individual land cover datasets for 10-year intervals from 1680 through 2010. “Current” (2020) and “future” (2030 through 2100) data are provided at 10-year time steps in files corresponding to the 21 different scenario combinations. The following provides a brief summary of the 7 major land-use scenarios. 1) Business-as-usual - Based on an extrapolation of recent land-cover trends as derived from remote-sensing data. Overall trends were provided by 2001 to 2016 change in the National Land Cover Database, while change in crop types were extrapolated from 2008 to 2018 change in the Cropland Data Layer. 2) Billion Ton Update (BTU) scenario ($40 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the BTU. The $40 scenario represents likely agricultural conditions under an assumed farmgate price of $40 per dry ton of biomass (for the production of biofuel). All three BTU scenarios include the representation of a “perennial grass” class (class #20) that represents grass crops such as miscanthus, switchgrass, or prairie grasses grown for production of cellulosic biofuel. 3) BTU scenario ($60 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the BTU. The $60 scenario represents likely agricultural conditions under an assumed farmgate price of $60 per dry ton of biomass (for the production of biofuel). 4) BTU scenario ($80 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the BTU. The $80 scenario represents likely agricultural conditions under an assumed farmgate price of $80 per dry ton of biomass (for the production of biofuel). 5) Global Change Analysis Model (GCAM) Reference scenario - Based on global-scale scenarios from the GCAM model, the \"reference\" scenario provides a likely landscape under a world without specific carbon or climate mitigation efforts. As such, it's another form of a \"business-as-usual\" scenario. 6) GCAM 2.6 scenario - Based on global-scale scenarios from the GCAM model, the GCAM 2.6 model represents a very aggressive climate mitigation scenario, where carbon payments and other mitigation efforts result in a net radiative forcing of only ~2.6 W/m2 by 2100. 7) GCAM 4.5 scenario - Based on global-scale scenarios from the GCAM model, the GCAM 4.5 model represents a mid-level climate mitigation scenario, where carbon payments and other mitigation efforts result in a net radiative forcing of ~4.5 W/m2 by 2100. For each of the 7 land-use scenarios, three alternative climate / vegetation scenarios were modeled, resulting in 21 unique scenario combinations. The alternative vegetation scenarios represent the potential changes in quantity and distribution of the major vegetation classes that were modeled (grassland, shrubland, deciduous forest, mixed forest, and evergreen forest), as a response to potential future climate conditions. The three alternative vegetation scenarios correspond to climate conditions consistent with 1) The Intergovernmental Panel on Climate Change (IPCC's) Representative Concentration Pathway (RCP) 8.5 scenario (a scenario of high climate change), 2) the RCP 4.5 scenario (a mid-level climate change scenario), and 3) a mid-point climate that averages RCP4.5 and RCP8.5 conditions Data are provided here as compressed ZIP files for 1) the historical landscape reconstruction time frame (1680 through 2010), and 2) for each of the 21 future scenario combinations, including the starting 2020 year and extending through 2100 (thus 22 downloadable ZIP files). The “attributes” section of the metadata provides a key for identifying file names associated with each of the scenario combinations and historical period.","doi_url":"https://doi.org/10.5066/P93J4Z2W","domain":["Land Cover"],"draft":false,"id":"cbd9f938-adc4-4f25-acd2-8d54e5ca3b42","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/605c987fd34ec5fa65eb6a74?f=__disk__c0%2Fee%2F36%2Fc0ee3640f7bce1fb08be36521d9ebbfd0ccfe643\u0026allowOpen=true"}],"name":"Long-term database of historical, current, and future land cover for the Delaware River Basin (1680 through 2100)","permalink":"/catalog/datasets/cbd9f938-adc4-4f25-acd2-8d54e5ca3b42/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Delaware River basin","spatial_resolution":"30 meter","temporal_coverage":"1680 - 2100","temporal_frequency":"10 years","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Land-cover Classes","Map coordinates","File Name and Description of Contents"],"vars":"Land-cover Classes; Map coordinates; File Name and Description of Contents","weight":1},{"access":[{"file_format":"SHP","name":"podaac.jpl.nasa.gov","url":"https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_node_D"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Surface Water Ocean Topography (SWOT). 2025. SWOT Level 2 River Single-Pass Vector Node Data Product, Version D. Ver. D. PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at https://doi.org/10.5067/SWOT-RIVERSP-D","creator":[],"creator_project":[],"date_created":"5/2/2025","date_updated":"6/3/2026","description":"The SWOT Level 2 River Single-Pass Vector Node Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, slope, width, and discharge derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025.Water surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.","doi_url":"https://doi.org/10.5067/SWOT-RIVERSP-D","domain":["Hydrology"],"draft":false,"id":"cbf34ed1-10ef-462c-bc9f-1fe62475a522","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_node_D"}],"name":"SWOT Level 2 River Single-Pass Vector Node Data Product, Version D","permalink":"/catalog/datasets/cbf34ed1-10ef-462c-bc9f-1fe62475a522/","project_use_history":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"project_using":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"ref_fabric":false,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"varies","temporal_coverage":"2023 - Present","temporal_frequency":"3 weeks","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["reach ID with which each node is associated","node ID of the node in the prior river database","time (UTC)","time (TAI)","latitude of centroid of water-detected pixels","longitude of centroid of water-detected pixels","uncertainty in the node longitude","river names","water surface elevation with respect to the geoid","total uncertainty in the water surface elevation","random-only uncertainty in the water surface elevation","node width","total uncertainty in the node width","total water surface area including dark water","uncertainty in the total water surface area","surface area of detected water pixels","uncertainty in the surface area of detected water","area used to compute water surface elevation","metric of layover effect","distance between observed and prior river database node location","distance to the satellite ground track","river flow direction relative tot he satellite ground track","summary quality indicator for the node","bitwise quality indicator for the node","fractional area of dark water","climatological ice cover flag","dynamical ice cover flag","number of pixels that have a valid WSE","quality of the cross-over calibration","sigma0","uncertainty in sigma0","polarization of sigma0","geoid height","solid Earth tide height","geocentric load tide height (FES)","geocentric load tide height (GOT)","geocentric pole tide height","dry troposphere vertical correction","wet troposphere vertical correction","ionosphere vertical correction","WSE correction from KaRIn crossovers","node water surface elevation","node water surface elevation variability","node width","node width variability","distance from the node to the outlet","length of node","dam ID from GRanD database","maximum number of channels detected in node","mode of the number of channels at the node"],"vars":"reach ID with which each node is associated; node ID of the node in the prior river database; time (UTC); time (TAI); latitude of centroid of water-detected pixels; longitude of centroid of water-detected pixels; uncertainty in the node longitude; river names; water surface elevation with respect to the geoid; total uncertainty in the water surface elevation; random-only uncertainty in the water surface elevation; node width; total uncertainty in the node width; total water surface area including dark water; uncertainty in the total water surface area; surface area of detected water pixels; uncertainty in the surface area of detected water; area used to compute water surface elevation; metric of layover effect; distance between observed and prior river database node location; distance to the satellite ground track; river flow direction relative tot he satellite ground track; summary quality indicator for the node; bitwise quality indicator for the node; fractional area of dark water; climatological ice cover flag; dynamical ice cover flag; number of pixels that have a valid WSE; quality of the cross-over calibration; sigma0; uncertainty in sigma0; polarization of sigma0; geoid height; solid Earth tide height; geocentric load tide height (FES); geocentric load tide height (GOT); geocentric pole tide height; dry troposphere vertical correction; wet troposphere vertical correction; ionosphere vertical correction; WSE correction from KaRIn crossovers; node water surface elevation; node water surface elevation variability; node width; node width variability; distance from the node to the outlet; length of node; dam ID from GRanD database; maximum number of channels detected in node; mode of the number of channels at the node","weight":1},{"access":[{"file_format":"HDF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-mcd12q1-006"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Friedl, M., Sulla-Menashe, D., 2019, MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006. distributed by NASA EOSDIS Land Processes DAAC, accessed [YYYY-MM-DD], https://doi.org/10.5067/MODIS/MCD12Q1.006","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The MCD12Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MCD12Q1 Version 6.1 data product.\u003cbr\u003eThe Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6 data product provides global land cover types at yearly intervals (2001-2019), derived from six different classification schemes listed in the User Guide. The MCD12Q1 Version 6 data product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes.","doi_url":"https://doi.org/10.5067/MODIS/MCD12Q1.006","domain":["Land Cover"],"draft":false,"id":"ccff05fe-1e13-4fa0-9559-14ced96a9ade","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf"}],"name":"MCD12Q1 v006: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid","permalink":"/catalog/datasets/ccff05fe-1e13-4fa0-9559-14ced96a9ade/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"500 meter","temporal_coverage":"2001 - 2020","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Evergreen Needleleaf Forests","Evergreen Broadleaf Forests","Deciduous Needleleaf Forests","Deciduous Broadleaf Forests","Mixed Forests","Closed Shrublands","Open Shrublands","Woody Savannas","Savannas","Grasslands","Permanent Wetlands","Croplands","Urban and Built-up Lands","Cropland/Natural Vegetation","Permanent Snow and Ice","Barren","Water Bodies","Unclassified"],"vars":"Evergreen Needleleaf Forests; Evergreen Broadleaf Forests; Deciduous Needleleaf Forests; Deciduous Broadleaf Forests; Mixed Forests; Closed Shrublands; Open Shrublands; Woody Savannas; Savannas; Grasslands; Permanent Wetlands; Croplands; Urban and Built-up Lands; Cropland/Natural Vegetation; Permanent Snow and Ice; Barren; Water Bodies; Unclassified","weight":1},{"access":[{"file_format":"TIF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-hlsl30-2.0"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., and Justice, C., 2021, HLS Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0: distributed by NASA EOSDIS Land Processes DAAC, accessed [YYYY-MM-DD] at https://doi.org/10.5067/HLS/HLSL30.002","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual constellation of satellite sensors. The Operational Land Imager (OLI) is housed aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral Instrument (MSI) is mounted aboard Europe’s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.\u003cbr\u003eThe HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8/9 OLI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system, and thus are “stackable” for time series analysis.\u003cbr\u003eThe HLSL30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. There are 11 bands included in the HLSL30 product along with one quality assessment (QA) band and four angle bands. See the User Guide for a more detailed description of the individual bands provided in the HLSL30 product.","doi_url":"https://doi.org/10.5067/HLS/HLSL30.002","domain":["Soils"],"draft":false,"id":"cd4013e0-a2fa-414b-915a-f94f3a176387","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf"}],"name":"Harmonized Landsat/Sentinel (HLS) L30 v002","permalink":"/catalog/datasets/cd4013e0-a2fa-414b-915a-f94f3a176387/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA; USGS","spatial_extent":"Global","spatial_resolution":"30 meter","temporal_coverage":"2013 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Coastal Aerosol","Blue","Green","Red","NIR","SWIR1","SWIR2","Cirrus","TIRS1","TIRS2","Quality Bits","Sun Zenith Angle","Sun Azimuth Angle","View Zenith Angle","View Azimuth Angle"],"vars":"Coastal Aerosol; Blue; Green; Red; NIR; SWIR1; SWIR2; Cirrus; TIRS1; TIRS2; Quality Bits; Sun Zenith Angle; Sun Azimuth Angle; View Zenith Angle; View Azimuth Angle","weight":1},{"access":[{"file_format":"NC","name":"disc.gsfc.nasa.gov","url":"https://disc.gsfc.nasa.gov/datasets/NLDAS_MOS0125_H_2.0/summary"}],"access_details":null,"bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., Mocko, D., 2012, Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J. Geophys. Res., 117, D03109, https://doi.org/10.1029/2011JD016048.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Mosaic land surface model output.\u003cbr\u003eThis data set contains thirty-eight fields simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB format).\u003cbr\u003e Mosaic was developed by Koster and Suarez (1994, 1996) to account for subgrid vegetation variability with a tile approach. Each vegetation tile carries its own energy and water balance and soil moisture and temperature. Each tile has three soil layers, with the first two in the root zone. In NLDAS, Mosaic is configured to support a maximum of 10 tiles per grid cell with a 5% cutoff that ignores vegetation classes covering less than 5% of the cell. Additionally in NLDAS, all tiles of Mosaic in a grid cell have a predominant soil type and three soil layers with fixed thickness values of 10, 30, and 160 cm (hence constant rooting depth of 40 cm and constant total column depth of 200 cm).\u003cbr\u003eDetails about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia and others (2012).","doi_url":"https://doi.org/10.1029/2011JD016048","domain":["Climate","Snow","Land Cover","Hydrology"],"draft":false,"id":"cdfadb49-a16e-44e9-a74a-bae3f5099745","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://hydro1.gesdisc.eosdis.nasa.gov/data/NLDAS/NLDAS2_README.pdf"}],"name":"NLDAS Mosaic Land Surface Model L4 Hourly 0.125 x 0.125 degree V2.0 (NLDAS_MOS0125_H)","permalink":"/catalog/datasets/cdfadb49-a16e-44e9-a74a-bae3f5099745/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"CONUS","spatial_resolution":"0.125 degrees","temporal_coverage":"1979 - Present","temporal_frequency":"hourly","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Total evapotranspiration","Water equivalent of accumulated snow depth","Snow depth","Albedo","Soil temperature","Soil moisture content","Vegetation","Snow melt","Net shortwave radiation flux (surface)","Net longwave radiation flux (surface)","Latent heat flux","Sensible heat flux","Potential latent heat flux (potential evaporation)","Average surface skin temperature","Liquid soil moisture content (non-frozen)","Ground heat flux","Frozen precipitation (for example, snowfall)","Liquid precipitation (rainfall)","Aerodynamic conductance","Canopy conductance","Leaf area index","Sublimation (evaporation from snow)","Direct evaporation from bare soil","Canopy water evaporation","Minimal stomatal resistance","Downward shortwave radiation flux","Downward longwave radiation flux","Moisture availability","Transpiration","Plant canopy surface water","Snow phase-change heat flux","Subsurface runoff (baseflow)","Surface runoff (non-infiltrating)","Snow cover","Solar parameter in canopy conductance","Temperature parameter in canopy conductance","Humidity parameter in canopy conductance","Soil moisture parameter in canopy conductance","Root zone soil moisture content","Relative soil moisture availability control factor"],"vars":"Total evapotranspiration; Water equivalent of accumulated snow depth; Snow depth; Albedo; Soil temperature; Soil moisture content; Vegetation; Snow melt; Net shortwave radiation flux (surface); Net longwave radiation flux (surface); Latent heat flux; Sensible heat flux; Potential latent heat flux (potential evaporation); Average surface skin temperature; Liquid soil moisture content (non-frozen); Ground heat flux; Frozen precipitation (for example, snowfall); Liquid precipitation (rainfall); Aerodynamic conductance; Canopy conductance; Leaf area index; Sublimation (evaporation from snow); Direct evaporation from bare soil; Canopy water evaporation; Minimal stomatal resistance; Downward shortwave radiation flux; Downward longwave radiation flux; Moisture availability; Transpiration; Plant canopy surface water; Snow phase-change heat flux; Subsurface runoff (baseflow); Surface runoff (non-infiltrating); Snow cover; Solar parameter in canopy conductance; Temperature parameter in canopy conductance; Humidity parameter in canopy conductance; Soil moisture parameter in canopy conductance; Root zone soil moisture content; Relative soil moisture availability control factor","weight":1},{"access":[{"file_format":"GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/537a6a01e4b0efa8af081544"}],"access_details":null,"bbox":{"east":-63.21,"north":50.741871378,"south":17.1,"west":-161.31},"citation":"Viger, R.J., 2014, Geospatial Fabric Attribute Tables for PRMS Soils Parameters based on SSURGO (Preliminary), U.S. Geological Survey; https://doi.org/10.5066/F7RX9937","creator":[{"creator_email":"rviger@usgs.gov","creator_name":"Roland Viger"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset contains a set of attributes describing the \"nhru\" GIS features (Hydrologic Response Units)in the Geospatial Fabric Features dataset(http://dx.doi.org/doi:10.5066/F7542KMD) that have been developed in support of the USGS PRMS watershed model. These tables are organized according to Geospatial Fabric Region; see the thumbnail of the Geospatial Fabric Features Regions (https://www.sciencebase.gov/catalog/item/535edb4ae4b08e65d60fc837). Each table contains a key field, \"hru_id\", that can be used to relate to the nhru feature class in the Geospatial Fabric Feature dataset for the corresponding Region. The methodologies used to derive the individual attributes can be located in the Appendix of the GIS Weasel Users Manual by the name of the attribute, which is the same as the name of the corresponding PRMS parameter, in (Viger and Leavesley, 2007). The metadata for each table within the current container identifies any ancillary datasets used to produce the table fields. For nhru instances that are partially or entirely beyond the borders of the United States, supporting GIS data was generally lacking. Where the value for a field could not be determined, values derived at the border were spatially extended to these areas to support derivation of a value. Users may want to review and modify the field values for these HRUs. Viger, R.J., and Leavesley, G.H., 2007, The GIS Weasel user's manual: U.S. Geological Survey Techniques and Methods, book 6, chap. B4, 201 p.","doi_url":"https://doi.org/10.5066/F7RX9937","domain":["Hydrology"],"draft":false,"id":"cf4f8e4a-bdc6-4b5e-8eaa-2a60e5c60e65","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/537a6a01e4b0efa8af081544?f=__disk__ea%2Fe1%2F12%2Feae1126f59a5933ad57ec6ff8d297709a3842c13\u0026allowOpen=true"}],"name":"SSURGO data for the Geospatial Fabric v1.0","permalink":"/catalog/datasets/cf4f8e4a-bdc6-4b5e-8eaa-2a60e5c60e65/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; HI; PR","spatial_resolution":"1:100,000","temporal_coverage":"2014","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["hru_id","soil_moist_max","soil_rechr_max","soil_type"],"vars":"hru_id; soil_moist_max; soil_rechr_max; soil_type","weight":1},{"access":[{"file_format":"NC","name":"disc.gsfc.nasa.gov","url":"https://disc.gsfc.nasa.gov/datasets/NLDAS_NOAH0125_H_2.0/summary"}],"access_details":null,"bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"NLDAS project, 2021, NLDAS Noah Land Surface Model L4 Hourly 0.125 x 0.125 degree V2.0, Edited by David M. Mocko, NASA/GSFC/HSL, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date] at https://doi.org/10.5067/T4OW83T8EXDO","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"NLDAS Noah land surface model output. \u003cbr\u003eThis data set contains fifty-three fields simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is hourly. The file format is netCDF (converted from the GRIB format).\u003cbr\u003eThe Noah model was developed as the land component of the NOAA NCEP mesoscale Eta model (Betts and others. (1997); Chen and others (1997); Ek and others (2003)). As used in NLDAS-2, recent modifications were made to Noah's cold-season (Livneh and others (2010)) and warm-season (Wei and others (2012)) parameterizations. Noah serves as the land component in the evolving Weather Research and Forecasting (WRF) regional atmospheric model, the NOAA NCEP coupled Climate Forecast System (CFS), and the Global Forecast System (GFS). The model simulates the soil freeze-thaw process and its impact on soil heating/cooling and transpiration, following Koren and others (1999). The model has four soil layers with spatially invariant thicknesses of 10, 30, 60, and 100 cm. The first three layers form the root zone in non-forested regions, with the fourth layer included in forested regions.\u003cbr\u003eDetails about the NLDAS-2 configuration of the Noah LSM can be found in Xia and others (2012).","doi_url":"https://doi.org/10.5067/T4OW83T8EXDO","domain":["Climate","Snow","Land Cover","Hydrology"],"draft":false,"id":"cf5de2b6-a67c-4a90-9cb5-1130599aa814","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://disc.gsfc.nasa.gov/datasets/NLDAS_NOAH0125_H_2.0/summary"}],"name":"NLDAS Noah Land Surface Model L4 Hourly 0.125 x 0.125 degree V2.0 (NLDAS_NOAH0125_H)","permalink":"/catalog/datasets/cf5de2b6-a67c-4a90-9cb5-1130599aa814/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"CONUS","spatial_resolution":"0.125 degrees","temporal_coverage":"1979 - Present","temporal_frequency":"hourly","update_detail":"append","update_frequency":"daily","update_type":"Dynamic","variables":["Total evapotranspiration","Water equivalent of accumulated snow depth","Snow depth","Albedo","Soil temperature","Soil moisture content","Vegetation","Snow melt","Net shortwave radiation flux (surface)","Net longwave radiation flux (surface)","Latent heat flux","Sensible heat flux","Potential latent heat flux (potential evaporation)","Average surface skin temperature","Liquid soil moisture content (non-frozen)","Ground heat flux","Frozen precipitation (for example, snowfall)","Liquid precipitation (rainfall)","Aerodynamic conductance","Canopy conductance","Leaf area index","Sublimation (evaporation from snow)","Direct evaporation from bare soil","Canopy water evaporation","Minimal stomatal resistance","Downward shortwave radiation flux","Downward longwave radiation flux","Moisture availability","Transpiration","Plant canopy surface water","Snow phase-change heat flux","Subsurface runoff (baseflow)","Surface runoff (non-infiltrating)","Snow cover","Solar parameter in canopy conductance","Temperature parameter in canopy conductance","Humidity parameter in canopy conductance","Soil moisture parameter in canopy conductance","Root zone soil moisture content","Relative soil moisture availability control factor"],"vars":"Total evapotranspiration; Water equivalent of accumulated snow depth; Snow depth; Albedo; Soil temperature; Soil moisture content; Vegetation; Snow melt; Net shortwave radiation flux (surface); Net longwave radiation flux (surface); Latent heat flux; Sensible heat flux; Potential latent heat flux (potential evaporation); Average surface skin temperature; Liquid soil moisture content (non-frozen); Ground heat flux; Frozen precipitation (for example, snowfall); Liquid precipitation (rainfall); Aerodynamic conductance; Canopy conductance; Leaf area index; Sublimation (evaporation from snow); Direct evaporation from bare soil; Canopy water evaporation; Minimal stomatal resistance; Downward shortwave radiation flux; Downward longwave radiation flux; Moisture availability; Transpiration; Plant canopy surface water; Snow phase-change heat flux; Subsurface runoff (baseflow); Surface runoff (non-infiltrating); Snow cover; Solar parameter in canopy conductance; Temperature parameter in canopy conductance; Humidity parameter in canopy conductance; Soil moisture parameter in canopy conductance; Root zone soil moisture content; Relative soil moisture availability control factor","weight":1},{"access":[{"file_format":"HDF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-mod15a2h-061"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Myneni, R., Y. Knyazikhin, T. Park., 2021, MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061., distributed by NASA EOSDIS Land Processes DAAC, Accessed [YYYY-MM-DD], https://doi.org/10.5067/MODIS/MOD15A2H.061.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The MOD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the \"best\" pixel available from all the acquisitions of the Terra sensor from within the 8-day period. \u003cbr\u003eLAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation, 400-700 nanometers (nm), absorbed by the green elements of a vegetation canopy. \u003cbr\u003eScience Datasets (SDSs) in the Level 4 (L4) MOD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MOD15A2H granule.","doi_url":"https://doi.org/10.5067/MODIS/MOD15A2H.061","domain":["Land Cover"],"draft":false,"id":"cf689e74-3adf-4fab-b9d5-6492bbf4ab97","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/documents/926/MOD15_User_Guide_V61.pdf"}],"name":"MOD15A2H v061 MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid","permalink":"/catalog/datasets/cf689e74-3adf-4fab-b9d5-6492bbf4ab97/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS; NASA","spatial_extent":"Global","spatial_resolution":"500 meter","temporal_coverage":"2000 - Present","temporal_frequency":"8 days","update_detail":"append","update_frequency":"8 days","update_type":"Dynamic","variables":["Leaf area index"],"vars":"Leaf area index","weight":1},{"access":[{"file_format":"xlsx; GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5fce600bd34e30b912396ad0"}],"access_details":null,"bbox":{"east":-65.3459930419922,"north":49.2104204456503,"south":17.4679176436679,"west":-125.404815673828},"citation":"Miller-Corbett, C.D. and Curtis, M.H., 2023, U.S. Geological Survey Inland to Coastal Zone Bathymetric and Topobathymetric Survey Inventory, version 3: U.S. Geological Survey data release, https://doi.org/10.5066/P9PDX9X3.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The U.S. Geological Survey (USGS) Inland Bathymetric and Topobathymetric Survey Inventory, v. 3 includes a survey records inventory and dataset footprints (when available) for inland bathymetric and topobathymetric surveys published by the USGS for the conterminous US and Puerto Rico. Survey records include water feature, state, publication title, data vintage, mission, online linkage to reports and datasets, collection methods, survey and survey product resolution, datums, geoid, and accuracy information if known. This database, identified as the USGS Inland Bathymetric and Topobathymetric Survey Inventory, v.3, has been approved for release by the U.S. Geological Survey (USGS). Although this database has been subjected to rigorous review and is substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. Furthermore, the database is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.","doi_url":"https://doi.org/10.5066/P9PDX9X3","domain":["Stream Characteristics","Hydrology","Topography"],"draft":false,"id":"cf8370b2-75c7-4888-8574-67aa379e828d","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"U.S. Geological Survey Inland Bathymetric and Topobathymetric Survey Inventory, version 3","permalink":"/catalog/datasets/cf8370b2-75c7-4888-8574-67aa379e828d/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"varies","temporal_coverage":"2006 - 2023","temporal_frequency":"NA","update_detail":"append and modify","update_frequency":"irregular","update_type":"Dynamic","variables":["Bathymetry"],"vars":"Bathymetry","weight":1},{"access":[{"file_format":"GPKG","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6644f800d34e1955f5a42da9"}],"bbox":{"east":-115.1567,"north":71.636,"south":51.3301,"west":-177.7148},"citation":"Bock, A.R., Santiago, M., Wieczorek, M.E., Koczot, K.M., Markstrom, S.L., Norton, P.A., and Blodgett, D.L., 2024, Geospatial Fabric for National Hydrologic Modeling, Alaska Domain: U.S. Geological Survey data release, https://doi.org/10.5066/P13FOGMM.","creator":[{"creator_email":"abock@usgs.gov","creator_name":"Andrew R Bock"}],"creator_project":[],"date_created":"1/14/2026","date_updated":"6/3/2026","description":"\u003cp\u003eThe Geospatial Fabric for National Hydrologic Modeling (Viger and Bock, 2014; Bock and others, 2020) is a dataset of hydrographic features and spatial data designed for use within the National Hydrologic Model that covers the conterminous United States (CONUS), Hawaii, and most major river basins that flow in from Canada. This U.S. Geological Survey (USGS) data release consists of the geospatial fabric features and other related spatial datasets created to expand the National Hydrologic Model to Alaska.\u003c/p\u003e\n\n\u003cp\u003eThis child item contains data and information related to the GIS features of the Geospatial Fabric for National Hydrologic Model, Alaska domain. Two Open Geospatial Consortium geopackages are provided: one containing source layers that have had some pre-processing done from their native data formats (Reference_19.gpkg), and one (GF_19.gpkg) containing 4 final feature layers for the NHM: points of interest (POIs), a stream network (nsegment), aggregated catchments (catchments), and hydrologic response units (nhru). Features were derived from the MERRIT Hydro Global Hydrography Dataset.\u003c/p\u003e\n","domain":["Hydrology"],"draft":false,"id":"d00aefec-91b8-4710-9bd3-258f3fa8b0b2","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6644f800d34e1955f5a42da9?f=__disk__6d%2F28%2F60%2F6d28608cd296e495fefb5ea93a91ebbc2c689c52\u0026allowOpen=true"}],"name":"GIS Features of the Geospatial Fabric for the National Hydrologic Model, Alaska Domain","permalink":"/catalog/datasets/d00aefec-91b8-4710-9bd3-258f3fa8b0b2/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"AK","spatial_resolution":"1:100,000","temporal_coverage":"2024","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["fid","geom","hu12","comid","hydroseq","fid","huc12","tohuc","geom","fid","objectid","gnis_name","geom","fid","comid","lengthkm","totdasqkm","streamorde","tocomid","arbolate_sum","outletcomid","hydroseq","levelpathI","terminalpa","pathlength","dnlevelpat","dnhydroseq","terminalfl","reachcode","tomeas","frommeas","areasqkm","fromcomid","uphydroseq","maxelevxmo","minelevsmo","startflag","geom","fid","comid","geom","fid","comid","type_huc12","type_gages","type_wbin","type_wbout","type_conf","type_term","type_elev","type_con","seg_id","gage_sourc","geom","fid","seg_id","to_segment","lengthkm","geom","fid","cat_id","areasqkm","geom","fid","hru_id","areasqkm","cat_id","hru_segmen","geom"],"vars":"fid; geom; hu12; comid; hydroseq; fid; huc12; tohuc; geom; fid; objectid; gnis_name; geom; fid; comid; lengthkm; totdasqkm; streamorde; tocomid; arbolate_sum; outletcomid; hydroseq; levelpathI; terminalpa; pathlength; dnlevelpat; dnhydroseq; terminalfl; reachcode; tomeas; frommeas; areasqkm; fromcomid; uphydroseq; maxelevxmo; minelevsmo; startflag; geom; fid; comid; geom; fid; comid; type_huc12; type_gages; type_wbin; type_wbout; type_conf; type_term; type_elev; type_con; seg_id; gage_sourc; geom; fid; seg_id; to_segment; lengthkm; geom; fid; cat_id; areasqkm; geom; fid; hru_id; areasqkm; cat_id; hru_segmen; geom","weight":1},{"access":[{"file_format":"NC; GRIB","name":"ldas.gsfc.nasa.gov","url":"https://ldas.gsfc.nasa.gov/nldas/v2/forcing?msclkid=f5dfc55cb50a11ecaad7bfa3ea49a9fe"}],"access_details":null,"bbox":{"east":-50,"north":72,"south":14,"west":-180},"citation":"Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., Mocko, D., 2012, Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J. Geophys. Res., 117, D03109, https://doi.org/10.1029/2011JD016048.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset contains the forcing data for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th-degree grid spacing and range from 01 Jan 1979 to present. The temporal resolution is hourly.\u003cbr\u003eThe non-precipitation land surface forcing datasets used by NLDAS-2 for temperature are based on the NCEP North American Regional Reanalysis (NARR), which includes retrospective data for the historical past and an operationally assimilated daily updated data set from the Regional Climate Data Assimilation System (R-CDAS).NARR analysis fields are 32-km spatial resolution and 3-hourly temporal frequency. Those NARR fields that are utilized to generate NLDAS-2 forcing fields are spatially interpolated to the finer resolution of the NLDAS 1/8th-degree grid and then temporally disaggregated to the NLDAS hourly frequency.\u003cbr\u003e The full list of variables includes: U wind component (m/s) at 10 meters above the surface; V wind component (m/s) at 10 meters above the surface; Air temperature (K)\u0026#10035; at 2 meters above the surface; Specific humidity (kg/kg)\u0026#10035;  at 2 meters above the surface; Surface pressure (Pa)\u0026#10035;; Surface downward longwave radiation (W/m2)\u0026#10035;; Surface downward shortwave radiation (W/m2) -- bias-corrected; Precipitation hourly total (kg/m2); Fraction of total precipitation that is convective (no units) from NARR; CAPE: Convective Available Potential Energy  (J/kg) from NARR; Potential evaporation (kg/m2): from NARR.\u003cbr\u003e Note: \u0026#10035; Indicates a field to which vertical adjustment is applied.","doi_url":"https://doi.org/10.1029/2011JD016048","domain":["Climate"],"draft":false,"id":"d0f4adbb-086c-4673-ba40-65c23d585e8d","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"NLDAS-2 forcing files and land model output files","permalink":"/catalog/datasets/d0f4adbb-086c-4673-ba40-65c23d585e8d/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"North America","spatial_resolution":"0.125 degrees","temporal_coverage":"1979 - Present","temporal_frequency":"hourly; monthly","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["U wind component","V wind component","Air temperature","Specific humidity","Surface pressure","Surface downward longwave radiation","Surface downward shortwave radiation","Precipitation hourly total","Fraction of total precipitation that is convective","CAPE (Convective Available Potential Energy)","Potential evaporation"],"vars":"U wind component; V wind component; Air temperature; Specific humidity; Surface pressure; Surface downward longwave radiation; Surface downward shortwave radiation; Precipitation hourly total; Fraction of total precipitation that is convective; CAPE (Convective Available Potential Energy); Potential evaporation","weight":1},{"access":[{"file_format":"GDB; SHP","name":"hydrosheds.org","url":"https://www.hydrosheds.org/products/gloric#downloads"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Ouellet Dallaire, C., Lehner, B., Sayre, R., Thieme, M., 2019, A multidisciplinary framework to derive global river reach classifications at high spatial resolution. Environmental Research Letters, 14(2): 024003. https://doi.org/10.1088/1748-9326/aad8e9\u003cbr\u003eOuellet Dallaire, C., Lehner, B., Creed, I., 2020, Multidisciplinary classification of Canadian river reaches to support the sustainable management of freshwater systems. Canadian Journal of Fisheries and Aquatic Sciences, 77(2): 326–341. https://doi.org/10.1139/cjfas-2018-0284","creator":[],"creator_project":[],"date_created":"5/6/2024","date_updated":"6/3/2026","description":"The Global River Classification (GloRiC) provides river types and sub-classifications for all river reaches contained in the HydroRIVERS database. GloRiC has been developed by utilizing the river network delineation of HydroRIVERS combined with the hydro-enviromental characteristics from the HydroATLAS database and auxiliary information.","doi_url":"https://doi.org/10.1088/1748-9326/aad8e9","domain":["Hydrology","Stream Characteristics"],"draft":false,"id":"d196f5ef-f98d-46a6-8669-ab9faeabb3f5","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://data.hydrosheds.org/file/technical-documentation/GloRiC_TechDoc_v10.pdf"}],"name":"GloRiC: Global River Classification","permalink":"/catalog/datasets/d196f5ef-f98d-46a6-8669-ab9faeabb3f5/","project_use_history":[{"id":"DJ60TRJ","name":"NHGF: National Hydrologic Geospatial Fabric"}],"project_using":[{"id":"DJ60TRJ","name":"NHGF: National Hydrologic Geospatial Fabric"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"HydroSHEDS","spatial_extent":"Global","spatial_resolution":"15 arcsecond","temporal_coverage":"2018","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Reach_ID","Next_down","Length_km","Log_Q_avg","Log_Q_var","Class_hydr","Temp_min","CMI_indx","Log_elev","Class_phys","Lake_wet","Stream_pow","Class_geom","Reach_type","Kmeans_30"],"vars":"Reach_ID; Next_down; Length_km; Log_Q_avg; Log_Q_var; Class_hydr; Temp_min; CMI_indx; Log_elev; Class_phys; Lake_wet; Stream_pow; Class_geom; Reach_type; Kmeans_30","weight":1},{"access":[{"file_format":"GDB; CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/61707c2ad34ea36449a6b066"}],"access_details":null,"bbox":{"east":-61.5234,"north":71.35,"south":18.6462,"west":-178.21},"citation":"Welty, J.L., and Jeffries, M.I., 2021, Combined wildland fire datasets for the United States and certain territories, 1800s-Present: U.S. Geological Survey data release, https://doi.org/10.5066/P9ZXGFY3","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"These datasets were created by combining 40 different, published wildland fire data sources. Each one of these data sources has a different spatial scale, spatial resolution, and time period for their particular wildland fire dataset. The purpose of these new datasets is to combine these disparate wildfire datasets, using a common set of attributes, into a single set of polygons with a single fire boundary for each fire. This dataset is intended to create a more comprehensive fire dataset than the existing datasets while eliminating duplication of fire polygons and attributes.","doi_url":"https://doi.org/10.5066/P9ZXGFY3","domain":["Land Cover"],"draft":false,"id":"d1ad674c-04db-4303-bb1b-54e55397968c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Combined wildland fire datasets for the United States and certain territories, 1800s-Present (combined wildland fire polygons)","permalink":"/catalog/datasets/d1ad674c-04db-4303-bb1b-54e55397968c/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; AK; HI; PR; VI","spatial_resolution":"varies","temporal_coverage":"1835 - 2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Wildland fire polygons"],"vars":"Wildland fire polygons","weight":1},{"access":[{"file_format":"GPKG; CSV; TIFF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5fc51e76d34e4b9faad8877e"}],"access_details":null,"bbox":{"east":-154.65,"north":22.6,"south":18.8,"west":-160.35},"citation":"Bock, A.R., Rosa, S.N., McDonald, R.R., Wieczorek, M.E., Santiago, M., Blodgett, D.L., and Norton, P.A., 2024, Geospatial Fabric for National Hydrologic Modeling, Hawaii Domain: U.S. Geological Survey data release, https://doi.org/10.5066/P9HMKOP8.","creator":[],"creator_project":[{"id":"DJ60TRJ","name":"NHGF: National Hydrologic Geospatial Fabric"}],"date_created":"7/10/2024","date_updated":"6/3/2026","description":"The Geospatial Fabric is a dataset of spatial modeling units for use within the National Hydrologic Model that covers the conterminous United States (CONUS), Alaska, and most major river basins that flow in from Canada.  This U.S. Geological Survey (USGS) data release consists of the geospatial fabric features and other related datasets created to expand the National Hydrologic Model to Hawaii.\u003cbr\u003eThese datasets are found as child items to this landing page: 1) Data Layers for the Geospatial Fabric for National Hydrologic Modeling, Hawaii Domain, 2) GIS (Geographic Information Systems) Features of the Geospatial Fabric for National Hydrologic Modeling, Hawaii Domain, 3) Parameter Database for the National Hydrologic Modeling, Hawaii Domain, and 4) Topographic derivative datasets for the Geospatial Fabric for National Hydrologic Modeling, Hawaii Domain. See each item for more details.","doi_url":"https://doi.org/10.5066/P9HMKOP8","domain":["Topography","Hydrology"],"draft":false,"id":"d39b528b-35e0-49e9-8b0c-61f66c858295","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5fc51e76d34e4b9faad8877e?f=__disk__40%2F9c%2F48%2F409c48d6ca0f4fad74f703497f99d5a8bb4659a8\u0026allowOpen=true"}],"name":"Geospatial Fabric for National Hydrologic Modeling, Hawaii Domain","permalink":"/catalog/datasets/d39b528b-35e0-49e9-8b0c-61f66c858295/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"ref_fabric":true,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"HI","spatial_resolution":"1:100,000; 250 meter; 30 meter","temporal_coverage":"2024","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["average water capacity","percent sand","percent silt","percent clay","soil texture","land use and land cover","snow values","summer rain values","winter rain values","leaf presence","leaf loss","percent tree canopy","percent impervious surface","snow depletion","rooting depth","permeability","water bodies"],"vars":"average water capacity; percent sand; percent silt; percent clay; soil texture; land use and land cover; snow values; summer rain values; winter rain values; leaf presence; leaf loss; percent tree canopy; percent impervious surface; snow depletion; rooting depth; permeability; water bodies","weight":1},{"access":[{"file_format":"NC","name":"daac.ornl.gov","url":"https://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=32"}],"bbox":{"east":-56,"north":72,"south":17,"west":-180},"citation":"Thornton, M.M., Shrestha, R., Wei, Y., Thornton, P.E., Kao, S., and Wilson, B.E.,  2020, Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4. ORNL DAAC, Oak Ridge, Tennessee, USAm, https://doi.org/10.3334/ORNLDAAC/1840","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset provides Daymet Version 4 data as gridded estimates of daily weather parameters for North America, Hawaii, and Puerto Rico. Daymet variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset covers the period from January 1, 1980, to December 31 (or December 30 in leap years) of the most recent full calendar year for the Continental North America and Hawaii spatial regions. Data for Puerto Rico is available starting in 1950. Each subsequent year is processed individually at the close of a calendar year. Daymet variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Areas of Hawaii and Puerto Rico are available as files separate from the continental North America. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant netCDF file format.","doi_url":"https://doi.org/10.3334/ORNLDAAC/1840","domain":["Climate"],"draft":false,"id":"d3e5f9e7-9ce3-45b3-9f78-0c7d720a4cc9","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://daymet.ornl.gov/files/Thornton_Daymet_V4_submitted_2021-01-20.pdf"}],"name":"Daymet v4","permalink":"/catalog/datasets/d3e5f9e7-9ce3-45b3-9f78-0c7d720a4cc9/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"ORNL","spatial_extent":"North America; HI; PR","spatial_resolution":"1 kilometer","temporal_coverage":"1980 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Temperature, minimum","Temperature, maximum","Precipitation","Snow water equivalent","Short-wave radiation","Vapor pressure","Length of day"],"vars":"Temperature, minimum; Temperature, maximum; Precipitation; Snow water equivalent; Short-wave radiation; Vapor pressure; Length of day","weight":1},{"access":[{"file_format":"TIF","name":"developers.google.com","url":"https://developers.google.com/s/results/earth-engine/datasets/?q=openet\u0026hl=en\u0026text=openet%20conus%20evapotranspiration%20v2.0"},{"file_format":"TIF","name":"developers.google.com","url":"https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0"},{"file_format":"TIF","name":"openet.gitbook.io","url":"https://openet.gitbook.io/docs"},{"file_format":"TIF","name":"wma.code-pages.usgs.gov","url":"https://wma.code-pages.usgs.gov/osd/openet/openet_et_training/training_resources.html"}],"access_details":"The first developers.google.com access url is to the public image collections on Google Earth Engine Data Catalog (for each of the 6 ET models), the second developers.google.com access url is for the ensemble, the openet.gitbook.io access url is for the official documentation for the API, and the wma.code-pages.usgs.gov access url is for an internal API guide.","bbox":{"east":-100,"north":51,"south":24,"west":-127},"citation":"Melton, F. S., Huntington, J., Grimm, R., Herring, J., Hall, M., Rollison, D., Erickson, T., Allen, R., Anderson, M., Fisher, J.B., Kilic, A., Senay, G.B., Volk, J., Hain, C., Johnson, L., Ruhoff, A., Blankenau, P., Bromley, M., Carrara, W., Daudert, B., Doherty, C., Dunkerly, C., Friedrichs, M., Guzman, A., Halverson, G., Hansen, J., Harding, J., Kang, Y., Ketchum, D., Minor, B., Morton, C., Ortega-Salazar, S., Ott, T., Ozdogan, M., ReVelle, P.M., Schull, M., Wang, C., Yang, Y., Anderson, R.G., 2022, OpenET: Filling a Critical Data Gap in Water Management for the Western United States: Journal of the American Water Resources Association, v 58, no 6,  https://doi.org/10.1111/1752-1688.12956","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Temporal coverage: 2013-23 (stable), 1985-2012 (provisional), and 2024-Present (provisional)\u003cbr\u003eThe OpenET project is a community-driven effort that is developing an operational system for generating and distributing evapotranspiration (ET) data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within +/-8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.\u003cbr\u003e\u003cbr\u003eOpenET has released the OpenET Data Explorer for use by the public, and the API is currently being evaluated and tested by use-case partners. The Data Explorer and API provide access to 30 meter resolution data for the 17 western states within the contiguous U.S. for the period from 2016 to present. In the near future, OpenET will also release the Reporting Interface, along with the API and OpenET Best Practices manual, and will make data from the OpenET gridded raster data archives available in the Earth Engine data catalog. OpenET is currently working to produce retrospective data from 2006 to 2015, with the potential to extend the data record back to 1984 - the start of the Landsat 5 data record.","doi_url":"https://doi.org/10.1111/1752-1688.12956","domain":["Hydrology","Climate"],"draft":false,"id":"d3f21635-253a-4254-9ca3-12c0df92fcc4","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.1111/1752-1688.12956"}],"name":"OpenET","permalink":"/catalog/datasets/d3f21635-253a-4254-9ca3-12c0df92fcc4/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"OpenET","spatial_extent":"17 western states","spatial_resolution":"30 meter","temporal_coverage":"1985 - Present","temporal_frequency":"NA","update_detail":"append","update_frequency":"annual","update_type":"Dynamic","variables":["Actual ET","Fraction of Reference ET","Reference ET","Precipitation (from gridMET)","Normalized Difference Vegetation Index (NDVI)"],"vars":"Actual ET; Fraction of Reference ET; Reference ET; Precipitation (from gridMET); Normalized Difference Vegetation Index (NDVI)","weight":1},{"access":[{"file_format":"HDF","name":"nsrdb.nrel.gov","url":"https://nsrdb.nrel.gov/data-sets/how-to-access-data"}],"access_details":null,"bbox":{"east":-55,"north":55,"south":14,"west":-180},"citation":"Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., Shelby, J., 2018, The National Solar Radiation Data Base (NSRDB), Renewable and Sustainable Energy Reviews, v 89, ppg 51-60, iss 1364-0321, https://doi.org/10.1016/j.rser.2018.03.003.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The National Solar Radiation Data Base (NSRDB), consisting of solar radiation and meteorological data over the United States and regions of the surrounding countries, is a publicly open dataset that has been created and disseminated during the last 23 years. This paper briefly reviews the complete package of surface observations, models, and satellite data used for the latest version of the NSRDB as well as improvements in the measurement and modeling technologies deployed in the NSRDB over the years. The current NSRDB provides solar irradiance at a 4-km horizontal resolution for each 30-min interval from 1998 to 2016 computed by the National Renewable Energy Laboratory's (NREL's) Physical Solar Model (PSM) and products from the National Oceanic and Atmospheric Administration's (NOAA's) Geostationary Operational Environmental Satellite (GOES), the National Ice Center's (NIC's) Interactive Multisensor Snow and Ice Mapping System (IMS), and the National Aeronautics and Space Administration's (NASA's) Moderate Resolution Imaging Spectroradiometer (MODIS) and Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). The NSRDB irradiance data have been validated and shown to agree with surface observations with mean percentage biases within 5% and 10% for global horizontal irradiance (GHI) and direct normal irradiance (DNI), respectively. The data can be freely accessed via https://nsrdb.nrel.gov or through an application programming interface (API). During the last 23 years, the NSRDB has been widely used by an ever-growing group of researchers and industry both directly and through tools such as NREL's System Advisor Model.","doi_url":"https://doi.org/10.1016/j.rser.2018.03.003","domain":["Climate"],"draft":false,"id":"d4651c8f-4a87-4826-a5ae-7a9bc856158e","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"National Solar Radiation Data Base (NRSB)","permalink":"/catalog/datasets/d4651c8f-4a87-4826-a5ae-7a9bc856158e/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NREL","spatial_extent":"United States","spatial_resolution":"4 kilometer","temporal_coverage":"1998 - 2024","temporal_frequency":"30 minutes","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Global Horizontal Irradiance (GHI)","Direct Normal Irradiance (DNI)"],"vars":"Global Horizontal Irradiance (GHI); Direct Normal Irradiance (DNI)","weight":1},{"access":[{"file_format":"HDF","name":"nsidc.org","url":"https://nsidc.org/data/myd10a1/versions/61"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Hall, D.K. andRiggs,  G.A., 2021, MODIS/Aqua Snow Cover Daily L3 Global 500m SIN Grid, Version 61. [Indicate subset used]. Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed [YYYY-MM-DD], https://doi.org/10.5067/MODIS/MYD10A1.061","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS/Aqua Snow Cover 5-Min L2 Swath 500m' data set (DOI:10.5067/MODIS/MYD10_L2.061). Each data granule is a 10 degree x 10 degree tile projected to a 500 m sinusoidal grid. MODIS Aqua daily Normalized Daily Snow Index and Albedo.","doi_url":"https://doi.org/10.5067/MODIS/MYD10A1.061","domain":["Snow"],"draft":false,"id":"d5f17213-5607-41f3-b527-2bc77f726333","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"MYD10A1: MODIS/Aqua Snow Cover Daily L3 Global 500m SIN Grid, Version 61","permalink":"/catalog/datasets/d5f17213-5607-41f3-b527-2bc77f726333/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NSIDC","spatial_extent":"Global","spatial_resolution":"500 meter","temporal_coverage":"2002 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Snow cover"],"vars":"Snow cover","weight":1},{"access":[{"file_format":"e00","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/631405bed34e36012efa30fd"}],"bbox":{"east":-65.5,"north":50.2,"south":23.24,"west":-127.8},"citation":"Alexander, R.B., Brakebill, J.W., Brew, R.E., and Smith, R.A., 1999, ERF1 -- Enhanced River Reach File 1.2: U.S. Geological Survey data release, https://doi.org/10.5066/P93JFBOE.","creator":[],"creator_project":[],"date_created":"1/23/2026","date_updated":"6/3/2026","description":"River reaches capable of supporting regional and national water-quality and river-flow modeling and transport investigations in the water-resources community. ERF1 has been recently used at the U.S Geological Survey to support interpretations of stream water-quality monitoring network data (see Alexander and others, 1996; Smith and others, 1995).  In these analyses, the reach network has been used to determine flow pathways between the sources of point and nonpoint pollutants (e.g., fertilizer use, municipal wastewater discharges) and downstream water-quality monitoring locations in support of predictive water-quality models of stream nutrient transport.","doi_url":"https://doi.org/10.5066/P93JFBOE","domain":["Hydrology"],"draft":false,"id":"d602e02e-94ac-4a2e-984c-093d4d2f940d","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/631405bed34e36012efa30fd?f=__disk__6b%2F78%2F76%2F6b7876f256e2f0c55f6167211ccd78b9facc8402\u0026allowOpen=true"}],"name":"ERF1 -- Enhanced River Reach File 1.2","permalink":"/catalog/datasets/d602e02e-94ac-4a2e-984c-093d4d2f940d/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:500,000","temporal_coverage":"1990","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["FNODE#","TNODE#","LPOLY#","RPOLY#","LENGTH","ERF1#","ERF1-ID","HUC","SEG","RFLAG","OWFLAG","TFLAG","SFLAG","TYPE","SEGL","LEV","PNAME","PNMCD","OWNAME","OWNMCD","RESCODE","RESVOIR1","RESVOIR2","RESVOIR3","RESVOIR4","ERF1##","E2RF1##","PNAME-RES","PNMCD-RES","MEANQ","MEANV","RCHAREA","TOTAREA","STRAHLER","STATE1","STATE2","STATE3","RCHTOT","RESTOT","TERMFLAG","CONTFLAG","LRATIO","EDACDA","EDANAME","RR","HUC2","HUC4","HUC6","CONTFLAG","EDACDA","EDANAME","ERF1#","ERF1##","E2RF1##","ERF1-ID","FNODE#","HUC","HUC2","HUC4","HUC6","LENGTH","LEV","LPOLY#","LRATIO","MEANQ","MEANV","OWFLAG","OWNAME","OWNMCD","PNAME","PNAME-RES","PNMCD","PNMCD-RES","RCHAREA","RCHTOT","RESCODE","RESTOT","RESVOIR1","RESVOIR2","RESVOIR3","RESVOIR4","RFLAG","RPOLY#","RR","SEG","SEGL","SFLAG","STATE1","STATE2","STATE3","STRAHLER","TERMFLAG","TFLAG","TNODE#","TOTAREA","TYPE"],"vars":"FNODE#; TNODE#; LPOLY#; RPOLY#; LENGTH; ERF1#; ERF1-ID; HUC; SEG; RFLAG; OWFLAG; TFLAG; SFLAG; TYPE; SEGL; LEV; PNAME; PNMCD; OWNAME; OWNMCD; RESCODE; RESVOIR1; RESVOIR2; RESVOIR3; RESVOIR4; ERF1##; E2RF1##; PNAME-RES; PNMCD-RES; MEANQ; MEANV; RCHAREA; TOTAREA; STRAHLER; STATE1; STATE2; STATE3; RCHTOT; RESTOT; TERMFLAG; CONTFLAG; LRATIO; EDACDA; EDANAME; RR; HUC2; HUC4; HUC6; CONTFLAG; EDACDA; EDANAME; ERF1#; ERF1##; E2RF1##; ERF1-ID; FNODE#; HUC; HUC2; HUC4; HUC6; LENGTH; LEV; LPOLY#; LRATIO; MEANQ; MEANV; OWFLAG; OWNAME; OWNMCD; PNAME; PNAME-RES; PNMCD; PNMCD-RES; RCHAREA; RCHTOT; RESCODE; RESTOT; RESVOIR1; RESVOIR2; RESVOIR3; RESVOIR4; RFLAG; RPOLY#; RR; SEG; SEGL; SFLAG; STATE1; STATE2; STATE3; STRAHLER; TERMFLAG; TFLAG; TNODE#; TOTAREA; TYPE","weight":1},{"access":[{"file_format":"CSV; TXT; NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/661eb368d34e7eb9eb7e3e19"}],"access_details":null,"bbox":{"east":-65.2,"north":18.7,"south":17.7,"west":-67.4},"citation":"LaFontaine, J.H., Swain, E.D., Norton, P.A., McDonald, R.R., Markstrom, S.L., Regan, R.S., and Bellino, J.C., 2024, Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System for Puerto Rico, Geospatial Fabric version 1.0, and Daymet version 4 Atmospheric Forcings, 1950-2021: U.S. Geological Survey data release, https://doi.org/10.5066/P14FNBF7.","creator":[{"creator_email":"jlafonta@usgs.gov","creator_name":"Jacob H LaFontaine"}],"creator_project":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"date_created":"5/5/2024","date_updated":"6/3/2026","description":"This data release contains inputs for and outputs from hydrologic simulations for Puerto Rico using the Precipitation Runoff Modeling System (PRMS) version 5.2.1, the USGS National Hydrologic Model infrastructure (NHM, Regan and others, 2018), National Hydrologic Geospatial Fabric version 1.0 (Viger and Bock, 2014), and the Daymet version 4 (Thornton et. al., 2020) atmospheric forcing dataset. These simulations were developed to provide estimates of the water budget for the period 1950 to 2021. The model parameters and associated model output included in this data release are described in Swain and Bellino (2022). Specific data included are: 1) model input files, 2) output files of simulated water budget components for each hydrologic response unit (HRU) and stream segment, and 3) performance statistics at selected streamgage locations. The first three years of the simulations are considered 'model initialization' and should not be included in any subsequent analysis.\u003cbr\u003e\n\u003cbr\u003e\nModel input files, located on the “ Input Data for Hydrologic Simulations of Puerto Rico using the NHM-PRMS, 1950-2021, Daymet version 4” child page, include ASCII formatted PRMS input files of 1) daily time step atmospheric forcings of minimum air temperature (tmin.day), maximum air temperature (tmax.day), precipitation accumulation (precip.day), and humidity (humidity.day), 2) PRMS model parameters (NHM-PRMS.param), 3) a PRMS control file that provides the simulation configuration information (NHM-PRMS_data_release.control), 4) and the PRMS data file that includes time series of streamflow observations (sf_data). Descriptions of model input parameters are included in the parameters_data_dictionary.csv file on this main page. Descriptions of control file parameters are included in the control_data_dictionary.csv file on this main page. Additional information about the model calibration and associated parameters is provided in Swain and Bellino (2022).\u003cbr\u003e\n\u003cbr\u003e\nModel output files, located on the “Output Data for Hydrologic Simulations of Puerto Rico using the NHM-PRMS, 1950-2021, Daymet version 4” child page, include 18 PRMS output variables for the model simulation. Each NetCDF format output file contains daily time step outputs for the period 1950-2021 for each hydrologic response unit or stream segment in the model application. Descriptions of model output variables are included in the output_variables_data_dictionary.csv file on this main page.\u003cbr\u003e\n\u003cbr\u003e\nStreamflow statistics of model performance at selected streamgages are located on this main page (gage_stats_pr.csv).","doi_url":"https://doi.org/10.5066/P14FNBF7","domain":["Climate","Hydrology"],"draft":false,"id":"d6943b6f-e353-40b5-aada-dfa095243da7","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/661eb368d34e7eb9eb7e3e19?f=__disk__88%2F6a%2F14%2F886a140bafeb93cdf013d08fd1383d083aa2df63\u0026allowOpen=true"}],"name":"Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System for Puerto Rico, Geospatial Fabric version 1.0, and Daymet version 4 Atmospheric Forcings, 1950-2021","permalink":"/catalog/datasets/d6943b6f-e353-40b5-aada-dfa095243da7/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"PR","spatial_resolution":"1:100,000","temporal_coverage":"1950 - 2021","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["poi_id","por_days","kge","ns","nslog","bias","variable_name","datatype","description","default","parameter_name","datatype","units","description","valid_minimum","valid_maximum","default","dimension","modules","variable_name","datatype","description","units","dimension"],"vars":"poi_id; por_days; kge; ns; nslog; bias; variable_name; datatype; description; default; parameter_name; datatype; units; description; valid_minimum; valid_maximum; default; dimension; modules; variable_name; datatype; description; units; dimension","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5b96c2f9e4b0702d0e826f6d"}],"access_details":null,"bbox":{"east":-66.264957220208,"north":49.000932513965,"south":23.673440501823,"west":-125.15167596785},"citation":"Sohl, T.L., Sayler, K.L., Bouchard, M.A., Reker, R.R., Freisz, A.M., Bennett, S.L., Sleeter, B.M., Sleeter, R.R., Wilson, T., Soulard, C., Knuppe, M., and Van Hofwegen, T., 2018, Conterminous United States Land Cover Projections - 1992 to 2100: U.S. Geological Survey data release, https://doi.org/10.5066/P95AK9HP.","creator":[{"creator_email":"sohl@usgs.gov","creator_name":"Terry Sohl"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The USGS’s FORE-SCE model was used to produce land-use and land-cover (LULC) projections for the conterminous United States. The projections were originally created as part of the \"LandCarbon\" project, an effort to understand biological carbon sequestration potential in the United States. However, the projections are being used for a wide variety of purposes, including analyses of the effects of landscape change on biodiversity, water quality, and regional weather and climate.\u003cbr\u003e\n\u003cbr\u003e\nThe year 1992 served as the baseline for the landscape modeling. The 1992 to 2005 period was considered the historical baseline, with datasets such as the National Land Cover Database (NLCD), USGS Land Cover Trends, and US Department of Agriculture's Census of Agriculture used to guide the recreation of historical land cover for this period. 2006 to 2100 was considered the future projection time frame. Four scenarios were modeled for 2006 to 2100, corresponding to four major scenario storylines from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES). The global IPCC SRES (A1B, A2, B1, and B2 scenarios) were downscaled to ecoregions in the conterminous United States, with the USGS Forecasting Scenarios of land use (FORE-SCE) model used to produce landscape projections consistent with the IPCC SRES. The land-use scenarios focused on socioeconomic impacts on anthropogenic land use (demographics, energy use, agricultural economics, and other socioeconomic considerations). The projections provided here are characterized by:\u003cbr\u003e\n1) 250-meter spatial resolution (250-m pixels)\u003cbr\u003e\n2) 17 land-cover classes, similar to classes from NLCD\u003cbr\u003e\n3) Annual land cover maps from 1992 to 2100\u003cbr\u003e\n4) Spatial coverage for the entire conterminous United States\u003cbr\u003e\n5) An additional \"forest stand age\" layer for both the historical period (1992-2005) and the projected period (2006-2100). These data mark age in years since last land-use change or disturbance for forest pixels.\u003cbr\u003e\n\u003cbr\u003e\nData are provided here for 1) the historical 1992 to 2005 period, and 2) for each of the four scenarios from 2006 to 2100. 10 .zip files are available for download, 5 representing land-use and land-cover maps for both the historical period and the four future scenarios, and 5 representing forest stand age. Each zip file contains GeoTIFF files with annual maps for the given timeframe. The metadata associated with this data release provides a key for identifying file names associated with each of the .zip files, as well as definitions for the 17 land-cover classes.\u003cbr\u003e\n\u003cbr\u003e\nA further description of these data can be found at: Sohl, T.L., Sayler, K.L., Bouchard, M.A., Reker, R.R., Freisz, A.M., Bennett, S.L., Sleeter, B.M., Sleeter, R.R., Wilson, T., Soulard, C., Knuppe, M., and Van Hofwegen, T, 2014, Spatially explicit modeling of 1992 to 2100 land cover and forest stand age for the conterminous United States.\u0026nbsp; Ecological Applications 24(5):1115-1136. \u003ca href=\"https://doi.org/10.1890/13-1245.1\"\u003ehttps://doi.org/10.1890/13-1245.1\u003c/a\u003e\u003cbr\u003e\n\u003cbr\u003e\n\u0026nbsp;","doi_url":"https://doi.org/10.5066/P95AK9HP","domain":["Land Cover"],"draft":false,"id":"d77d5c65-3ddc-4f7e-86a5-6e0ec10e2163","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5b96c2f9e4b0702d0e826f6d?f=__disk__af%2Fa4%2Fc8%2Fafa4c83a1a37ffeb7db9cc511cb214d3e9ff9974\u0026allowOpen=true"}],"name":"Conterminous United States Land Cover Projections - 1992 to 2100","permalink":"/catalog/datasets/d77d5c65-3ddc-4f7e-86a5-6e0ec10e2163/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"250 meter","temporal_coverage":"1992 - 2100","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Land-cover Classes","Map coordinates","CONUS_Landcover_Historical.zip","CONUS_Landcover_A1B.zip","CONUS_Landcover_A2.zip","CONUS_Landcover_B1.zip","CONUS_Landcover_B2.zip","CONUS_Forest_History_Historical.zip","CONUS_Forest_History_A1B.zip","CONUS_Forest_History_A2.zip","CONUS_Forest_History_B1.zip","CONUS_Forest_History_B2.zip"],"vars":"Land-cover Classes; Map coordinates; CONUS_Landcover_Historical.zip; CONUS_Landcover_A1B.zip; CONUS_Landcover_A2.zip; CONUS_Landcover_B1.zip; CONUS_Landcover_B2.zip; CONUS_Forest_History_Historical.zip; CONUS_Forest_History_A1B.zip; CONUS_Forest_History_A2.zip; CONUS_Forest_History_B1.zip; CONUS_Forest_History_B2.zip","weight":1},{"access":[{"file_format":"GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/537a6a10e4b0efa8af081547"}],"access_details":null,"bbox":{"east":-63.21,"north":50.741871378,"south":17.1,"west":-161.31},"citation":"Viger, R.J., 2014, Geospatial Fabric Attribute Tables for PRMS Landcover Parameters based on NLCD2001 (Preliminary), U.S. Geological Survey; https://doi.org/10.5066/F7N58JD8","creator":[{"creator_email":"rviger@usgs.gov","creator_name":"Roland Viger"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset contains a set of attributes describing the \"nhru\" GIS features (Hydrologic Response Units)in the Geospatial Fabric Features dataset(http://dx.doi.org/doi:10.5066/F7542KMD) that have been developed in support of the USGS PRMS watershed model. These tables are organized according to Geospatial Fabric Region; see the thumbnail of the Geospatial Fabric Features Regions (https://www.sciencebase.gov/catalog/item/535edb4ae4b08e65d60fc837). Each table contains a key field, \"hru_id\", that can be used to relate to the nhru feature class in the Geospatial Fabric Feature dataset for the corresponding Region. The methodologies used to derive the individual attributes can be located in the Appendix of the GIS Weasel Users Manual by the name of the attribute, which is the same as the name of the corresponding PRMS parameter, in (Viger and Leavesley, 2007). The metadata for each table within the current container identifies any ancillary datasets used to produce the table fields. For nhru instances that are partially or entirely beyond the borders of the United States, supporting GIS data was generally lacking. Where the value for a field could not be determined, values derived at the border were spatially extended to these areas to support derivation of a value. Users may want to review and modify the field values for these HRUs. Viger, R.J., and Leavesley, G.H., 2007, The GIS Weasel user's manual: U.S. Geological Survey Techniques and Methods, book 6, chap. B4, 201 p.","doi_url":"https://doi.org/10.5066/F7N58JD8","domain":["Hydrology"],"draft":false,"id":"d9d6645d-9617-436f-ba97-8956974693da","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/537a6a10e4b0efa8af081547?f=__disk__5b%2Fe5%2Fd0%2F5be5d0b5e5b22ce3fe9ed5246475cb1ae906433d\u0026transform=1\u0026allowOpen=true"}],"name":"NLCD data for the Geospatial Fabric v1.0","permalink":"/catalog/datasets/d9d6645d-9617-436f-ba97-8956974693da/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; HI; PR","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Region-specific identifier for HRU feature. This field is used to relate to Geospatial Fabric Features for the region","Fraction of the feature area that is underlain by the land cover data layer used to derive the soil type parameters","Land cover type designation","The percentage of the land surface within the feature that is shaded by vegetation when illuminated from directly above","The percentage of the land surface within the feature that is shaded by vegetation when illuminated from directly above","Radiation transmission coefficient","Canopy interception of summer precipitation","Canopy interception of winter precipitation","Canopy interception of snow","Percentage of feature area that is covered by impervious surfaces"],"vars":"Region-specific identifier for HRU feature. This field is used to relate to Geospatial Fabric Features for the region; Fraction of the feature area that is underlain by the land cover data layer used to derive the soil type parameters; Land cover type designation; The percentage of the land surface within the feature that is shaded by vegetation when illuminated from directly above; The percentage of the land surface within the feature that is shaded by vegetation when illuminated from directly above; Radiation transmission coefficient; Canopy interception of summer precipitation; Canopy interception of winter precipitation; Canopy interception of snow; Percentage of feature area that is covered by impervious surfaces","weight":1},{"access":[{"file_format":"TIF","name":"usgs.gov","url":"https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-collection-2-level-3-fractional-snow-covered-area#overview"}],"access_details":null,"bbox":{"east":-56,"north":72,"south":24,"west":-180},"citation":"Earth Resources Observation and Science (EROS) Center. (2022). Landsat Level-3 Fractional Snow Covered Area, Collection 2 [dataset]: U.S. Geological Survey. https://doi.org/10.5066/P97ALZ2X","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Subpixel snow covered area, grain size, and albedo from Landsat. Persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. ","doi_url":"https://doi.org/10.5066/P97ALZ2X","domain":["Climate"],"draft":false,"id":"db4bf45a-5500-44fe-895e-f8e9ac131417","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.usgs.gov/landsat-missions/landsat-collection-2-level-3-fractional-snow-covered-area-science-product"}],"name":"USGS EROS Archive - Landsat Collection 2 Level-3 Fractional Snow Covered Area (fSCA) Science Product","permalink":"/catalog/datasets/db4bf45a-5500-44fe-895e-f8e9ac131417/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Western and northern CONUS; AK","spatial_resolution":"30 meter","temporal_coverage":"1984 - Present","temporal_frequency":"8 days","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Fractional snow covered area"],"vars":"Fractional snow covered area","weight":1},{"access":[{"file_format":"CSV","name":"cdmo.baruch.sc.edu","url":"https://cdmo.baruch.sc.edu/get/landing.cfm"}],"access_details":null,"bbox":{"east":-53,"north":72,"south":14,"west":-172},"citation":"National Estuarine Research Reserve System (NERRS), YYYY, System-wide Monitoring Program. Data accessed from the NOAA NERRS Centralized Data Management Office website: NOAA, accessed [YYYY-MM-DD] at www.nerrsdata.org","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Site-based monitoring data provide standardized, quantitative measures to determine how conditions are changing in the short and long term. Three major components are focused on (1) abiotic indicators of water quality and weather, (2) biological monitoring, and (3) watershed, habitat, and land use mapping. Shapefiles of each reserve’s management boundary and watersheds of interest are provided.\u003cbr\u003eAbiotic parameters collected include nutrients, temperature, salinity, pH, dissolved oxygen, relative humidity, barometric pressure, wind speed/direction, and precipitation. Biological monitoring includes measures of biodiversity, habitat, and population characteristics. Watershed and land use classifications provide information on types of land use by humans and changes in land cover associated with each reserve.\u003cbr\u003eThe National Estuarine Research Reserve System is a network of 30 sites protected for long-term research, ecosystem monitoring, education, and coastal stewardship.\u003cbr\u003eThe GIS Application provides access to NERRS reserve boundaries, related watershed boundaries, and high resolution reserve habitat maps. These products are available as either KML files for us in Google Earth (GE) or shapefiles (SH) for use with ESRI or other GIS software packages. NERR System-Wide Monitoring Program station locations are also available in KML file format.","doi_url":null,"domain":["Climate","Land Cover","Water Quality","Infrastructure"],"draft":false,"id":"dc191d30-6902-4745-b277-98ec87c5e06d","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://coast.noaa.gov/digitalcoast/data/nerr.html"}],"name":"National Estuarine Research Reserve System (NERRS)","permalink":"/catalog/datasets/dc191d30-6902-4745-b277-98ec87c5e06d/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NOAA","spatial_extent":"CONUS; AK; HI; PR","spatial_resolution":"NA","temporal_coverage":"2025","temporal_frequency":"15 minutes","update_detail":"append","update_frequency":"15 minutes","update_type":"Dynamic","variables":["Water-quality monitoring data","Vegetation monitoring data","Nutrient monitoring data","Meteorological monitoring data","Habitat maps","SWMP boundaries"],"vars":"Water-quality monitoring data; Vegetation monitoring data; Nutrient monitoring data; Meteorological monitoring data; Habitat maps; SWMP boundaries","weight":1},{"access":[{"file_format":"CSV; GDB; GPKG","name":"portal.edirepository.org","url":"https://portal.edirepository.org/nis/mapbrowse?packageid=edi.1136.3"}],"access_details":null,"bbox":{"east":-56,"north":51,"south":24,"west":-127},"citation":"Smith, N.J., Webster, K.E., Rodriguez, L.K., Cheruvelil, K.S. and Soranno, P.A., 2022, LAGOS-US GEO v1.0: Data module of lake geospatial ecological context at multiple spatial and temporal scales in the conterminous U.S. ver 3. Environmental Data Initiative, Acessed [YYYY-MM-DD], https://doi.org/10.6073/pasta/0e443bd43d7e24c2b6abc7af54ca424a","creator":[{"creator_email":"nicole.j.smith@gmail.com","creator_name":"Nicole Smith"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The LAGOS-US GEO data package is one of the core data modules of LAGOS-US, an extensible research-ready platform designed to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). The GEO module contains data on the geospatial and temporal ecological setting (e.g., land use, terrain, soils, climate, hydrology, atmospheric deposition, and human influence) quantified at multiple spatial divisions (e.g., equidistant buffers around lakes, watersheds, hydrologic basins, political boundaries, and ecoregions) relevant to the LAGOS-US lake population defined in the LAGOS-US LOCUS module. The database design that supports the LAGOS-US research platform was created based on several important design features: lakes are the fundamental unit of consideration, all lakes in the spatial extent above the minimum size must be represented, and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other two core data modules that are part of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds) and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.","doi_url":null,"domain":["Hydrology","Ecology","Soils"],"draft":false,"id":"dd11eb43-fca7-4038-92c3-2a4dbd0e2705","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://portal.edirepository.org/nis/metadataviewer?packageid=edi.1136.2\u0026contentType=application/xml"},{"name":"Documentation","url":"https://portal.edirepository.org/nis/metadataviewer?packageid=edi.1136.2"}],"name":"LAGOS-US GEO v1.0: Data module of lake geospatial ecological context at multiple spatial and temporal scales in the conterminous U.S.","permalink":"/catalog/datasets/dd11eb43-fca7-4038-92c3-2a4dbd0e2705/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"LAGOS","spatial_extent":"CONUS","spatial_resolution":"1:24,000","temporal_coverage":"2016 - 2022","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Land use","Soils","Lithology","Temperature","Precipitaion","Human disturbances","Atmospheric deposition","Runoff","Baseflow index","Recharge","Lake density","Stream density"],"vars":"Land use; Soils; Lithology; Temperature; Precipitaion; Human disturbances; Atmospheric deposition; Runoff; Baseflow index; Recharge; Lake density; Stream density","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/650b31f8d34e823a02735c0b"}],"access_details":null,"bbox":{"east":179.77,"north":71.35,"south":18.92,"west":-178.21},"citation":"Cherry, M.L., Hecht, J.S., and Johnson, Z.C., 2024, Long-term monotonic trends in annual groundwater metrics in the United States through 2020: U.S. Geologic Survey data release, https://doi.org/10.5066/P9ZACZ6H.","creator":[],"creator_project":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"date_created":"5/7/2024","date_updated":"6/3/2026","description":"The U.S. Geological Survey (USGS) Water Resources Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand by delivering routine assessments of water supply and demand. A key part of these national assessments is identifying long-term trends in water availability, including groundwater and surface water quantity, quality, and use.  This data release contains Mann-Kendall monotonic trend analyses for annual groundwater metrics at 39,964 wells located in the conterminous United States, Alaska, Hawaii, and Puerto Rico.\u003cbr\u003eThe groundwater metrics include annual mean, maximum, and minimum water level and the timing of the annual maximum and minimum groundwater level. These metrics are computed from groundwater water levels from publicly available data from the National Water Information System (NWIS) and the National Groundwater Monitoring Network (NGWMN). Trend analyses are computed using annual groundwater metrics through the water year, which is defined as the 12-month period October 1, for any given year through September 30 of the following year and named for the year in which it ends  (for example, October 2019 through September 2020 is water year 2020). Trends at each well are available for up to three different periods: i) the longest possible period that meets completeness criteria at each well, (ii) 1981-2020, (iii) 2001-2020.\u003cbr\u003eAnnual mean, maximum, and minimum water-level metrics for wells screened in unconfined aquifers were determined only when a well's water-level time series was at least 70 percent complete.  Additionally, each of these time series must have at least 70 percent complete records in the first and last decade.  All longest possible period time series for wells in unconfined aquifer must be at least 10 years long and have annual metric values calculated for at least 70% of the years of the record.  Annual mean, maximum, and minimum water-level metrics for wells screened in confined aquifers were determined only when a well's water-level time series was at least 50 percent complete.  Additionally, each of these time series must have at least 50 percent complete records in the first and last decade.  All longest possible period time series for wells in confined aquifer must be at least 10 years long and have annual metric values calculated for at least 50% of the years in the last 10 years of the record.\u003cbr\u003eCaution must be exercised when utilizing monotonic trend analyses conducted over periods of up to several decades (and in some places longer ones) due to the potential for confounding deterministic gradual trends with multi-decadal climatic fluctuations.\u003cbr\u003eThis data release contains four input files:\u003cbr\u003e1. NGWMN_gwl_meta.csv, the metadata from the National Groundwater Monitoring Network\u003cbr\u003e2. NGWMN_gwl_data.csv, the groundwater water level data from the National Groundwater Monitoring Network\u003cbr\u003e3. NWIS_gwl_meta.csv, the metadata from the National Water Information System\u003cbr\u003e4. NWIS_gwl_data.csv, the groundwater water level data from the National Water Information System\u003cbr\u003etwo output files:\u003cbr\u003e1. GW_trendsout.csv, the groundwater water level trend data from both the National Groundwater Monitoring Network and the National Water Information System\u003cbr\u003e 2. GW_conf_bands_out.csv, the confidence bands associated with the groundwater water level trend data from both the National Monitoring Network and the National Water Information System\u003cbr\u003eA .zip file containing all of the code used to compute these trends along with a README file with information on using the code","doi_url":"https://doi.org/10.5066/P9ZACZ6H","domain":["Hydrology","Water Quality"],"draft":false,"id":"dd5ca48e-50ed-43ff-8a03-6d42a85f6183","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/650b31f8d34e823a02735c0b?f=__disk__bb%2Ff7%2F3b%2Fbbf73bf12272c3ecbe06a527e9b3eb88d9e1c1c0\u0026allowOpen=true"}],"name":"Long-term monotonic trends in annual groundwater metrics in the United States through 2020","permalink":"/catalog/datasets/dd5ca48e-50ed-43ff-8a03-6d42a85f6183/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"unknown","temporal_coverage":"1851 - 2023","temporal_frequency":"varies","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["site_id","trend_period_start_yr","trend_period_end_yr","Q_med","tau","pval","trend_like","pval_mod_autocorr","trend_like_mod_autocorr","int","slope","slope_nomiss","decadal_change","decadal_pct_change","slope_ci_low","slope_ci_high","pbias","MAD","RMSD","int_ln","slope_ln","slope_nomiss_ln","decadal_pct_change_ln","slope_ci_low_ln","slope_ci_high_ln","pbias_ln","MAD_ln","RMSD_ln","decadal_change_ln","test_id","metric_id","year_type_id","aquifer_type_id","POI_type_id","trend_type_id","year_value","line_fit","line_ci_high","line_ci_low","AGENCY_CD","AGENCY_NM","DEC_LAT_VA","DEC_LONG_V","WELL_DEPTH","WELL_DEPT1","NAT_AQUIFE","NAT_AQFR_D","AQFR_CHAR","MY_SITEID","site_no","dec_long_va","well_depth_va","nat_aqfr_cd","aqfr_type_cd","site_tp_cd","lev_dt","lev_va","sl_lev_va","sl_datum_cd","lev_status_cd","lev_agency_cd","lev_dt_acy_cd","lev_acy_cd","lev_src_cd","lev_meth_cd","lev_age_cd","parameter_cd","lev_dateTime","lev_tz_cd","flag_metadata_pullDate","date","value","uom"],"vars":"site_id; trend_period_start_yr; trend_period_end_yr; Q_med; tau; pval; trend_like; pval_mod_autocorr; trend_like_mod_autocorr; int; slope; slope_nomiss; decadal_change; decadal_pct_change; slope_ci_low; slope_ci_high; pbias; MAD; RMSD; int_ln; slope_ln; slope_nomiss_ln; decadal_pct_change_ln; slope_ci_low_ln; slope_ci_high_ln; pbias_ln; MAD_ln; RMSD_ln; decadal_change_ln; test_id; metric_id; year_type_id; aquifer_type_id; POI_type_id; trend_type_id; year_value; line_fit; line_ci_high; line_ci_low; AGENCY_CD; AGENCY_NM; DEC_LAT_VA; DEC_LONG_V; WELL_DEPTH; WELL_DEPT1; NAT_AQUIFE; NAT_AQFR_D; AQFR_CHAR; MY_SITEID; site_no; dec_long_va; well_depth_va; nat_aqfr_cd; aqfr_type_cd; site_tp_cd; lev_dt; lev_va; sl_lev_va; sl_datum_cd; lev_status_cd; lev_agency_cd; lev_dt_acy_cd; lev_acy_cd; lev_src_cd; lev_meth_cd; lev_age_cd; parameter_cd; lev_dateTime; lev_tz_cd; flag_metadata_pullDate; date; value; uom","weight":1},{"access":[{"file_format":"HDF","name":"earthdata.nasa.gov","url":"https://www.earthdata.nasa.gov/data/catalog/lpcloud-mod13q1-061"}],"access_details":"An Earthdata Login is required before users can download data or use selected tools that comprise NASA's Earth Observing System Data and Information System (EOSDIS).","bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Didan, K., 2021, MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061 [dataset], NASA Land Processes DAAC, accessed [YYYY-MM-DD] at https://doi.org/10.5067/MODIS/MOD13Q1.061","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.\u003cbr\u003eAlong with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.","doi_url":"https://doi.org/10.5067/MODIS/MOD13Q1.061","domain":["Land Cover"],"draft":false,"id":"dd93cc96-a562-4966-9033-8b99be306eec","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://lpdaac.usgs.gov/documents/621/MOD13_User_Guide_V61.pdf"}],"name":"MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061","permalink":"/catalog/datasets/dd93cc96-a562-4966-9033-8b99be306eec/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASA","spatial_extent":"Global","spatial_resolution":"250 meter","temporal_coverage":"2000 - Present","temporal_frequency":"16 day","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["EVI/NDVI"],"vars":"EVI/NDVI","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5e91dee782ce172707f02cdd"}],"access_details":null,"bbox":{"east":-64.039306640625,"north":70.4717168040628,"south":17.2404979312374,"west":-165.640869140625},"citation":"U.S. Geological Survey, USDA Forest Service, Nelson, K., 2021, Monitoring Trends in Burn Severity Thematic Burn Severity Mosaic: U.S. Geological Survey data release, https://doi.org/10.5066/P9NETC0T.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in the United States for a specific year. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available or fires were not discernable from available imagery.","doi_url":"https://doi.org/10.5066/P9NETC0T","domain":["Land Cover"],"draft":false,"id":"dd97ba1e-c6b1-44d6-95fa-e8ae8d0f8706","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5e91dee782ce172707f02cdd?f=__disk__dd%2F89%2F39%2Fdd8939ace4e4b633cae965df2f941d0bcf29bd82\u0026allowOpen=true"}],"name":"Monitoring Trends in Burn Severity Thematic Burn Severity Mosaic from 1984 to present (ver. 3.0, January 2023)","permalink":"/catalog/datasets/dd97ba1e-c6b1-44d6-95fa-e8ae8d0f8706/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS; USFS","spatial_extent":"CONUS; AK; HI; PR","spatial_resolution":"30 meter","temporal_coverage":"1984 - 2018","temporal_frequency":"annual","update_detail":"append","update_frequency":"annual","update_type":"Dynamic","variables":["unburned to low","low","moderate","high","and increased greenness (increased post-fire vegetation response)"],"vars":"unburned to low; low; moderate; high; and increased greenness (increased post-fire vegetation response)","weight":1},{"access":[{"file_format":"CSV; GPKG","name":"nid.sec.usace.army.mil","url":"https://nid.sec.usace.army.mil/#/downloads"}],"access_details":null,"bbox":{"east":144,"north":72,"south":13,"west":-180},"citation":null,"creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The National Inventory of Dams (NID) documents all known dams in the U.S. and its territories that meet certain criteria. It is designed to provide a variety of users the ability to search for specific data about dams in the U.S. and serves as a resource to support awareness of dams and actions to prepare for a dam-related emergency. USACE is responsible for maintaining the NID and works in close collaboration with federal dam regulating agencies, including the Federal Emergency Management Agency (FEMA) and federal and state dam regulating agencies, to obtain accurate and complete information about dams in the database. The database contains information about a dam's location, type, size, purpose, uses and benefits, date of last inspection, other structural and geographical information, and much more (there are more than 70 data fields for each dams). The NID is also used to assist federal, state, and local agencies develop dam safety policies.","doi_url":null,"domain":["Infrastructure"],"draft":false,"id":"def72df7-debc-4d61-9120-cb65fbad7eac","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.fema.gov/emergency-managers/risk-management/dam-safety/national-inventory-dams"}],"name":"National Inventory of Dams (NID)","permalink":"/catalog/datasets/def72df7-debc-4d61-9120-cb65fbad7eac/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USACE","spatial_extent":"CONUS; AK; HI; PR; VI; GU","spatial_resolution":"NA","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Dam locations"],"vars":"Dam locations","weight":1},{"access":[{"file_format":"TIF","name":"cec.org","url":"http://www.cec.org/north-american-environmental-atlas/land-cover-2005-modis-250m/"}],"access_details":null,"bbox":{"east":-56,"north":72,"south":23,"west":-178},"citation":"Commission for Environmental Cooperation (CEC), 2013, 2005 Land Cover of North America at 250 meters, North American Land Change Monitoring System, Canada Centre for Remote Sensing (CCRS), U.S. Geological Survey (USGS), Comision Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO), Comision Nacional Forestal (CONAFOR), Instituto Nacional de Estadistica y Geografia (INEGI), Ed. 3.0, Raster digital data [250-m], accessed [YYYY-MM-DD], http://www.cec.org/north-american-environmental-atlas/land-cover-2005-modis-250m/","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data set replaces the 2010 edition (Edition 1.0) of the 2005 Land Cover of North America.  Following the release of the first 2005 land cover data, several errors were identified in the data, including both errors in labeling and misinterpretation of thematic classes. To correct the labeling errors, each country focused on its national territory and corrected the errors which it considered most critical or misleading. For the continental data sets (including surrounding water fringe) 17440830 pixels (4.33% of the area) changed in the update. The following national counts exclude the water fringe: Canada, 10223412 pixels changed (6.44%); Mexico, 141142 pixels changed (0.45%), and U.S., 6878656 pixels changed (4.54%).\u003cbr\u003eThe countries worked together to produce a definitive list of land cover classifications for the 2005 data; this document is available for download from the same site as the data and is entitled: North American Land Cover Classifications (2005).\u003cbr\u003eVersion 1 of the 2005 North American Land Cover data set was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between the Canada Centre for Remote Sensing, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia), National Commission for the Knowledge and Use of the Biodiversity (Comision Nacional Para el Conocimiento y Uso de la Biodiversidad) and the National Forestry Commission of Mexico (Comision Nacional Forestal).  The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries.\u003cbr\u003eThe general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country's specific requirements. The data set of 2005 Land Cover of North America at a resolution of 250 meters is the first step toward this goal. The initial data set used to generate land cover information over North America was produced by the Canada Centre for Remote Sensing from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS/Terra).  All seven land spectral bands were processed from Level 1 granules into top-of-atmosphere reflectance covering North America at a 250-meter spatial and 10-day temporal resolution.\u003cbr\u003eIn order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by INEGI, CONABIO, and CONAFOR; and for the United States by the USGS.  Each country used specific training data and land cover mapping methodologies to create national data sets. This North America data set was produced by combining the national land cover data sets.\u003cbr\u003eThis version 3.0 of the North America Land Cover map at 250 meters spatial resolution has removed the water buffer along the coastline of North America, this will ensure consistency in the statistics calculations of class 18 (water) without incorporating ocean water.","doi_url":null,"domain":["Land Cover"],"draft":false,"id":"e0401108-61c2-4bf9-9aac-d76dbdd5a91d","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"http://www.cec.org/north-american-environmental-atlas/land-cover-2005-modis-250m/"}],"name":"Land cover 2005 (MODIS)","permalink":"/catalog/datasets/e0401108-61c2-4bf9-9aac-d76dbdd5a91d/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"CEC","spatial_extent":"North America","spatial_resolution":"250 meter","temporal_coverage":"2005","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Temperate or sub-polar needleleaf forest","Sub-polar taiga needleleaf forest","Tropical or sub-tropical broadleaf evergreen forest","Tropical or sub-tropical broadleaf deciduous forest","Temperate or sub-polar broadleaf deciduous forest","Mixed forest","Tropical or sub-tropical shrubland","Temperate or sub-polar shrubland","Tropical or sub-tropical grassland","Temperate or sub-polar grassland","Sub-polar or polar shrubland-lichen-moss","Sub-polar or polar grassland-lichen-moss","Sub-polar or polar barren-lichen-moss","Wetland","Cropland","Barren land","Urban and built-up","Water","Snow and ice"],"vars":"Temperate or sub-polar needleleaf forest; Sub-polar taiga needleleaf forest; Tropical or sub-tropical broadleaf evergreen forest; Tropical or sub-tropical broadleaf deciduous forest; Temperate or sub-polar broadleaf deciduous forest; Mixed forest; Tropical or sub-tropical shrubland;  Temperate or sub-polar shrubland; Tropical or sub-tropical grassland;  Temperate or sub-polar grassland; Sub-polar or polar shrubland-lichen-moss; Sub-polar or polar grassland-lichen-moss; Sub-polar or polar barren-lichen-moss; Wetland; Cropland; Barren land; Urban and built-up; Water; Snow and ice","weight":1},{"access":[{"file_format":"NC","name":"ncei.noaa.gov","url":"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332"}],"access_details":null,"bbox":{"east":-56,"north":72,"south":23,"west":-180},"citation":"Vose, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Jr., Fenimore, C., Gleason, K., and Arndt, D., 2014, Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions, Journal of Applied Meteorology and Climatology, 53(5), pp 1232-1251, https://doi.org/10.1175/JAMC-D-13-0248.1","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. On an annual basis, approximately one year of \"final\" nClimGrid will be submitted to replace the initially supplied \"preliminary\" data for the same time period. Users should be sure to ascertain which level of data is required for their research.","doi_url":null,"domain":["Climate"],"draft":false,"id":"e1fb698d-eb57-4663-bc47-26c9ee071600","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332;view=iso"},{"name":"Documentation","url":"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332"}],"name":"NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid)","permalink":"/catalog/datasets/e1fb698d-eb57-4663-bc47-26c9ee071600/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NOAA","spatial_extent":"Continental United States","spatial_resolution":"5 kilometer","temporal_coverage":"1895 - Present","temporal_frequency":"monthly","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Temperature, maximum","Temperature, minimum","Temperature, average","Precipitation"],"vars":"Temperature, maximum; Temperature, minimum; Temperature, average; Precipitation","weight":1},{"access":[{"file_format":"GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/631405ded34e36012efa3470"}],"access_details":null,"bbox":{"east":-65.3748,"north":51.5121,"south":23.2444,"west":-127.8572},"citation":"Moore, R., Belitz, K., Arnold, T.L., Sharpe, J.B., and Starn, J.J., 2019, National Multi Order Hydrologic Position (MOHP - High Resolution) Predictor Data for Groundwater and Groundwater-Quality Modeling: U.S. Geological Survey data release, https://doi.org/10.5066/P9ST73KV.","creator":[],"creator_project":[],"date_created":"5/5/2024","date_updated":"6/3/2026","description":"Multi Order Hydrologic Position (MOHP) raster datasets: Distance from Stream to Divide (DSD) and Lateral Position (LP) have been produced nationally for the 48 contiguous United States at a 30-meter resolution for stream orders 1 through 9. These data are available for testing as predictor variables for various regional and national groundwater-flow and groundwater-quality statistical models. The concept behind MOHP is that for any given point on the earth’s surface there is the potential for longer and longer groundwater flow paths as one goes deeper and deeper beneath the land surface. These increasing depths correspond to increasing stream orders. Or in other words, with increasing depth these paths of groundwater flow travel further from divides to point of discharge which are to increasingly larger streams of higher stream order. DSD – Raster – Distance from Stream to Divide (DSD) rasters have cell values equal to the sum of the shortest distance to the stream or associated waterbody plus the shortest distance to the matching Thiessen divide. There are 9 rasters for streams orders 1 through 9. Units are in meters. LP – Raster -- the lateral position (LP) raster has cell values equal to the shortest distance to the stream or associated waterbody divided by the DSD. There are 9 rasters for streams orders 1 through 9. Combined, these two factors, DSD and LP, provide a measure or description of potential distance of groundwater flow to any location along the groundwater flow path.","doi_url":"https://doi.org/10.5066/P9ST73KV","domain":["Hydrogeology","Hydrology","Stream Characteristics"],"draft":false,"id":"e2be47f1-04cb-4f0b-91e5-9a9d58365470","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/631405ded34e36012efa3470?f=__disk__b9%2F7c%2F73%2Fb97c73898cdef2a79a1bac7498049ce7701b9e5b"}],"name":"National Multi Order Hydrologic Position (MOHP - High Resolution) Predictor Data for Groundwater and Groundwater-Quality Modeling","permalink":"/catalog/datasets/e2be47f1-04cb-4f0b-91e5-9a9d58365470/","project_use_history":[{"id":"DJ50U93","name":"NEHF: National Extent Hydrogeologic Framework"}],"project_using":[{"id":"DJ50U93","name":"NEHF: National Extent Hydrogeologic Framework"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"2018","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["COMID","REACHCODE","FROMMEAS","TOMEAS","BURNLENKM","InRPU","GridCode","Catchment","Burn","StreamCalc","Hydroseq","LevelPathI","Pathlength","TerminalPa","DnLevel","UpLevelPat","UpHydroseq","DnLevelPat","DnMinorHyd","DnDrainCou","DnHydroseq","NEW_ORDER","LEV_ORD","HYDSEQSTRM","MIN_HYDORD","DNLEV_ORD","Country","LevelPathI","AREASQKM","FTYPE","FCODE","ONOFFNET"],"vars":"COMID; REACHCODE; FROMMEAS; TOMEAS; BURNLENKM; InRPU; GridCode; Catchment; Burn; StreamCalc; Hydroseq; LevelPathI; Pathlength; TerminalPa; DnLevel; UpLevelPat; UpHydroseq; DnLevelPat; DnMinorHyd; DnDrainCou; DnHydroseq; NEW_ORDER; LEV_ORD; HYDSEQSTRM; MIN_HYDORD; DNLEV_ORD; Country; LevelPathI; AREASQKM; FTYPE; FCODE; ONOFFNET","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6515cecbd34e469cabfcdce6"}],"access_details":null,"bbox":{"east":179.77,"north":71.35,"south":18.92,"west":-178.21},"citation":"Hecht, J.S., Johnson, Z.C., and Dunne, K.A. 2024. Long-term monotonic trends in annual and monthly streamflow metrics at streamgages in the United States (ver. 2.0, October 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P9VBR38I.","creator":[],"creator_project":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"date_created":"5/8/2024","date_updated":"6/3/2026","description":"The U.S. Geological Survey (USGS) Water Resources Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand and an understanding of the natural and human factors affecting the balance between supply and demand. A key part of these national assessments is identifying long-term trends in water availability, including groundwater and surface water quantity, quality, and use.\u003cbr\u003eThis data release contains Mann-Kendall monotonic trend analyses for 18 observed annual and monthly streamflow metrics at 6,347 U.S. Geological Survey streamgages located in the conterminous United States, Alaska, Hawaii, and Puerto Rico. Streamflow metrics include annual mean flow, maximum 1-day and 7-day flows, minimum 7-day and 30-day flows, and the date of the center of volume (the date on which 50% of the annual flow has passed by a gage), along with the mean flow for each month of the year. Annual streamflow metrics are computed from mean daily discharge records at U.S. Geological Survey streamgages that are publicly available from the National Water Information System (NWIS). Trend analyses are computed using annual streamflow metrics computed through climate year 2022 (April 2022- March 2023) for low-flow metrics and water year 2022 (October 2021 - September 2022) for all other metrics. Trends at each site are available for up to four different periods: (i) the longest possible period that meets completeness criteria at each site, (ii) 1980-2020, (iii) 1990-2020, (iv) 2000-2020. Annual metric time series analyzed for trends must have 80 percent complete records during fixed periods. In addition, each of these time series must have 80 percent complete records during their first and last decades. All longest possible period time series must be at least 10 years long and have annual metric values for at least 80% of the years running from 2013 to 2022.\u003cbr\u003eThis data release provides the following five CSV output files along with a model archive:\u003cbr\u003e(1) streamflow_trend_results.csv - contains test results of all trend analyses with each row representing one unique combination of (i) NWIS streamgage identifiers, (ii) metric (computed using Oct 1 - Sep 30 water years except for low-flow metrics computed using climate years (Apr 1 - Mar 31), (iii) trend periods of interest (longest possible period through 2022, 1980-2020, 1990-2020, 2000-2020) and (iv) records containing either the full trend period or only a portion of the trend period following substantial increases in  cumulative upstream reservoir storage capacity.  This is an output from the final process step (#5) of the workflow.\u003cbr\u003e(2) streamflow_trend_trajectories_with_confidence_bands.csv - contains annual trend trajectories estimated using Theil-Sen regression, which estimates the median of the probability distribution of a metric for a given year, along with 90 percent confidence intervals (5th and 95h percentile values). This is an output from the final process step (#5) of the workflow.\u003cbr\u003e(3) streamflow_trend_screening_all_steps.csv - contains the screening results of all 7,873 streamgages initially considered as candidate sites for trend analysis and identifies the screens that prevented some sites from being included in the Mann-Kendall trend analysis.\u003cbr\u003e(4) all_site_year_metrics.csv - contains annual time series values of streamflow metrics computed from mean daily discharge data at 7,873 candidate sites. This is an output of Process Step 1 in the workflow.\u003cbr\u003e(5) all_site_year_filters.csv - contains information about the completeness and quality of daily mean discharge at each streamgage during each year (water year, climate year, and calendar year). This is also an output of Process Step 1 in the workflow and is combined with all_site_year_metrics.csv in Process Step 2.\u003cbr\u003eIn addition, a .zip file contains a model archive for reproducing the trend results using R 4.3.2 statistical software. See the README file contained in the model archive for more information.\u003cbr\u003eCaution must be exercised when utilizing monotonic trend analyses conducted over periods of up to several decades (and in some places longer ones) due to the potential for confounding deterministic gradual trends with multi-decadal climatic fluctuations. In addition, trend results are available for post-reservoir construction periods within the four trend periods described above to avoid including abrupt changes arising from the construction of larger reservoirs in periods for which gradual monotonic trends are computed. Other abrupt changes, such as changes to water withdrawals and wastewater return flows, or episodic disturbances with multi-year recovery periods, such as wildfires, are not evaluated. Sites with pronounced abrupt changes or other non-monotonic trajectories of change may require more sophisticated trend analyses than those presented in this data release.","doi_url":"https://doi.org/10.5066/P9VBR38I","domain":["Hydrology","Water Quality"],"draft":false,"id":"e39a9b83-9876-4ab0-bf3b-ee61ab9f793b","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6515cecbd34e469cabfcdce6?f=__disk__3d%2F56%2F14%2F3d5614693150fe0c331f222a0704fb20aa2bcc31\u0026allowOpen=true"}],"name":"Long-term monotonic trends in annual and monthly streamflow metrics at streamgages in the United States (ver. 2.0, October 2024)","permalink":"/catalog/datasets/e39a9b83-9876-4ab0-bf3b-ee61ab9f793b/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"unknown","temporal_coverage":"1863 - 2023","temporal_frequency":"annual; monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["id","test_id","site_id","metric_id","year_type_id","poi_type_id","record_segment_type_id","rl_complete_criterion_id","trend_period_start_yr","trend_period_end_yr","log_transform","tau","null_hypothesis_rec","p_value","trend_lh","p_value_autocorr","trend_lh_autocorr","int","slope","slope_nomiss","decadal_change","decadal_percent_change","slope_ci_low","slope_ci_high","pbias","mad","rmsd","year_value","obs_value","line_fit","confidence_low","confidence_high","date","value","miss30_flag","miss3_consec_flag","neg_flow_flag","zero_flow_flag","month_sd_flag","pass_count","frac_est","pass_one_year_filter_flag","site_max_year_flag","data_in_poi_flag","zero_flow_flag_percent_year_flag","poi_max_year_flag","min_record_length_flag","fixed_period_flag","first_decade_flag","last_decade_flag","record_length_unfiltered","record_length_filtered","percent_filtered","percent_zero","all_flags_passed"],"vars":"id; test_id; site_id; metric_id; year_type_id; poi_type_id; record_segment_type_id; rl_complete_criterion_id; trend_period_start_yr; trend_period_end_yr; log_transform; tau; null_hypothesis_rec; p_value; trend_lh; p_value_autocorr; trend_lh_autocorr; int; slope; slope_nomiss; decadal_change; decadal_percent_change; slope_ci_low; slope_ci_high; pbias; mad; rmsd; year_value; obs_value; line_fit; confidence_low; confidence_high; date; value; miss30_flag; miss3_consec_flag; neg_flow_flag; zero_flow_flag; month_sd_flag; pass_count; frac_est; pass_one_year_filter_flag; site_max_year_flag; data_in_poi_flag; zero_flow_flag_percent_year_flag; poi_max_year_flag; min_record_length_flag; fixed_period_flag; first_decade_flag; last_decade_flag; record_length_unfiltered; record_length_filtered; percent_filtered; percent_zero; all_flags_passed","weight":1},{"access":[{"file_format":"CSV; SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/669999f2d34e9ac16e164e18"}],"access_details":null,"bbox":{"east":-130,"north":72,"south":54,"west":-170},"citation":"Bjerklie, D.M., Sturtevant, L.P., McCarthy, B.A., Dudley, R.W., Harlan, M.E., Mason, C.A., and Best, H.R., 2024, Estimated streamflow using satellite data for selected rivers in Alaska (updated 2024-12-17): U.S. Geological Survey data release, https://doi.org/10.5066/P18WWJHU.","creator":[],"creator_project":[{"id":"DJ33V0C","name":"Alaska SWOT Satellite Discharge"}],"date_created":"5/2/2025","date_updated":"6/3/2026","description":"This dataset provides estimated Remote Sensing Streamflow (RSQ) data at selected river reaches in Alaska. Reach-specific relations between satellite-derived water-surface elevation data and dynamic surface water extent data were used with a modified form of the Manning's streamflow equation to estimate streamflows. The streamflow estimates were used to create rating tables that define the relation between discharge and satellite-observed water-surface elevation. The streamflow estimates in the gage_data.csv files are derived from these rating tables, interpolated using a polynomial regression equation.","doi_url":"https://doi.org/10.5066/P18WWJHU","domain":["Hydrology","Stream Characteristics"],"draft":false,"id":"e55dbb4d-1e8d-489c-94fd-8d254c1f3ee1","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"Estimated Streamflow Using Satellite Data for Selected Rivers in Alaska","permalink":"/catalog/datasets/e55dbb4d-1e8d-489c-94fd-8d254c1f3ee1/","project_use_history":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"project_using":[{"id":"DJ30GUX","name":"Remote Sensing R\u0026D"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"AK","spatial_resolution":"unknown","temporal_coverage":"2009 - Present","temporal_frequency":"irregular","update_detail":"append","update_frequency":"irregular","update_type":"Dynamic","variables":["Date and time","Time zone","Unique identifier","Satellite altimeter source","Version system of the altimetry source data","Satellite mission","Version of SWOT","SWORD version","SWORD reach","RSQ gage site","USGS site number","Latitude","Longitude","USGS Gage Station","Water surface elevation","Elevation uncertainty","Estimated river width","Estimated river discharge","Satellite observed reach-averaged river width","Satellite observed reach-averaged river width uncertainty","Date of altimetry data download","Version of rating table","Ice observed by satellite","Flag for observation being outside the bounds of the rating table"],"vars":"Date and time; Time zone; Unique identifier; Satellite altimeter source; Version system of the altimetry source data; Satellite mission; Version of SWOT; SWORD version; SWORD reach; RSQ gage site; USGS site number; Latitude; Longitude; USGS Gage Station; Water surface elevation; Elevation uncertainty; Estimated river width; Estimated river discharge; Satellite observed reach-averaged river width; Satellite observed reach-averaged river width uncertainty; Date of altimetry data download; Version of rating table; Ice observed by satellite; Flag for observation being outside the bounds of the rating table","weight":1},{"access":[{"file_format":"TIF","name":"usgs.gov","url":"https://www.usgs.gov/landsat-missions/landsat-provisional-actual-evapotranspiration"}],"access_details":null,"bbox":{"east":-56,"north":51,"south":24,"west":-127},"citation":"Senay, G.B., 2018, Satellite psychrometric formulation of the operational Simplified Surface Energy Balance (SSEBop) Model for quantifying and mapping Evapotranspiration. Applied Engineering in Agriculture, 34(3), pp 555-566, https://doi.org/10.13031/aea.12614","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Landsat Provisional Actual Evapotranspiration (ETa) science product is generated by calculating the latent heat flux based on surface energy balance principles using a robust model and can be fundamental in the understanding of the spatiotemporal dynamics of water use over land surfaces. Full description  (including qualifiers) available at: https://www.usgs.gov/core-science-systems/nli/landsat/landsat-provisional-actual-evapotranspiration (accessed 2021-09-02).","doi_url":"https://doi.org/10.13031/aea.12614","domain":["Climate"],"draft":false,"id":"e563ca3f-d919-4b96-a163-758e427a51bc","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.usgs.gov/media/files/landsat-provisional-actual-evapotranspiration-product-guide"}],"name":"SSEBop (Landsat)","permalink":"/catalog/datasets/e563ca3f-d919-4b96-a163-758e427a51bc/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"1984 - Present","temporal_frequency":"8 days","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Evapotranspiration"],"vars":"Evapotranspiration","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6230edbad34ec9f19eeaf615"}],"access_details":null,"bbox":{"east":-90.6637,"north":46.4678,"south":46.1854,"west":-91.0586},"citation":"Fitzpatrick, F.A., Nelson, B.R., Magyera, K.H., Dantoin, E.D., Sterner, S.P., and Fredrick, R.A., 2022, Fluvial Erosion Hazard Rapid Geomorphic Assessment Data from the Marengo Watershed, Ashland County, Wisconsin: U.S. Geological Survey data release, https://doi.org/10.5066/P93AMGQR.","creator":[],"creator_project":[{"id":"DJ60TRJ","name":"NHGF: National Hydrologic Geospatial Fabric"}],"date_created":"4/19/2024","date_updated":"6/3/2026","description":"This dataset contains the results of 15 geomorphic assessments completed October 2020 as part of a pilot study evaluating the susceptibility of streams in the Marengo River watershed to fluvial erosion hazards. Included in the dataset are geomorphic and habitat field data, photos of assessment sites, and estimations of annual erosion for each site.\u003cbr\u003eAn extreme flood in 2016 caused widespread culvert blockages and road failures across northern Wisconsin, including extensive damage along steep tributaries and ravines in the Marengo River watershed. Along with the flooding, there were fluvial erosion hazards (FEH) associated with a large amount of erosion in headwater areas. Of special concern were FEHs associated with gullying, loss of wetland storage, and valley-side mass wasting. In 2020, a pilot study was begun to map and classify ephemeral and perennial streams and wetlands in terms of their susceptibility to fluvial erosion hazards. This study combines rapid geomorphic field assessments of river corridor erosion and coupled sediment and debris delivery with mapping geomorphic vulnerability zones in a Geographic Information System (GIS). The FEH assessment results will ultimately be used to identify and prioritize natural flood management projects related to stream and wetland restoration.","doi_url":"https://doi.org/10.5066/P93AMGQR","domain":["Stream Characteristics"],"draft":false,"id":"e63ecdec-1043-4c46-9f85-2db62d2e9cde","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https:/www.sciencebase.gov/catalog/file/get/6230edbad34ec9f19eeaf615?f=__disk__86%2Fc3%2F9d%2F86c39dc7aa35b9f498b58ab2589c55512c00e3eb\u0026allowOpen=true"}],"name":"Fluvial Erosion Hazard Rapid Geomorphic Assessment Data from the Marengo Watershed, Ashland County, Wisconsin","permalink":"/catalog/datasets/e63ecdec-1043-4c46-9f85-2db62d2e9cde/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Marengo River watershed Wisconsin","spatial_resolution":"150 meter","temporal_coverage":"2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["RGAID","SiteName","USLatitude","USLongitude","DSLatitude","DSLongitude","RGAType","Date","Time","Weather","AirTempF","RiverStage","RiverStageNote","FieldCrew","ReachLength","SiteDescription","WaterClarity","AdjLandUse1","AdjLandUse2","AdjLandUse3","AdjLandUse4","AdjLandUseNote","RipLandUse1","RipLandUse2","RipLandUse3","RipLandUseNote","ValleyStreamLoc","ValleyDeposit1","ValleyDeposit2","ValleyDeposit3","ValleyDeposit4","ValleyDepositNote","ValleyFailure","ValleyFailureLoc","ValleyFailureNote","ValleyType","ValleyTypeNote","FloodplainFeature1","FloodplainFeature2","FloodplainFeature3","FloodplainFeature4","FloodLines","FloodLinesHeight","FloodLineNote","FloodplainWidth","FloodplainWidthNote","FloodAttenuation","ChannelPlanform","ChannelPlanformNote","RiparianLivestock","GeneralNote","OverbankDeposit","OverbankDepositDepth_m","OverbankDepositNote","LeveeType","LeveeContinuity","LeveeBank","LeveeNote","BedControl","BedControlType","WidthControl","WidthControlType","StreamType","BaseflowPercWidth","BaseflowWidthNote","ChannelType1","ChannelType2","ChannelType3","ChannelTypeNote","FlowType1","FlowType2","FlowType3","BedPercSoftSed","BedPercSand","ChannelD50","ChannelD84","ChannelD16","MarginalBarCount","MarginalBarD50","MarginalBarCover","MarginalBarNote","PointBarCount","PointBarD50","PointBarCover","PointBarNote","MidchanBarCount","MidchanBarD50","MidchanBarCover","BFIndicatorQuality","EntrenchRatio","GeneralNotes","RGATransect","GeomorphicUnit","BankfullWidth","WettedWidth","WettedDepth","BankfullDepth","SoftSedLeft_cm","SoftSedMid_cm","SoftSedRight_cm","BedSubstrate","LBankHeight","RBankHeight","LBankSlope_deg","RBankSlope_deg","LBankCondition","RBankCondition","LBankPercVeg","RBankPercVeg","LBankSubstrate","RBankSubstrate","RGALocation","BankSide","ErosionHeight","ErosionLength","ErosionCategory","ErosionRate_m_yr","ErosionVolume_m3_yr","Conversion_kg_m3","AnnualErosion_mT_yr","ErosionExtent","ErosionMech","ErosionDesc","ErosionStrat","ErosionTexture","ReachErosion_mT_yr","ReachLength_m","Erosion_mT_km_yr","WildlifeSign1","WildlifeSign2","WildlifeSign3","WildlifeSign4","WildlifeSignNote","LivestockTramp","LivestockTrampNote","FilAlgae","FilAlgeaNote","AquaPlant","AquaPlantType","AquaPlantNote","WoodyDebris","WoodyDebrisNote","StreamShade_perc","LunkStructCount","LunkStructCondition","HabImprovement","LBProtection_perc","RBProtection_perc","Channelization_perc","ArtificialRiffle","BridgeCulvert","BridgeCulvertNotes","Indicator1","Indicator2","Indicator3","Indicator4","Indicator5","Indicator6","Indicator7","Indicator8","IndicatorNote","VerticalActivity1","VerticalActivity2","VerticalSeverity","VerticalNote","VerticalExtent","VerticalConfidence","LateralActivity1","LateralActivity2","LateralActivity3","LateralSeverity","LateralNotes","LateralExtent","LateralConfidence","SedTransMode1","SedTransMode2","SedTransMode3","SedTransMode4","SedTransMode5","SedTransMode6","SedTransMode7","SedTransModeNote","GeomorphProcess1","GeomorphProcess2","GeomorphProcess3","GeomorphProcess4","GeomorphProcess5","GeomorphProcess6","GeomorphProcess7","GeomorphProcess8","GeomorphProcessNote","GeomorphDisturb1","GeomorphDisturb2","GeomorphDisturb3","GeomorphDisturb4","GeomorphDisturbNote","InterpretiveNotes","BarLength","BarWidth","BarSubstrate","BarType","BarNote","RGATape_ft","FeatureHeight","FeatureLength","FeatureDescription","FeatureNotes","PhotoLocation","Camera","PhotoLabel","PhotoDescription"],"vars":"RGAID; SiteName; USLatitude; USLongitude; DSLatitude; DSLongitude; RGAType; Date; Time; Weather; AirTempF; RiverStage; RiverStageNote; FieldCrew; ReachLength; SiteDescription; WaterClarity; AdjLandUse1; AdjLandUse2; AdjLandUse3; AdjLandUse4; AdjLandUseNote; RipLandUse1; RipLandUse2; RipLandUse3; RipLandUseNote; ValleyStreamLoc; ValleyDeposit1; ValleyDeposit2; ValleyDeposit3; ValleyDeposit4; ValleyDepositNote; ValleyFailure; ValleyFailureLoc; ValleyFailureNote; ValleyType; ValleyTypeNote; FloodplainFeature1; FloodplainFeature2; FloodplainFeature3; FloodplainFeature4; FloodLines; FloodLinesHeight; FloodLineNote; FloodplainWidth; FloodplainWidthNote; FloodAttenuation; ChannelPlanform; ChannelPlanformNote; RiparianLivestock; GeneralNote; OverbankDeposit; OverbankDepositDepth_m; OverbankDepositNote; LeveeType; LeveeContinuity; LeveeBank; LeveeNote; BedControl; BedControlType; WidthControl; WidthControlType; StreamType; BaseflowPercWidth; BaseflowWidthNote; ChannelType1; ChannelType2; ChannelType3; ChannelTypeNote; FlowType1; FlowType2; FlowType3; BedPercSoftSed; BedPercSand; ChannelD50; ChannelD84; ChannelD16; MarginalBarCount; MarginalBarD50; MarginalBarCover; MarginalBarNote; PointBarCount; PointBarD50; PointBarCover; PointBarNote; MidchanBarCount; MidchanBarD50; MidchanBarCover; BFIndicatorQuality; EntrenchRatio; GeneralNotes; RGATransect; GeomorphicUnit; BankfullWidth; WettedWidth; WettedDepth; BankfullDepth; SoftSedLeft_cm; SoftSedMid_cm; SoftSedRight_cm; BedSubstrate; LBankHeight; RBankHeight; LBankSlope_deg; RBankSlope_deg; LBankCondition; RBankCondition; LBankPercVeg; RBankPercVeg; LBankSubstrate; RBankSubstrate; RGALocation; BankSide; ErosionHeight; ErosionLength; ErosionCategory; ErosionRate_m_yr; ErosionVolume_m3_yr; Conversion_kg_m3; AnnualErosion_mT_yr; ErosionExtent; ErosionMech; ErosionDesc; ErosionStrat; ErosionTexture; ReachErosion_mT_yr; ReachLength_m; Erosion_mT_km_yr; WildlifeSign1; WildlifeSign2; WildlifeSign3; WildlifeSign4; WildlifeSignNote; LivestockTramp; LivestockTrampNote; FilAlgae; FilAlgeaNote; AquaPlant; AquaPlantType; AquaPlantNote; WoodyDebris; WoodyDebrisNote; StreamShade_perc; LunkStructCount; LunkStructCondition; HabImprovement; LBProtection_perc; RBProtection_perc; Channelization_perc; ArtificialRiffle; BridgeCulvert; BridgeCulvertNotes; Indicator1; Indicator2; Indicator3; Indicator4; Indicator5; Indicator6; Indicator7; Indicator8; IndicatorNote; VerticalActivity1; VerticalActivity2; VerticalSeverity; VerticalNote; VerticalExtent; VerticalConfidence; LateralActivity1; LateralActivity2; LateralActivity3; LateralSeverity; LateralNotes; LateralExtent; LateralConfidence; SedTransMode1; SedTransMode2; SedTransMode3; SedTransMode4; SedTransMode5; SedTransMode6; SedTransMode7; SedTransModeNote; GeomorphProcess1; GeomorphProcess2; GeomorphProcess3; GeomorphProcess4; GeomorphProcess5; GeomorphProcess6; GeomorphProcess7; GeomorphProcess8; GeomorphProcessNote; GeomorphDisturb1; GeomorphDisturb2; GeomorphDisturb3; GeomorphDisturb4; GeomorphDisturbNote; InterpretiveNotes; BarLength; BarWidth; BarSubstrate; BarType; BarNote; RGATape_ft; FeatureHeight; FeatureLength; FeatureDescription; FeatureNotes; PhotoLocation; Camera; PhotoLabel; PhotoDescription","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6852efe9d4be023cfee77720"}],"bbox":{"east":-63.1667,"north":54.8088,"south":21.0821,"west":-129.7712},"citation":"Wieczorek, M.E., Staub, L.E., and Wnuk, K.C., Hafen, K.C., 2023, Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portions of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins (ver. 2.0, July 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P98IG8LO.","creator":[{"creator_email":"mewieczo@usgs.gov","creator_name":"Michael E Wieczorek"}],"creator_project":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"date_created":"2/25/2026","date_updated":"6/3/2026","description":"These tabular data sets represent monthly meteorological metrics processed from North American Multi-Model Ensemble (NMME) for the hindcast (1982-2011) and forecast (2011-2023) periods of record and compiled for the spatial component of select United States Geological Survey stream gage basins (Staub and others, 2023). Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021). The following monthly meteorological metrics were processed: reference temperature (degree Celsius), and total precipitation (millimeters) for forecast periods of 15, 45, 75, and 105 days (0.5 to 3.5 months).","doi_url":"https://doi.org/10.5066/P98IG8LO","domain":["Hydrology","Climate"],"draft":false,"id":"e7814ebe-7712-491d-8e50-d609c409dab8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6852efe9d4be023cfee77720?f=__disk__b0%2Fc6%2F86%2Fb0c68635d53d097494ee4d689e3b3883226b77c2\u0026allowOpen=true"}],"name":"Data-Driven Drought Prediction Project Model Inputs for Select U.S. Geological Survey Streamgage Basins: Monthly Climate Metrics from North American Multi-Model Ensemble (NMME) Phase 2, 1982 - 2023","permalink":"/catalog/datasets/e7814ebe-7712-491d-8e50-d609c409dab8/","project_use_history":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"project_using":[{"id":"DJ60VCN","name":"National Public Delivery of Data-Driven Streamflow Drought Forecasts"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1980 - 2020","temporal_frequency":"monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Date","station_no","mean temperature","mean total precipitation"],"vars":"Date; station_no; mean temperature; mean total precipitation","weight":1},{"access":[{"file_format":"SHP","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5a5643b6e4b01e7be24449fc"},{"file_format":"CSV","name":"github.com","url":"https://github.com/internetofwater/geoconnex.us/blob/master/namespaces/ref/sec_hydrg_reg/sec_hydrg_reg.csv"}],"access_details":null,"bbox":{"east":-65.3400396165,"north":51.5766913163,"south":22.8631673386,"west":-127.889820607},"citation":"Belitz, K., Watson, E., Johnson, T.D., and Sharpe, J.B., 2018, Data release for secondary hydrogeologic regions of the conterminous United States (ver. 2.0, June 2022): U.S. Geological Survey data release, https://doi.org/10.5066/F7F76BSS.","creator":[],"creator_project":[{"id":"DJ50U93","name":"NEHF: National Extent Hydrogeologic Framework"}],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The U.S. Geological Survey (USGS) previously identified 62 Principal Aquifers (PAs) in the U.S., with 57 located in the conterminous states. The USGS characterized areas outside of PAs as \"other rocks\" other rocks account for about 40% of the area of the conterminous states. This paper subdivides the large area identified as other rocks into Secondary Hydrogeologic Regions (SHRs). SHRs are defined as areas of other rock within which the rocks are of comparable geologic age, lithology, and relationship to the presence or absence of underling PAs or overlying glacial deposits. A total of 69 SHRs were identified.\u003cbr\u003eBelitz, K., Watson, E., Johnson, T.D., and Sharpe, J.B., 2018, Data release for secondary hydrogeologic regions of the conterminous United States: U.S. Geological Survey data release, https://doi.org/10.5066/F7F76BSS.\u003cbr\u003eThe current implementation creating these ids and landing-content is in: https://github.com/internetofwater/geoconnex_prep and hosted on https://reference.geoconnex.us/collections/sec_hydrg_reg geojson source for the landing content is available in this repository here.\u003cbr\u003eExample: https://geoconnex.us/ref/sec_hydrg_reg/S47","doi_url":"https://doi.org/10.5066/F7F76BSS","domain":["Hydrogeology"],"draft":false,"id":"e9b2769d-0e91-4d50-9327-aee4abfb6c07","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.1111/gwat.12806"},{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5a5643b6e4b01e7be24449fc?f=__disk__0c%2F28%2F49%2F0c2849f573af3233fad59dd89b5fa8a69b033060\u0026allowOpen=true"}],"name":"Data release for secondary hydrogeologic regions of the conterminous United States (ver. 2.0, June 2022)","permalink":"/catalog/datasets/e9b2769d-0e91-4d50-9327-aee4abfb6c07/","project_use_history":[{"id":"DJ60TRJ","name":"NHGF: National Hydrologic Geospatial Fabric"}],"project_using":[{"id":"DJ60TRJ","name":"NHGF: National Hydrologic Geospatial Fabric"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:2,500,000","temporal_coverage":"2022","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Aquifer area"],"vars":"Aquifer area","weight":1},{"access":[{"file_format":"NC; ZARR","name":"registry.opendata.aws","url":"https://registry.opendata.aws/noaa-nws-aorc/"}],"access_details":null,"bbox":{"east":-56,"north":72,"south":24,"west":-175},"citation":"Recommended citation:\u003cbr\u003eNOAA Analysis of Record for Calibration (AORC) Dataset was accessed [YYYY-MM-DD] at https://registry.opendata.aws/noaa-nws-aorc.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Analysis of Record for Calibration (AORC) is a gridded record of near-surface weather conditions covering the conterminous United States and Alaska and their hydrologically contributing areas. It is defined on a latitude/longitude spatial grid with a mesh length of 30 arc seconds (~800 m), and a temporal resolution of one hour. Elements include hourly total precipitation, temperature, specific humidity, terrain-level pressure, downward longwave and shortwave radiation, and west-east and south-north wind components. It spans the period from 1979 at Continental U.S. (CONUS) locations / 1981 in Alaska, to the near-present (at all locations). This suite of eight variables is sufficient to drive most land-surface and hydrologic models and is used as input to the National Water Model (NWM) retrospective simulation. While the native AORC process generates netCDF output, the data is post-processed to create a cloud optimized Zarr formatted equivalent for dissemination using cloud technology and infrastructure.\u003cbr\u003eAORC Version 1.1 dataset creation\u003cbr\u003eThe AORC dataset was created after reviewing, identifying, and processing multiple large-scale, observation, and analysis datasets. There are two versions of The Analysis Of Record for Calibration (AORC) data.\u003cbr\u003eThe initial AORC Version 1.0 dataset was completed in November 2019 and consisted of a grid with 8 elements at a resolution of 30 arc seconds. The AORC version 1.1 dataset was created to address issues \"see Table 1 in Fall et al., 2023\" in the version 1.0 CONUS dataset. Full documentation on version 1.1 of the AORC data and the related journal publication are provided below.\u003cbr\u003eThe native AORC version 1.1 process creates a dataset that consists of netCDF files with the following dimensions: 1 hour, 4201 latitude values (ranging from 25.0 to 53.0), and 8401 longitude values (ranging from -125.0 to -67).\u003cbr\u003eThe data creation runs with a 10-day lag to ensure the inclusion of any corrections to the input Stage IV and NLDAS data.\u003cbr\u003eNote - The full extent of the AORC grid as defined in its data files exceed those cited above; those outermost rows and columns of data grids are filled with missing values and are the remnant of an early set of required AORC extents that have since been adjusted inward.\u003cbr\u003eFall, G., Kitzmiller, D., Pavlovic, S., Zhang, Z., Patrick, N., St. Laurent, M., Trypaluk, C., Wu, W., and Miller, D., 2023. The Office of Water Prediction's Analysis of Record For Calibration, Version 1.1: Dataset Description and Precipitation Evaluation: JAWRA Journal of the American Water Resources Association v. 59, no. 6, p. 1246–1272, https://doi.org/10.1111/1752-1688.13143.","doi_url":null,"domain":["Climate"],"draft":false,"id":"e9d27490-b301-48d6-8f3e-0a1612a3870d","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.weather.gov/media/owp/operations/aorc_v1_1_methods.pdf"}],"name":"Analysis Of Record for Calibration (AORC) Dataset","permalink":"/catalog/datasets/e9d27490-b301-48d6-8f3e-0a1612a3870d/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NOAA","spatial_extent":"CONUS; AK","spatial_resolution":"1 kilometer","temporal_coverage":"1979 - Present","temporal_frequency":"hourly","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Total Precipitaion (APCP_surface)","Air Temperature (TMP_2maboveground)","Specific Humidity (SPFH_2maboveground)","Downward Long-Wave Radiation Flux (DLWRF_surface)","Downward Short-Wave Radiation Flux (DSWRF_surface)","Pressure (PRES_surface)","U-Component of Wind (UGRD_10maboveground)","V-Component of Wind (VGRD_10maboveground)"],"vars":"Total Precipitaion (APCP_surface); Air Temperature (TMP_2maboveground); Specific Humidity (SPFH_2maboveground); Downward Long-Wave Radiation Flux (DLWRF_surface); Downward Short-Wave Radiation Flux (DSWRF_surface); Pressure (PRES_surface); U-Component of Wind (UGRD_10maboveground); V-Component of Wind (VGRD_10maboveground)","weight":1},{"access":[{"file_format":"GDB; SHP","name":"pubs.usgs.gov","url":"https://pubs.usgs.gov/ds/425/"}],"access_details":null,"bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"Soller, D.R., Reheis, M.C., Garrity, C.P., and Van Sistine, D.R., 2009, Map database for surficial materials in the conterminous United States: U.S. Geological Survey Data Series 425, scale 1:5,000,000, https://pubs.usgs.gov/ds/425/","creator":[{"creator_email":"drsoller@usgs.gov","creator_name":"David R. Soller"},{"creator_email":"cgarrity@usgs.gov","creator_name":"Christopher P. Garrity"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Earth's bedrock is overlain in many places by a loosely compacted and mostly unconsolidated blanket of sediments in which soils commonly are developed. These sediments generally were eroded from underlying rock, and then were transported and deposited. In places, they exceed 1000 ft (330 m) in thickness. Where the sediment blanket is absent, bedrock is either exposed or has been weathered to produce a residual soil. For the conterminous United States, a map by Soller and Reheis (2004, scale 1:5,000,000; https://pubs.usgs.gov/of/2003/of03-275/) shows these sediments and the weathered, residual material; for ease of discussion, these are referred to as \"surficial materials.\" That map was produced as a PDF file, from an Adobe Illustrator-formatted version of the provisional GIS database. The provisional GIS files were further processed without modifying the content of the published map, and are here published.\u003cbr\u003eThis database is a highly generalized depiction of surficial materials for the conterminous United States. It is intended solely as an overview of existing knowledge, as an educational tool, and as a guide to support discussions on where additional geologic mapping might be needed. Because of its generalized unit descriptions, regional scale, and incomplete integration across the map area (as discussed in Soller and Reheis, 2004), this map is not intended to be used at a larger (greater detail) scale than 1:5,000,000.","doi_url":null,"domain":["Soils"],"draft":false,"id":"ebea51d9-aa41-4cc8-8336-901a9bacb9fb","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://pubs.usgs.gov/ds/425/ds425_metadata.pdf"},{"name":"Documentation","url":"https://pubs.usgs.gov/ds/425/ds425_text.pdf"}],"name":"Map Database for Surficial Materials in the Conterminous United States","permalink":"/catalog/datasets/ebea51d9-aa41-4cc8-8336-901a9bacb9fb/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:5,000,000","temporal_coverage":"2009","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Surficial materials"],"vars":"Surficial materials","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5c586b4ee4b0708288ff2725"}],"access_details":null,"bbox":{"east":-63.1667,"north":54.8088,"south":21.0821,"west":-129.7712},"citation":"Sabitov, T.Y., and Wieczorek, M.E., 2019, 30 year (1981-2010) average of annual maximum duration of consecutive dry and wet days per event for the Conterminous United States and District of Columbia: U.S. Geological Survey data release, https://doi.org/10.5066/P9JAJY9Z","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data describes the average of annual maximum duration of consecutive dry and wet days per event, where precipitation totals are 0 or equal and exceeds 1 millimeters respectively, during the 30-year period 1981 - 2010 for the conterminous United States. A wet event is defined as a period when the number of consecutive days with precipitation equals or exceeds 1 millimeter. A dry event is defined as a period when the number of consecutive days with precipitation equals 0 millimeters. The source data was produced and acquired from DAYMET (2018) and is presented here as a 1-kilometer resolution GeoTIFF file.","doi_url":"https://doi.org/10.5066/P9JAJY9Z","domain":["Climate"],"draft":false,"id":"ec24365f-fb04-4730-8fe9-3cfc118f292d","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5c586b4ee4b0708288ff2725?f=__disk__76%2Ff8%2F2a%2F76f82a50cb93ab66333397b80248cfe160c677b1\u0026allowOpen=true"}],"name":"30 year (1981-2010) average of annual maximum duration of consecutive dry and wet days per event for the Conterminous United States and District of Columbia","permalink":"/catalog/datasets/ec24365f-fb04-4730-8fe9-3cfc118f292d/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1 kilometer","temporal_coverage":"1981 - 2010","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Precipitation, maximum duration of consecutive dry days per event","Precipitation, maximum duration of consecutive wet days per event"],"vars":"Precipitation, maximum duration of consecutive dry days per event; Precipitation, maximum duration of consecutive wet days per event","weight":1},{"access":[{"file_format":"TIF","name":"nass.usda.gov","url":"https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php"}],"access_details":null,"bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"USDA National Agricultural Statistics Service Cropland Data Layer, [YEAR], Published crop-specific data layer: USDA-NASS, accessed [YYYY-MM-DD] at https://nassgeodata.gmu.edu/CropScape/","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.\u003cbr\u003eThe Cropland Data Layer (CDL) has a spatial resolution of 30 meters and was produced using satellite imagery collected throughout the growing season. Some CDL states used additional ancillary inputs to supplement and improve the land cover classification including historical CDL data, the United States Geological Survey (USGS) National Elevation Dataset (NED), USDA National Resources Conservation Service (NRCS) National Commodity Crop Productivity Index (NCCPI), and the most current versions of the USGS National Land Cover Database imperviousness and the tree canopy data layers. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. Some CDL states incorporate additional crop-specific ground reference obtained from the following non-FSA sources which are detailed in the 'Lineage' Section of this metadata: US Bureau of Reclamation, NASS Citrus Data Layer (internal use only), California Department of Water Resources, Florida Department of Agriculture and Consumer Services Office of Agricultural Water Policy, Cornell University grape/vineyard data, Oregon State University tree crop and vineyard data, Utah Department of Water Resources, and Washington State Department of Agriculture. The most current version of the NLCD is used as non-agricultural training and validation data. Please visit the CDL FAQs and metadata webpages at \u003chttps://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php\u003e to view a complete list of imagery, ancillary inputs, and ground reference used for a specific state and year.","doi_url":null,"domain":["Land Cover"],"draft":false,"id":"ef517a23-cfe4-4af1-846c-017d20f0832e","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php"}],"name":"Cropland Data Layer (CDL)","permalink":"/catalog/datasets/ef517a23-cfe4-4af1-846c-017d20f0832e/","project_use_history":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NASS","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"2008 - 2024","temporal_frequency":"annual","update_detail":"append","update_frequency":"annual","update_type":"Dynamic","variables":["Cropland designation"],"vars":"Cropland designation","weight":1},{"access":[{"file_format":"TIF","name":"cec.org","url":"http://www.cec.org/north-american-environmental-atlas/land-cover-2010-modis-250m/"}],"access_details":null,"bbox":{"east":-50,"north":72,"south":15,"west":-180},"citation":"Commission for Environmental Cooperation (CEC), 2021, 2010 Land Cover of North America at 250 meters, North American Land Change Monitoring System, Canada Centre for Remote Sensing (CCRS), U.S. Geological Survey (USGS), Comision Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO), Comision Nacional Forestal (CONAFOR), Instituto Nacional de Estadistica y Geografia (INEGI) Ed. 2.0, Raster digital data [250-m], accessed [YYYY-MM-DD] at http://www.cec.org/north-american-environmental-atlas/land-cover-2010-modis-250m/","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The 2010 North American Land Cover data set was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between the Canada Centre for Remote Sensing, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia), National Commission for the Knowledge and Use of the Biodiversity (Comision Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comision Nacional Forestal).  The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries.\u003cbr\u003eThe general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country's specific requirements.\u003cbr\u003eThe initial data set of North American Land Cover at 250 meters reflected land cover information for 2005.  This 2010 data set was produced by updating the 2005 data to show land cover changes as determined from more recent data.  No changes were mapped in Hawaii because newer data were not available.  Land cover classification changed between 2005 and 2010 for approximately 1 percent of the continental area.  For the continental data sets (including surrounding water fringe) 4150241 pixels (1.03% of the area) changed in the update. The following national counts exclude the water fringe: Canada, 3264779 pixels changed (2.05%); Mexico, 47070 pixels changed (0.15%), and U.S., 836706 pixels changed (0.55%). The initial data set used to generate land cover information over North America was produced by the Canada Centre for Remote Sensing from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS/Terra).  All seven land spectral bands were processed from Level 1 granules into top-of-atmosphere reflectance covering North America at a 250-meter spatial and 10-day temporal resolution.\u003cbr\u003eIn order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by INEGI, CONABIO, and CONAFOR; and for the United States by the USGS.  Each country used specific training data and land cover mapping methodologies to create national data sets. This North America data set was produced by combining the national land cover data sets.\u003cbr\u003eThis version 2.0 of the North America Land Cover map at 250 meters spatial resolution has removed the water buffer along the coastline of North America, this will ensure consistency in the statistics calculations of class 18 (water) without incorporating ocean water.\u003cbr\u003eThe countries worked together to produce a definitive list of land cover classifications for the 2005 data; the same classifications were used for the 2010 data.  This document is available for download from the same site as the data and is entitled: North American Land Cover Classifications (2005).","doi_url":null,"domain":["Land Cover"],"draft":false,"id":"ef8b8630-4f38-4ace-8251-2233d2387d1e","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"http://www.cec.org/north-american-environmental-atlas/land-cover-2010-modis-250m/"}],"name":"Land cover 2010 (MODIS)","permalink":"/catalog/datasets/ef8b8630-4f38-4ace-8251-2233d2387d1e/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"CEC","spatial_extent":"North America","spatial_resolution":"250 meter","temporal_coverage":"2010","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Temperate or sub-polar needleleaf forest","Sub-polar taiga needleleaf forest","Tropical or sub-tropical broadleaf evergreen forest","Tropical or sub-tropical broadleaf deciduous forest","Temperate or sub-polar broadleaf deciduous forest","Mixed forest","Tropical or sub-tropical shrubland","Temperate or sub-polar shrubland","Tropical or sub-tropical grassland","Temperate or sub-polar grassland","Sub-polar or polar shrubland-lichen-moss","Sub-polar or polar grassland-lichen-moss","Sub-polar or polar barren-lichen-moss","Wetland","Cropland","Barren land","Urban and built-up","Water","Snow and ice"],"vars":"Temperate or sub-polar needleleaf forest; Sub-polar taiga needleleaf forest; Tropical or sub-tropical broadleaf evergreen forest; Tropical or sub-tropical broadleaf deciduous forest; Temperate or sub-polar broadleaf deciduous forest; Mixed forest; Tropical or sub-tropical shrubland;  Temperate or sub-polar shrubland; Tropical or sub-tropical grassland;  Temperate or sub-polar grassland; Sub-polar or polar shrubland-lichen-moss; Sub-polar or polar grassland-lichen-moss; Sub-polar or polar barren-lichen-moss; Wetland; Cropland; Barren land; Urban and built-up; Water; Snow and ice","weight":1},{"access":[{"file_format":"NC","name":"climatologylab.org","url":"https://www.climatologylab.org/gridmet.html"},{"file_format":"ZARR","name":"WMA STAC","url":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/gridMET"}],"access_details":null,"bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"Abatzoglou, J.T., 2013, Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33: 121-131. https://doi.org/10.1002/joc.3413","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"These data blend the high quality spatial attributes of PRISM with the temporal attributes and additional data from NLDAS-2.\u003cbr\u003egridMET is a dataset of daily high-spatial resolution (~4-km, 1/24th degree) surface meteorological data covering the contiguous US from 1979-yesterday. Climatology Lab have also extended these data to cover southern British Columbia in our real time products. These data can provide important inputs for ecological, agricultural, and hydrological models. These data are updated daily.  gridMET is the preferred naming convention for these data; however, the data are also known as cited as METDATA.","doi_url":"https://doi.org/10.1002/joc.3413","domain":["Climate"],"draft":false,"id":"ef98187e-8703-4ec6-afc1-4dbc72c9d6d8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[{"id":"148a7db7-1175-47aa-8437-5ae39f62b1ce","rel_type":"IsSourceOf"},{"id":"52e59f23-6b64-4035-bfdf-1f78087c6b5c","rel_type":"IsSourceOf"},{"id":"71a9ec77-a83f-4f14-92eb-d7c3266078ce","rel_type":"IsSourceOf"}],"links":[{"name":"Documentation","url":"https://www.climatologylab.org/gridmet.html"}],"name":"gridMET","permalink":"/catalog/datasets/ef98187e-8703-4ec6-afc1-4dbc72c9d6d8/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"CONUS","spatial_resolution":"4 kilometer","temporal_coverage":"1979 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Temperature, maximum","Temperature, minimum","Precipitation accumulation","Downward surface shortwave radiation","Reference evapotranspiration (ASCE Penman-Montieth)","Energy Release Component (fuel model G (conifer forest))","Burning Index (fuel model G (conifer forest))","100-hour and 1000-hour dead fuel moisture","Mean vapor pressure deficit","10-day Palmer Drought Severity Index","Humidity, maximum","Humidity, minimum","Relative humidity","Specific humidity","Wind velocity"],"vars":"Temperature, maximum; Temperature, minimum; Precipitation accumulation; Downward surface shortwave radiation; Reference evapotranspiration (ASCE Penman-Montieth); Energy Release Component (fuel model G (conifer forest)); Burning Index (fuel model G (conifer forest)); 100-hour and 1000-hour dead fuel moisture; Mean vapor pressure deficit; 10-day Palmer Drought Severity Index; Humidity, maximum; Humidity, minimum; Relative humidity; Specific humidity; Wind velocity","weight":1},{"access":[{"file_format":"CSV","name":"zenodo.org","url":"https://zenodo.org/records/4602277"}],"access_details":null,"bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"Turner, S.W.D., Steyaert, J.C., Condon, L., and Voisin, N., 2021, Water storage and release policies for all large reservoirs of conterminous United States: Journal of Hydrology, v. 603, https://doi.org/10.1016/j.jhydrol.2021.126843","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Large-scale hydrological and water resource models (LHMs) require water storage and release schemes to represent flow regulation by reservoirs. Owing to a lack of observed reservoir operations, state-of-the-art LHMs deploy a generic reservoir scheme that may fail to represent local operating behaviors. This is a new dataset of bespoke water storage and release policies for 1,930 reservoirs of conterminous United States. The Inferred Storage Targets and Release Functions (ISTARF-CONUS) dataset relies on a new inventory of observed daily reservoir operations (ResOpsUS) to generate reservoir operating rules for 595 data-rich reservoirs. These functions are developed in a standardized form that allows for extrapolation of operating schemes to 1,335 data-scarce reservoirs—leading to the first inventory of empirically derived reservoir operating policies for all large CONUS reservoirs documented in the Global Reservoir and Dams (GRanD) database. Evaluation of the new scheme in daily simulations forced with observed inflow demonstrates substantial and robust improvement for both release and storage relative to the popular Hanasaki method. Performance of the extrapolation approach for data-scarce reservoirs is evaluated with leave-one-out validation and is shown to also offer modest gains on average over Hanasaki. ISTARF-CONUS may be readily adopted in any LHM featuring large reservoirs of the conterminous United States.","doi_url":"https://doi.org/10.5281/zenodo.4602277","domain":["Hydrology"],"draft":false,"id":"efa1aa42-dd2b-417a-8d7e-cb46dd6277d8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.1016/j.jhydrol.2021.126843"}],"name":"ISTARF-CONUS","permalink":"/catalog/datasets/efa1aa42-dd2b-417a-8d7e-cb46dd6277d8/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"PNNL","spatial_extent":"CONUS","spatial_resolution":"100 kilometer","temporal_coverage":"2021","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Inferred storage targets","Inferred release functions","Reservoir storage capacity","Estimated mean inflow rate","Mean inflow rate","Observation parameters","Extrapolated parametrs","Storage parameters","Release parameters"],"vars":"Inferred storage targets; Inferred release functions; Reservoir storage capacity; Estimated mean inflow rate; Mean inflow rate; Observation parameters; Extrapolated parametrs; Storage parameters; Release parameters","weight":1},{"access":[{"file_format":"GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/535eda80e4b08e65d60fc834"}],"access_details":null,"bbox":{"east":-63.21,"north":50.741871378,"south":17.1,"west":-161.31},"citation":"Viger, R.J. and Bock, A., 2014, GIS Features of the Geospatial Fabric for National Hydrologic Modeling, U.S. Geological Survey; https://doi.org/10.5066/F7542KMD ","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The Geospatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline (\"one\"), 2) Points of Interest (\"POIs\"), 3) a routing network (\"nsegment\"), and 4) Hydrologic Response Units (\"nhru\"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature class and its derivation is provided within the section of this metadata document. The first entry in that section provides an overview of the delineation process, with each subsequent corresponding to one of the four types of feature. These entries describe the derivation of feature types in the order in which they are created. Minimal attribution (feature size, location, and routing connectivity) is provided for the feature classes within the GF Feature data set. More extensive feature attribution is published separately as individual tables of attributes(for example, http://dx.doi.org/doi:10.5066/F7RX9937) or via entire configurations of tables engineered to satisfy particular watershed models (for example, http://dx.doi.org/doi:10.5066/F7WM1BF7).","doi_url":"https://doi.org/10.5066/F7542KMD","domain":["Hydrology"],"draft":false,"id":"efb748cd-03fe-4f50-84e0-10824663d28d","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[{"id":"14b6eb88-06dc-4152-b37f-a355c850bc7e","rel_type":"IsSourceOf"}],"linked_usecases":[{"id":"52e59f23-6b64-4035-bfdf-1f78087c6b5c","rel_type":"IsSourceOf"}],"links":[],"name":"GIS Features of the Geospatial Fabric for National Hydrologic Modeling, version 1.0","permalink":"/catalog/datasets/efb748cd-03fe-4f50-84e0-10824663d28d/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":true,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; HI; PR","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Points of interest","Flowpaths","Hydrologic Response Units (hrus)"],"vars":"Points of interest; Flowpaths; Hydrologic Response Units (hrus)","weight":1},{"access":[{"file_format":"ASCII","name":"fhwa.dot.gov","url":"https://www.fhwa.dot.gov/bridge/nbi/ascii2021.cfm"},{"file_format":"Unknown","name":"fedmaps.maps.arcgis.com","url":"https://fedmaps.maps.arcgis.com/home/item.html?id=a0fa29a39fe444ac97d4337c569b9801\u0026view=table\u0026sortOrder=desc\u0026sortField=defaultFSOrder"}],"access_details":null,"bbox":{"east":-53,"north":50,"south":15,"west":-127},"citation":null,"creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The National Bridge Inventory provides information on the locations and safety inspections of bridges in the U.S.","doi_url":null,"domain":["Infrastructure"],"draft":false,"id":"f02d4cfd-3655-46ae-854d-62df0fb3644a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.fhwa.dot.gov/bridge/nbi/format.cfm"}],"name":"National Bridge Inventory","permalink":"/catalog/datasets/f02d4cfd-3655-46ae-854d-62df0fb3644a/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"FHWA","spatial_extent":"CONUS; PR","spatial_resolution":"NA","temporal_coverage":"1992 - 2021","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Bridge locations and other bridge specific data"],"vars":"Bridge locations and other bridge specific data","weight":1},{"access":[{"file_format":"SHP; NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6193fbb0d34eb622f68f13e5"}],"access_details":null,"bbox":{"east":-87.3122,"north":44.8951,"south":43.8623,"west":-88.3192},"citation":"Minsley, B.J, Bloss, B.R., Hart, D.J., Fitzpatrick, W., Muldoon, M.A., Stewart, E.K., Hunt, R.J., James, S.R., Foks, N.L., and Komiskey, M.J., 2022, Airborne electromagnetic and magnetic survey data, northeast Wisconsin (ver. 1.1, June 2022): U.S. Geological Survey data release, https://doi.org/10.5066/P93SY9LI.","creator":[{"creator_email":"bminsley@usgs.gov","creator_name":"Burke J. Minsley"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"Airborne electromagnetic (AEM) and magnetic survey data were collected during January and February 2021 over a distance of 3,170 line kilometers in northeast Wisconsin. These data were collected in support of an effort to improve estimates of depth to bedrock through a collaborative project between the U.S. Geological Survey (USGS), Wisconsin Department of Agriculture, Trade, and Consumer Protection (DATCP), and Wisconsin Geological and Natural History Survey (WGNHS). Data were acquired by SkyTEM Canada Inc. with the SkyTEM 304M time-domain helicopter-borne electromagnetic system together with a Geometrics G822A cesium vapor magnetometer. The survey was acquired at a nominal flight height of 30 - 40 m above terrain along parallel flight lines oriented northwest-southeast with nominal line spacing of 0.5 miles (800 m). AEM data were inverted to produce models of electrical resistivity along flight paths, with typical depth of investigation up to about 300 m and 1 - 2 m near-surface resolution. Shallow resistivity transitions were used to estimate depth to bedrock across the survey area.","doi_url":"https://doi.org/10.5066/P93SY9LI","domain":["Geophysical"],"draft":false,"id":"f19328de-efea-4755-9070-7252de9dcb13","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6193fbb0d34eb622f68f13e5?f=__disk__a0%2Fbe%2F64%2Fa0be648f277299a184cbe65a164069df275f64cf\u0026allowOpen=true"}],"name":"Airborne electromagnetic and magnetic survey data, northeast Wisconsin (ver. 1.1, June 2022)","permalink":"/catalog/datasets/f19328de-efea-4755-9070-7252de9dcb13/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"northeast Wisconsin","spatial_resolution":"30 meter; 100 meter; 800 meter","temporal_coverage":"2021","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["FID","Shape","ID"],"vars":"FID; Shape; ID","weight":1},{"access":[{"file_format":"GDB","name":"springernature.figshare.com","url":"https://springernature.figshare.com/collections/Inter-basin_surface_water_transfers_database_for_the_conterminous_United_States_1986-2015/6203224"}],"access_details":"At the springernature.figshare.com access point scroll to the bottom of the page, click on \"dataset\", and navigate to the option to download the zipped file: IBTsCONUSpublished2023.gdb.zip (599.11 MB).","bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"Dobbs, G. Rebecca; Liu, Ning; Caldwell, Peter; Miniat, Chelcy; Sun, Ge; Duan, Kai; et al. (2023). Inter-basin surface water transfers database for the conterminous United States, 1986-2015. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.6203224.v1","creator":[],"creator_project":[],"date_created":"4/23/2025","date_updated":"6/3/2026","description":"This geodatabase contains interbasin transfers (IBTs) connected with public water supplies in the conterminous United States (CONUS) from 1986 to 2015. The data includes surface water transfer volumes collected, evaluated, and compiled from disparate sources that cross HUC8 boundaries. All transfers are depicted using an arrow from the centroid of each starting HUC12 to the centroid of the destination HUC12. Transfer volume data in million meters cubed per year for 30 years (1986-2015) are included in a separate table for each transfer and sub-transfer.","doi_url":"https://doi.org/10.6084/m9.figshare.c.6203224.v1","domain":["Hydrology","Infrastructure","Water Use"],"draft":false,"id":"f197ceee-5d52-4e6f-afe0-bb1dedc8c072","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"ae9546b9-1eda-407a-9129-8a62b43ac056","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://doi.org/10.1038/s41597-023-02148-5"}],"name":"Inter-basin surface water transfers database for the conterminous United States, 1986-2015","permalink":"/catalog/datasets/f197ceee-5d52-4e6f-afe0-bb1dedc8c072/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USDA; DOE","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1986 - 2015","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["conveyance start and end (XY location)","ID","Description","FromHUC12","ToHUC12","yearly volumes in million meters cubed from 1986 to 2015"],"vars":"conveyance start and end (XY location); ID; Description; FromHUC12; ToHUC12; yearly volumes in million meters cubed from 1986 to 2015","weight":1},{"access":[{"file_format":"CSV","name":"zenodo.org","url":"https://zenodo.org/records/5893641"}],"access_details":"For the zenodo access point, metadata is a README.md zip file available with data download.","bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"Steyaert, J.C., Condon, L.E., Turner, S.W.D., Voisin, N., 2022, ResOpsUS, a dataset of historical reservoir operations in the contiguous United States: Scientific Data, v. 9, https://doi.org/10.1038/s41597-022-01134-7","creator":[{"creator_email":"steyaertj@email.arizona.edu","creator_name":"Jennie Steyaert"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset contains time series of historical reservoir operations for the contiguous United States (CONUS) (and areas draining to the contiguous US). The database includes: inflows, outflows, change in storage and evaporative losses as available. Data was assembled directly from reservoir operators and spans 1974 to 2020.","doi_url":"https://doi.org/10.5281/zenodo.5893641","domain":["Hydrology","Infrastructure"],"draft":false,"id":"f1bb6ddc-faa4-4e3b-883d-de3ac02c116c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://doi.org/10.6084/m9.figshare.17161415"},{"name":"Documentation","url":"https://doi.org/10.1038/s41597-022-01134-7"}],"name":"ResOpsUS","permalink":"/catalog/datasets/f1bb6ddc-faa4-4e3b-883d-de3ac02c116c/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"Academic Institution(s)","spatial_extent":"CONUS","spatial_resolution":"NA","temporal_coverage":"1974 - 2020","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Storage","Inflow","Outflow","Evaporation","Elevation"],"vars":"Storage; Inflow; Outflow; Evaporation; Elevation","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6642ad62d34e1955f5a41e4f"}],"access_details":null,"bbox":{"east":-105.7612,"north":43.1659,"south":34.1458,"west":-111.6359},"citation":"Lopez, S.F., Knight, J.E., Wise, D.R., Jones, C.J., and Masbruch, M.M., 2024, Compilation of surface water diversion sites and daily withdrawals in the Upper Colorado River and Little Colorado River Basins, 1980-2022: U.S. Geological Survey data release, https://doi.org/10.5066/P1496VHX.","creator":[{"creator_email":"slopez@usgs.gov","creator_name":"Sam Lopez"}],"creator_project":[{"id":"DJ60UX2","name":"RIMBE: Regional IWAAs Integrated Methods for Base Evaluation"}],"date_created":"5/1/2025","date_updated":"6/3/2026","description":"This data release contains an inventory of 1,358 surface water diversion structures with associated daily time series withdrawal records (1980-2022) for structures within the Upper Colorado River and Little Colorado River Basins. Diversion structures were included in this dataset if they were determined to have the capacity to divert water at rates greater than 10 cubic feet per second. Sites are classified by use: irrigation, municipal, industrial, hydropower, intrabasin (across HUC4), and interbasin (exported from the Upper Colorado to another HUC2). The original withdrawal data, the processed datasets, and a Python script to automate retrieval of daily time series data from source databases are included in the data release.","doi_url":"https://doi.org/10.5066/P1496VHX","domain":["Hydrology","Infrastructure","Water Use"],"draft":false,"id":"f22b1d0a-bd1a-4759-ab30-07ef0bed5e40","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"ae9546b9-1eda-407a-9129-8a62b43ac056","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6642ad62d34e1955f5a41e4f?f=__disk__7f%2Fb7%2F36%2F7fb7362d4b104357af67bf00cedf5d27665c6691\u0026allowOpen=true"}],"name":"Compilation of surface water diversion sites and daily withdrawals in the Upper Colorado River and Little Colorado River Basins, 1980-2022","permalink":"/catalog/datasets/f22b1d0a-bd1a-4759-ab30-07ef0bed5e40/","project_use_history":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"project_using":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Upper Colorado River Basin; Little Colorado River Basin","spatial_resolution":"NA","temporal_coverage":"1980 - 2022","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Diversion structure ID","Diversion structure name","basin code","SiteUse","Destination code","original source data IDs","decLat","decLong","dest_decLat","dest_decLong","notes","withdrawal startDate","withdrawal endDate"],"vars":"Diversion structure ID; Diversion structure name; basin code; SiteUse; Destination code; original source data IDs; decLat; decLong; dest_decLat; dest_decLong; notes; withdrawal startDate; withdrawal endDate","weight":1},{"access":[{"file_format":"CSV; PARQUET","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/6685ad6bd34e10615ec29864"}],"bbox":{"east":-66.0187,"north":52.8807,"south":24.3953,"west":-124.9022},"citation":"Miller, O.L., Martinez, A.J., Cashman, M.J., Powlen, K.A., Padilla, J.A., and Stets, E.G., 2024, Water budget results for a water availability assessment across the conterminous United States for water years 2010-2020: U.S. Geological Survey data release, https://doi.org/10.5066/P1CFYEHO.","creator":[{"creator_email":"omiller@usgs.gov","creator_name":"Olivia L. Miller"}],"creator_project":[],"date_created":"1/20/2026","date_updated":"6/3/2026","description":"This data release contains results from a simple monthly water budget that includes water supply and consumptive use for thermoelectric, irrigation, and public supply for 12-digit Hydrologic Unit Codes (HUC12) across the conterminous United States for water years 2010-2020. These results were developed in support of Integrated water availability in the conterminous United States, 2010–20, chap. F\u0026nbsp;\u003cem\u003eof\u003c/em\u003e\u0026nbsp;U.S. Geological Survey integrated water availability assessment—2010–20 (Stets et al., 2025;\u0026nbsp;\u003cu\u003e\u003ca href=\"https://doi.org/10.3133/pp1894F\" style=\"border: 0px; font-style: inherit; font-variant-caps: inherit; font-stretch: inherit; font-size: inherit; line-height: inherit; font-family: inherit; font-size-adjust: inherit; font-kerning: inherit; font-variant-alternates: inherit; font-variant-ligatures: inherit; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-variant-position: inherit; font-feature-settings: inherit; font-optical-sizing: inherit; font-variation-settings: inherit; padding: 0px; vertical-align: baseline; color: rgb(5, 99, 193) !important;\" title=\"https://doi.org/10.3133/pp1894F\"\u003ehttps://doi.org/10.3133/pp1894F\u003c/a\u003e\u003c/u\u003e). Water budget results also include an assessment of supply and use imbalances within the context of historical climatic conditions to calculate a surface water supply and use index and when considering a range of environmental flow allocation methods.","doi_url":"https://doi.org/10.5066/P1CFYEHO","domain":["Hydrology"],"draft":false,"id":"f3cb10eb-4461-4968-8ae7-7f13d70fdc23","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/6685ad6bd34e10615ec29864?f=__disk__3b%2Fd8%2Fcf%2F3bd8cfea59ec84c63a555beba46a1dd6455f2cf9\u0026allowOpen=true"}],"name":"Water budget results for a water availability assessment across the conterminous United States for water years 2010-2020","permalink":"/catalog/datasets/f3cb10eb-4461-4968-8ae7-7f13d70fdc23/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2009 - 2020","temporal_frequency":"monthly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["year_month","wy_month","huc","to_huc","season","q_bf_inc_m3","q_qf_inc_m3","q_ir_inc_m3","q_th_inc_m3","q_ps_inc_m3","q_in_m3","inc_aream2","q_bf_cum_m3","q_qf_cum_m3","q_ir_cum_m3","q_th_cum_m3","q_ps_cum_m3","q_ws_cum_m3","q_loss_cum_m3","cum_aream2","q_out_m3","q_totsupply_m3","q_totloss_inc_m3","q_out_mm","q_in_mm","q_out_inc_mm","q_in_inc_mm","q_ws_cum_mm","q_bf_cum_mm","q_qf_cum_mm","q_totsupply_mm","q_totloss_inc_mm","q_bf_inc_mm","q_qf_inc_mm","q_ir_inc_mm","q_th_inc_mm","q_ps_inc_mm","q_ws_cum_m3_monthmedian","SUI","SUIclass","smakhtin_EFR_m3_EFR_SUI","tennant_EFRoutstanding_m3_EFR_SUI","tennant_EFRexcellent_m3_EFR_SUI","tennant_EFRgood_m3_EFR_SUI","tennant_EFRfair_m3_EFR_SUI","q50q90_EFRfair_m3_EFR_SUI","Tessman_EFR_m3_EFR_SUI","VMF_EFRfair_m3_EFR_SUI","Richter_EFRMod_m3_EFR_SUI","Richter_EFRHigh_m3_EFR_SUI","Carlisle_EFRMod_m3_EFR_SUI","X25pcMAF_EFR_m3_EFR_SUI","X50pcMMF_EFR_m3_EFR_SUI","year_month","wy_month","huc","to_huc","season","q_bf_inc_m3","q_qf_inc_m3","q_ir_inc_m3","q_th_inc_m3","q_ps_inc_m3","q_in_m3","inc_aream2","q_bf_cum_m3","q_qf_cum_m3","q_ir_cum_m3","q_th_cum_m3","q_ps_cum_m3","q_ws_cum_m3","q_loss_cum_m3","cum_aream2","q_out_m3","q_totsupply_m3","q_totloss_inc_m3","q_out_mm","q_in_mm","q_out_inc_mm","q_in_inc_mm","q_ws_cum_mm","q_bf_cum_mm","q_qf_cum_mm","q_totsupply_mm","q_totloss_inc_mm","q_bf_inc_mm","q_qf_inc_mm","q_ir_inc_mm","q_th_inc_mm","q_ws_cum_m3_monthmedian","SUI","SUIclass","smakhtin_EFR_m3_EFR_SUI","tennant_EFRoutstanding_m3_EFR_SUI","tennant_EFRexcellent_m3_EFR_SUI","tennant_EFRgood_m3_EFR_SUI","tennant_EFRfair_m3_EFR_SUI","q50q90_EFRfair_m3_EFR_SUI","Tessman_EFR_m3_EFR_SUI","VMF_EFRfair_m3_EFR_SUI","Richter_EFRMod_m3_EFR_SUI","Richter_EFRHigh_m3_EFR_SUI","Carlisle_EFRMod_m3_EFR_SUI","X25pcMAF_EFR_m3_EFR_SUI","X50pcMMF_EFR_m3_EFR_SUI","year_month","wy_month","huc","to_huc","season","q_bf_inc_m3","q_qf_inc_m3","q_ir_inc_m3","q_th_inc_m3","q_ps_inc_m3","q_in_m3","inc_aream2","q_bf_cum_m3","q_qf_cum_m3","q_ir_cum_m3","q_th_cum_m3","q_ps_cum_m3","q_ws_cum_m3","q_loss_cum_m3","cum_aream2","q_out_m3","q_totsupply_m3","q_totloss_inc_m3","q_out_mm","q_in_mm","q_out_inc_mm","q_in_inc_mm","q_ws_cum_mm","q_bf_cum_mm","q_qf_cum_mm","q_totsupply_mm","q_totloss_inc_mm","q_bf_inc_mm","q_qf_inc_mm","q_ir_inc_mm","q_th_inc_mm","q_ps_inc_mm","q_ws_cum_m3_monthmedian","SUI","SUIclass","smakhtin_EFR_m3_EFR_SUI","tennant_EFRoutstanding_m3_EFR_SUI","tennant_EFRexcellent_m3_EFR_SUI","tennant_EFRgood_m3_EFR_SUI","tennant_EFRfair_m3_EFR_SUI","q50q90_EFRfair_m3_EFR_SUI","Tessman_EFR_m3_EFR_SUI","VMF_EFRfair_m3_EFR_SUI","Richter_EFRMod_m3_EFR_SUI","Richter_EFRHigh_m3_EFR_SUI","Carlisle_EFRMod_m3_EFR_SUI","X25pcMAF_EFR_m3_EFR_SUI","X50pcMMF_EFR_m3_EFR_SUI"],"vars":"year_month; wy_month; huc; to_huc; season; q_bf_inc_m3; q_qf_inc_m3; q_ir_inc_m3; q_th_inc_m3; q_ps_inc_m3; q_in_m3; inc_aream2; q_bf_cum_m3; q_qf_cum_m3; q_ir_cum_m3; q_th_cum_m3; q_ps_cum_m3; q_ws_cum_m3; q_loss_cum_m3; cum_aream2; q_out_m3; q_totsupply_m3; q_totloss_inc_m3; q_out_mm; q_in_mm; q_out_inc_mm; q_in_inc_mm; q_ws_cum_mm; q_bf_cum_mm; q_qf_cum_mm; q_totsupply_mm; q_totloss_inc_mm; q_bf_inc_mm; q_qf_inc_mm; q_ir_inc_mm; q_th_inc_mm; q_ps_inc_mm; q_ws_cum_m3_monthmedian; SUI; SUIclass; smakhtin_EFR_m3_EFR_SUI; tennant_EFRoutstanding_m3_EFR_SUI; tennant_EFRexcellent_m3_EFR_SUI; tennant_EFRgood_m3_EFR_SUI; tennant_EFRfair_m3_EFR_SUI; q50q90_EFRfair_m3_EFR_SUI; Tessman_EFR_m3_EFR_SUI; VMF_EFRfair_m3_EFR_SUI; Richter_EFRMod_m3_EFR_SUI; Richter_EFRHigh_m3_EFR_SUI; Carlisle_EFRMod_m3_EFR_SUI; X25pcMAF_EFR_m3_EFR_SUI; X50pcMMF_EFR_m3_EFR_SUI; year_month; wy_month; huc; to_huc; season; q_bf_inc_m3; q_qf_inc_m3; q_ir_inc_m3; q_th_inc_m3; q_ps_inc_m3; q_in_m3; inc_aream2; q_bf_cum_m3; q_qf_cum_m3; q_ir_cum_m3; q_th_cum_m3; q_ps_cum_m3; q_ws_cum_m3; q_loss_cum_m3; cum_aream2; q_out_m3; q_totsupply_m3; q_totloss_inc_m3; q_out_mm; q_in_mm; q_out_inc_mm; q_in_inc_mm; q_ws_cum_mm; q_bf_cum_mm; q_qf_cum_mm; q_totsupply_mm; q_totloss_inc_mm; q_bf_inc_mm; q_qf_inc_mm; q_ir_inc_mm; q_th_inc_mm; q_ws_cum_m3_monthmedian; SUI; SUIclass; smakhtin_EFR_m3_EFR_SUI; tennant_EFRoutstanding_m3_EFR_SUI; tennant_EFRexcellent_m3_EFR_SUI; tennant_EFRgood_m3_EFR_SUI; tennant_EFRfair_m3_EFR_SUI; q50q90_EFRfair_m3_EFR_SUI; Tessman_EFR_m3_EFR_SUI; VMF_EFRfair_m3_EFR_SUI; Richter_EFRMod_m3_EFR_SUI; Richter_EFRHigh_m3_EFR_SUI; Carlisle_EFRMod_m3_EFR_SUI; X25pcMAF_EFR_m3_EFR_SUI; X50pcMMF_EFR_m3_EFR_SUI; year_month; wy_month; huc; to_huc; season; q_bf_inc_m3; q_qf_inc_m3; q_ir_inc_m3; q_th_inc_m3; q_ps_inc_m3; q_in_m3; inc_aream2; q_bf_cum_m3; q_qf_cum_m3; q_ir_cum_m3; q_th_cum_m3; q_ps_cum_m3; q_ws_cum_m3; q_loss_cum_m3; cum_aream2; q_out_m3; q_totsupply_m3; q_totloss_inc_m3; q_out_mm; q_in_mm; q_out_inc_mm; q_in_inc_mm; q_ws_cum_mm; q_bf_cum_mm; q_qf_cum_mm; q_totsupply_mm; q_totloss_inc_mm; q_bf_inc_mm; q_qf_inc_mm; q_ir_inc_mm; q_th_inc_mm; q_ps_inc_mm; q_ws_cum_m3_monthmedian; SUI; SUIclass; smakhtin_EFR_m3_EFR_SUI; tennant_EFRoutstanding_m3_EFR_SUI; tennant_EFRexcellent_m3_EFR_SUI; tennant_EFRgood_m3_EFR_SUI; tennant_EFRfair_m3_EFR_SUI; q50q90_EFRfair_m3_EFR_SUI; Tessman_EFR_m3_EFR_SUI; VMF_EFRfair_m3_EFR_SUI; Richter_EFRMod_m3_EFR_SUI; Richter_EFRHigh_m3_EFR_SUI; Carlisle_EFRMod_m3_EFR_SUI; X25pcMAF_EFR_m3_EFR_SUI; X50pcMMF_EFR_m3_EFR_SUI","weight":1},{"access":[{"file_format":"HDF","name":"nsidc.org","url":"https://nsidc.org/data/mod10a1f/versions/61"}],"access_details":null,"bbox":{"east":180,"north":90,"south":-90,"west":-180},"citation":"Hall, D.K. and Riggs, G.A., 2020, MODIS/Terra CGF Snow Cover Daily L3 Global 500m SIN Grid, Version 61. [Indicate subset used], Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed [YYYY-MM-DD] at https://doi.org/10.5067/MODIS/MOD10A1F.061","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This global Level-3 data set (MOD10A1F) provides daily cloud-free snow cover derived from the MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid data set (MOD10A1). Grid cells in MOD10A1 which are obscured by cloud cover are filled by retaining clear-sky views of the surface from previous days. A separate parameter is provided which tracks the number of days in each cell since the last clear-sky observation. Each data granule contains a 10 degree x 10 degree tile projected to the 500 meter sinusoidal grid. MODIS Terra daily Normalized Daily Snow Index and Albedo.","doi_url":"https://doi.org/10.5067/MODIS/MOD10A1F.061","domain":["Snow"],"draft":false,"id":"f52742c4-9302-46fc-b540-05847c487cf6","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://nsidc.org/sites/default/files/mod10a1f-v061-userguide_0.pdf"}],"name":"MOD10A1F: MODIS/Terra CGF Snow Cover Daily L3 Global 500m SIN Grid, Version 61","permalink":"/catalog/datasets/f52742c4-9302-46fc-b540-05847c487cf6/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NSIDC","spatial_extent":"Global","spatial_resolution":"500 meter","temporal_coverage":"2000 - Present","temporal_frequency":"daily","update_detail":"append","update_frequency":"unknown","update_type":"Dynamic","variables":["Snow cover"],"vars":"Snow cover","weight":1},{"access":[{"file_format":"XML; JSON; CSV","name":"api.tidesandcurrents.noaa.gov","url":"https://api.tidesandcurrents.noaa.gov/api/prod/"}],"access_details":null,"bbox":{"east":-56,"north":72,"south":17,"west":-172},"citation":null,"creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"NOAA's Center for Operational Oceanographic Products and Services (CO-OPS) is the authoritative source for accurate, reliable, and timely tides, water levels, currents, and other coastal oceanographic and meteorological information. Our services support safe and efficient maritime commerce and transportation, help protect public health and safety, and promote robust, resilient coastal communities. CO-OPS maintains ocean observing infrastructure, including more than 200 permanent water level stations on the U.S. coasts and Great Lakes, an integrated system of real-time sensors concentrated in busy seaports, and temporary meters that collect observations for tidal current predictions. Through these systems, historic and real-time data are provided for the nation, forecasts, predictions, and scientific analyses that protect life, the economy, and the environment on the coast.\u003cbr\u003eCoastal and estuarine currents describe the movement of water from one location to another. NOAA periodically conducts current surveys in areas around the nation to ensure the accuracy of tidal current predictions. CO-OPS also offers real-time current information as part of many of our Physical Oceanographic Real Time Systems. Commercial and recreational mariners depend on this information for safe navigation.\u003cbr\u003eCO-OPS maintains the National Water Level Observation Network (NWLON), an observation network with more than 200 permanent water level stations on the coasts and Great Lakes. This system allows NOAA to provide the official tidal predictions for the nation. Accurate water level data is critical for safe and efficient marine navigation and for the protection of infrastructure along the coast. The NWLON also provides the national standards for tide and water level reference datums used for nautical charting, coastal engineering, international treaty regulation, and boundary determination. The NWLON is also widely recognized as the key federal component of the Integrated Ocean Observing System (IOOS).","doi_url":null,"domain":["Hydrology"],"draft":false,"id":"f56842cf-d94f-4bce-a3e6-0a25a06a6cb8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://tidesandcurrents.noaa.gov/products.html"}],"name":"Tides and Currents","permalink":"/catalog/datasets/f56842cf-d94f-4bce-a3e6-0a25a06a6cb8/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"NOAA","spatial_extent":"CONUS; AK; HI","spatial_resolution":"NA","temporal_coverage":"1854 - Present","temporal_frequency":"1 minute; 6 minutes; 30 minutes; hourly","update_detail":"append","update_frequency":"18 minutes","update_type":"Dynamic","variables":["Water levels","Tide predictions","Sea level trends","Extreme sea levels","Coastal inundation","Historic Tides and tidal current","Current data","Historic current data","Meteorological observations","Water temperature and conductivity"],"vars":"Water levels; Tide predictions; Sea level trends; Extreme sea levels; Coastal inundation; Historic Tides and tidal current; Current data; Historic current data; Meteorological observations; Water temperature and conductivity","weight":1},{"access":[{"file_format":"SHP; CSV; NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/623e4418d34e915b67d7dd78"}],"access_details":null,"bbox":{"east":-74.380785128688,"north":42.4544544671721,"south":38.78945802269,"west":-76.3879905924101},"citation":"Oliver, S.K., Sleckman, M.J., Appling, A.P., Corson-Dosch, H.R., Zwart, J.A., Thompson, T.P., Koenig, L., White, E., Watkins, D., Platt, L.R., Padilla, J.A., and Sadler, J.M., 2022, Data to support water quality modeling efforts in the Delaware River Basin: U.S. Geological Survey data release, https://doi.org/10.5066/P9GUHX1U.","creator":[{"creator_email":"soliver@usgs.gov","creator_name":"Sam Oliver"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this data release includes data that characterize both rivers and a subset of reservoirs in the basin. This release provides an update to many of the files provided in a previous data release (Oliver and others, 2021). The data are stored in 3 child folders: 1) spatial information, 2) observations, and 3) model driver data.\u003cbr\u003e) 1. Spatial Information - Spatial data used for modeling efforts in the Delaware River Basin - a shapefile of polylines for the river segments, point data for observation locations, and polygons for the three (Pepacton, Cannonsville, and Neversink) reservoirs in this dataset.\u003cbr\u003e2. Observations - Reservoir (surface levels, releases, diversions, water temperature) and river (water temperature and flow) observations that can be used to train and test water quality models.\u003cbr\u003e3) Model driver data - Driver data used to force water quality models, including stream reach distance matrices and daily meteorology data from NOAA GEFS and gridMET. This child item also includes the inputs and outputs of an uncalibrated run of PRMS-SNTemp which predicts mean water temperature at all reaches in the DRB. This data compilation was funded by the USGS.","doi_url":"https://doi.org/10.5066/P9GUHX1U","domain":["Climate","Hydrology","Water Quality"],"draft":false,"id":"f5b136b2-6ced-4618-89b2-ff8074024dc8","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/623e4418d34e915b67d7dd78?f=__disk__5c%2Fe3%2F1d%2F5ce31d94d89e5016f7c715f3e3f75ce40440fe13\u0026allowOpen=true"}],"name":"Data to support water quality modeling efforts in the Delaware River Basin","permalink":"/catalog/datasets/f5b136b2-6ced-4618-89b2-ff8074024dc8/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"Delaware River basin","spatial_resolution":"1:24,000; 1:100,000","temporal_coverage":"1979 - 2022","temporal_frequency":"daily","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["NHGF v1.0 segments","NHD HR reservoir polygons","EcoSHEDS and NWIS sites"],"vars":"NHGF v1.0 segments; NHD HR reservoir polygons; EcoSHEDS and NWIS sites","weight":1},{"access":[{"file_format":"CSV","name":"epa.gov","url":"https://www.epa.gov/national-aquatic-resource-surveys/streamcat-dataset#access-streamcat-data"}],"access_details":null,"bbox":{"east":-56,"north":50,"south":24,"west":-127},"citation":"Hill, R.A., Weber, M.H., Leibowitz, S.G., Olsen, A.R., and Thornbrugh, D.J., 2016, The Stream-Catchment (StreamCat) Dataset: A Database of Watershed Metrics for the Conterminous United States: Journal of the American Water Resources Association (JAWRA), v. 52, no. 1, p. 120-128, accessed November 28, 2023, at https://doi.org/10.1111/1752-1688.12372","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This is an extensive database of landscape metrics for ~2.65 million stream segments, and their associated catchments, within the conterminous United States (U.S.): The Stream-Catchment (StreamCat) Dataset. These data are publically available and greatly reduce the specialized geospatial expertise needed by researchers and managers to acquire landscape information for both catchments (i.e., the nearby landscape flowing directly into streams) and full upstream watersheds of specific stream reaches. When combined with an existing geospatial framework of the Nation's rivers and streams (National Hydrography Dataset Plus Version 2), the distribution of catchment and watershed characteristics can be visualized for the conterminous U.S. The StreamCat Dataset provides an important tool for stream researchers and managers to understand and characterize the Nation's rivers and streams.","doi_url":"https://doi.org/10.1111/1752-1688.12372","domain":["Land Cover","Hydrology"],"draft":false,"id":"f5bc87be-8f39-44a9-8d25-e2108db06522","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Documentation","url":"https://www.epa.gov/national-aquatic-resource-surveys/streamcat-dataset"}],"name":"StreamCAT","permalink":"/catalog/datasets/f5bc87be-8f39-44a9-8d25-e2108db06522/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"EPA","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"2015","temporal_frequency":"NA","update_detail":"modify","update_frequency":"irregular - several times a year","update_type":"Dynamic","variables":["over 600 metrics that include local catchment, watershed, and special metrics"],"vars":"over 600 metrics that include local catchment, watershed, and special metrics","weight":1},{"access":[{"file_format":"ZARR","name":"hytest-org.github.io","url":"https://hytest-org.github.io/hytest/dataset_access/CONUS404_ACCESS.html"},{"file_format":"NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/64f77acad34ed30c20544c18"},{"file_format":"ZARR","name":"WMA STAC","url":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/conus404-biasadjusted"}],"access_details":null,"bbox":{"east":-63.1184,"north":52.898,"south":20.1149,"west":-131.1649},"citation":"Zhang, Y., Grim, J., Cabell, R., Srivastava, I., Gochis, D., Prein, A., Rasmussen, R., Ikeda, K., and Schneider, T., 2024, CONUS404 climate forcing variable subset for hydrologic models, 1979-2022: downscaled to 1 km and bias-adjusted for precipitation and temperature: U.S. Geological Survey data release, https://doi.org/10.5066/P9JE61P7.","creator":[],"creator_project":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"}],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This dataset is a bias-adjusted version of a subset of variables in the [CONUS404](/datasets/68d031e8-4101-4cdf-952a-657396e92a5a/) dataset. The CONUS404 BA output contain downscaled hourly data for U component of wind, V component of wind, downward longwave radiation, downward shortwave radiation, air temperature, water vapor mixing ratio, surface pressure, and accumulated precipitation variables. The air temperature and accumulated precipitation variables were both downscaled and bias adjusted while the remaining variables were just downscaled. This is a dataset of historical conditions (water years 1980-2022, October 1, 1979-September 30, 2022) and has sufficient temporal and spatial detail to resolve mesoscale atmospheric processes, making it appropriate for forcing hydrological models and conducting meteorological analyses.\u003cbr\u003eThis dataset was prepared for use in a WRF-Hydro simulation run for use in the National Water Availability Assessment.","doi_url":"https://doi.org/10.5066/P9JE61P7","domain":["Climate"],"draft":false,"id":"f930b17a-c6ea-4623-b89d-d7d85ac698aa","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"68d031e8-4101-4cdf-952a-657396e92a5a","rel_type":"IsDerivedFrom"},{"id":"9d0a986c-c1a6-4e21-87e7-188ea8d0a636","rel_type":"IsVariantFormOf"},{"id":"3a2ece15-f510-473e-82e5-7b23d4b37fed","rel_type":"IsSourceOf"},{"id":"ff36c37c-6714-4d18-97a1-c5cd3211b275","rel_type":"IsSourceOf"},{"id":"5cf8cd14-0c2d-4918-9850-7d7c244c1530","rel_type":"IsSourceOf"},{"id":"64f51777-01dd-48f5-a4ed-f2d75a51ce6b","rel_type":"IsSourceOf"},{"id":"9ceb4994-f7f4-42c1-9e2b-b3a74f5d3b58","rel_type":"IsSourceOf"},{"id":"2c95bc77-cb0b-4965-ac7a-ef2cf44867bd","rel_type":"IsSourceOf"},{"id":"93537c0d-2ce7-4f61-9465-3db8ab8c72be","rel_type":"IsSourceOf"},{"id":"b2fc46a1-18db-4370-8515-a5ae6912a2ba","rel_type":"IsSourceOf"}],"linked_tools":[],"linked_usecases":[{"id":"148a7db7-1175-47aa-8437-5ae39f62b1ce","rel_type":"IsSourceOf"}],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/64f77acad34ed30c20544c18?f=__disk__6e%2Fd1%2F1d%2F6ed11de6e4b99be2484e25a13c02314be818829f\u0026allowOpen=true"}],"name":"CONUS404 BA: CONUS404 climate forcing variable subset for hydrologic models, 1979-2022: downscaled to 1 km and bias-adjusted for precipitation and temperature","permalink":"/catalog/datasets/f930b17a-c6ea-4623-b89d-d7d85ac698aa/","project_use_history":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"project_using":[{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"}],"ref_fabric":false,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1 kilometer","temporal_coverage":"1979 - 2022","temporal_frequency":"hourly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["U2D","V2D","LWDOWN","RAINRATE","T2D","Q2D","PSFC","SWDOWN","x","y","time","Reference_time","conus_precip_bias_corr_day_1km","conus404_daymet_T_bias_corr","x","y","crs"],"vars":"U2D; V2D; LWDOWN; RAINRATE; T2D; Q2D; PSFC; SWDOWN; x; y; time; Reference_time; conus_precip_bias_corr_day_1km; conus404_daymet_T_bias_corr; x; y; crs","weight":1},{"access":[{"file_format":"GPKG; CSV; JSON","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/682cd1b0d4be021a0d6b77b7"}],"bbox":{"east":-17.68,"north":81.5,"south":-25.68,"west":142.8},"citation":"Blodgett, D.L., 2025, Comparison of four snapshots of Watershed Boundary Dataset Twelve-Digit Hydrologic Units: U.S. Geological Survey data release, https://doi.org/10.5066/P1HR67C5.","creator":[{"creator_email":"dblodgett@usgs.gov","creator_name":"David L Blodgett"}],"creator_project":[{"id":"DJ50VG3","name":"NHGF Model Fabric: National Hydrologic Geospatial Fabric Model Fabric"}],"date_created":"1/15/2026","date_updated":"6/3/2026","description":"This data release contains a registry and comparison of the representation of twelve-digit hydrologic unit code (HUC) hydrologic unit polygons from four snapshots of the Watershed Boundary Dataset (WBD). The four snapshots were chosen due to their presence in broadly: National Hydrologic Dataset Plus V2.1 (nhdpv2) and National Hydrologic Dataset Plus High Resolution (nhdphr) products, use in the 2025 National Integrated Water Availability Assessment (niwaa), and the importance of the 2025 Watershed Boundary Dataset containing the final version of the Coterminous United States WBD (final). They are identified as “nhdpv2”, “nhdphr”, “niwaa”, and “final” respectively. Note that the niwaa WBD snapshot for the 2025 assessment is from 2020. File Summary: - The registry JSON file contains only the HUC identifier and date it was last edited for each of the four WBD snapshots. - The GPKG file contains the most recently geometry from one of the four snapshots for every HUC identifier in the registry. Note that polygons in this GPKG are the most recent and may overlap HUCs from earlier snapshots. It also contains attributes indicating whether a HUC was deprecated, which snapshots it exists in, and whether it changed from one snapshot to the next. - Four comma separated files, one for each snapshot comparison, contain polygon area attributes for the both the entire HUC polygon from each snapshot and each partial polygon implied by the spatial intersection of the compared pair of snapshots. Comparisons were performed for four pairs of snapshots: 1) nhdpv2 to nhdphr, 2) nhdpv2 to niwaa, 3) niwaa to final, and 4) nhdphr to final. The nhdphr snapshot is composed of several regional subset snapshots taken over the period that nhdphr was being produced. In contrast, the niwaa snapshot is a complete snapshot of the WBD taken at a single point in time. Due to this, some hydrologic units defined in the nhdphr snapshot are older than those in the niwaa snapshot and others are newer. Because of this overlap, direct comparison of the nhdphr and niwaa snapshots was not possible. If additional comparisons not presented in this data release are required, the script used to generate the comparisons is included as an ancillary file.","doi_url":"https://doi.org/10.5066/P1HR67C5","domain":["Hydrology"],"draft":false,"id":"f9c21418-4652-473d-b825-7a0716fc1490","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/682cd1b0d4be021a0d6b77b7?f=__disk__34%2F48%2Fcd%2F3448cd48688d1e4e458a704f2336a3c42e042846\u0026allowOpen=true"}],"name":"WBD Comparison: Comparison of four snapshots of Watershed Boundary Dataset Twelve-Digit Hydrologic Units","permalink":"/catalog/datasets/f9c21418-4652-473d-b825-7a0716fc1490/","project_use_history":[],"project_using":[],"ref_fabric":true,"release_status":"Released","source":"USGS","spatial_extent":"United States","spatial_resolution":"varies","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["HUC12","date","huc12","nhdpv2","nhdphr","niwaa","final","date","latest","deprecated","nhdpv2_nhdphr_changed","nhdpv2_niwaa_changed","nhdphr_final_changed","niwaa_final_changed","geom","nhdpv2","niwaa","nhdpv2_area_sqkm","niwaa_area_sqkm","part_area_sqkm","nhdpv2","nhdphr","nhdpv2_area_sqkm","nhdphr_area_sqkm","part_area_sqkm","niwaa","final","niwaa_area_sqkm","final_area_sqkm","part_area_sqkm","nhdphr","final","nhdphr_area_sqkm","final_area_sqkm","part_area_sqkm"],"vars":"HUC12; date; huc12; nhdpv2; nhdphr; niwaa; final; date; latest; deprecated; nhdpv2_nhdphr_changed; nhdpv2_niwaa_changed; nhdphr_final_changed; niwaa_final_changed; geom; nhdpv2; niwaa; nhdpv2_area_sqkm; niwaa_area_sqkm; part_area_sqkm; nhdpv2; nhdphr; nhdpv2_area_sqkm; nhdphr_area_sqkm; part_area_sqkm; niwaa; final; niwaa_area_sqkm; final_area_sqkm; part_area_sqkm; nhdphr; final; nhdphr_area_sqkm; final_area_sqkm; part_area_sqkm","weight":1},{"access":[{"file_format":"JSON","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5e8d2b5982cee42d13466001"}],"access_details":null,"bbox":{"east":-67.1281,"north":48.9932,"south":28.9584,"west":-124.7227},"citation":"Wieferich, D.J., Daniel, W.M., Procopio, J.M., and Morningstar, C.R., 2020, Waterfalls and Rapids in the Conterminous United States Linked to the National Hydrography Datasets V2.0, U.S. Geological Survey data release, https://doi.org/10.5066/P9QQTKA0","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This GeoJSON dataset contains information about 10780 waterfall and 1080 rapid locations (referred to as falls throughout the metadata) and characteristics (e.g. type and height) for the conterminous United States. This dataset centralizes known information about falls while providing basic quality control (i.e. resolving duplicate records and spatial accuracy checks) and linkages to stream networks intended to facilitate stream network analyses. Locations of falls were sourced from the World Waterfall Database (WWD, www.worldwaterfalldatabase.com), the US Forest Service Center for Aquatic Technology Transfer (acquired from Southeast Aquatic Barrier Inventory), and Geographic Names Information System (GNIS, https://geonames.usgs.gov). The coordinates and spatial attributes from source data were used to verify locations using The HydroLink Tool (https://maps.usgs.gov/hydrolink). The HydroLink Tool was also used to address locations (similar to the concept of street addresses) to both the National Hydrography Dataset Plus Medium Resolution Version 2.1 (1:100,000 scale)(NHDPlusV2.1) and National Hydrography Dataset High Resolution (1:24,000 scale)(NHD HR) geospatial stream networks. The development of this dataset did not impose strict fall definitions but instead compiled qualified falls as defined by sources while capturing characteristics when available to help users identify falls of interest for any given use case. More specifically there is a gradation of waterfalls represented in the dataset where general fall types are defined by source datasets but are not standardized by USGS staff.","doi_url":"https://doi.org/10.5066/P9QQTKA0","domain":["Hydrology","Stream Characteristics"],"draft":false,"id":"faebebe5-b0f3-4fce-9707-ec64f93ea396","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/5e8d2b5982cee42d13466001?f=__disk__84%2F96%2Fe1%2F8496e10c81bb6a5b1232c79abc2b1a9a57f79483\u0026allowOpen=true"}],"name":"Waterfalls and Rapids in the Conterminous United States Linked to the National Hydrography Datasets V2.0","permalink":"/catalog/datasets/faebebe5-b0f3-4fce-9707-ec64f93ea396/","project_use_history":[],"project_using":[],"ref_fabric":true,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"1:24,000; 1:100,000","temporal_coverage":"2014 - 2020","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Waterfalls","Rapids"],"vars":"Waterfalls; Rapids","weight":1},{"access":[{"file_format":"SHP; CSV; TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/64135576d34eb496d1ce3d2e"}],"access_details":null,"bbox":{"east":-65.0391,"north":49.838,"south":24.5271,"west":-127.793},"citation":"Reitz, M., Sanford, W.E., and Saxe, S.W., 2023, Historical Evapotranspiration for the Conterminous U.S.: U.S. Geological Survey data release, https://doi.org/10.5066/P9EZ3VAS","creator":[{"creator_email":"mreitz@usgs.gov","creator_name":"Meredith Reitz"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This data release provides rasters of actual evapotranspiration (ET) at the Conterminous U.S. (CONUS) scale from October 1895 to September 2018. Data are provided at the annual and monthly time scales at 800 meter spatial resolution. The dataset was produced using ensemble estimation methods described in the associated journal article. The data release also includes associated datasets developed in the production of these ET estimates, including monthly maps of groundwater and surface water irrigation from 1980-2018, as well as data underlying the figures in the associated paper.","doi_url":"https://doi.org/10.5066/P9EZ3VAS","domain":["Climate","Hydrology"],"draft":false,"id":"fc95c6a7-04b9-44cf-bf09-5522f164c704","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/64135576d34eb496d1ce3d2e?f=__disk__70%2Fa9%2F8d%2F70a98dad64b332cb8ab4e50ddfc9045580ba94b4\u0026allowOpen=true"},{"name":"Documentation","url":"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR034012"}],"name":"Historical evapotranspiration","permalink":"/catalog/datasets/fc95c6a7-04b9-44cf-bf09-5522f164c704/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"800 meter","temporal_coverage":"1895 - 2018","temporal_frequency":"monthly; annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Actual evapotranspiration (ET)"],"vars":"Actual evapotranspiration (ET)","weight":1},{"access":[{"file_format":"TIF","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/647626cbd34e4e58932d9d4e"}],"access_details":null,"bbox":{"east":-63.6722,"north":52.851,"south":21.7423,"west":-130.2328},"citation":"Dewitz, J., 2023, National Land Cover Database (NLCD) 2021 Products: U.S. Geological Survey data release, https://doi.org/10.5066/P9JZ7AO3.","creator":[{"creator_email":"mrlc@usgs.gov","creator_name":"land cover mapping team"}],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"The U.S. Geological Survey (USGS), in partnership with several federal agencies, has now developed and released seven National Land Cover Database (NLCD) products: NLCD 1992, 2001, 2006, 2011, 2016, 2019, and 2021. Beginning with the 2016 release, land cover products were created for two-to-three-year intervals between 2001 and the most recent year. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. NLCD continues to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database. NLCD 2021 adds an additional year to the map products produced for NLCD 2019, with a streamlined compositing process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a theme-based post-classification protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and a scripted operational system. The overall accuracy of the 2019 Level I land cover was 91%. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2021 operational mapping (see https://doi.org/10.1080/15481603.2023.2181143 for the latest accuracy assessment publication). Questions about the NLCD 2021 land cover product can be directed to the NLCD 2021 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.","doi_url":"https://doi.org/10.5066/P9JZ7AO3","domain":["Land Cover"],"draft":false,"id":"fcbe8a2f-5e09-4cd6-9fd5-23e9df1fc09c","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.mrlc.gov/downloads/sciweb1/shared/mrlc/metadata/nlcd_2021_land_cover_l48_20230630.xml"},{"name":"Documentation","url":"https://www.usgs.gov/centers/eros/science/national-land-cover-database"}],"name":"National Land Cover Database (NLCD) 2021 Products","permalink":"/catalog/datasets/fcbe8a2f-5e09-4cd6-9fd5-23e9df1fc09c/","project_use_history":[],"project_using":[],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"MRLC","spatial_extent":"CONUS","spatial_resolution":"30 meter","temporal_coverage":"2001; 2004; 2006; 2008; 2011; 2013; 2016; 2019; 2021","temporal_frequency":"annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Unclassified","Open Water","Perennial Ice/Snow","Developed, Open Space","Developed, Medium Intensity","Developed, High Intensity","Barren Land (Rock/Sand/Clay)","Deciduous Forest","Evergreen Forest","Mixed Forest","Dwarf Scrub","Shrub/Scrub","Grassland/Herbaceous","Sedge/Herbaceous","Lichens","Moss","Pasture/Hay","Cultivated Crops","Woody Wetlands","Emergent Herbaceous Wetlands","Impervious surface"],"vars":"Unclassified; Open Water; Perennial Ice/Snow; Developed, Open Space; Developed, Medium Intensity; Developed, High Intensity; Barren Land (Rock/Sand/Clay); Deciduous Forest; Evergreen Forest; Mixed Forest; Dwarf Scrub; Shrub/Scrub; Grassland/Herbaceous; Sedge/Herbaceous; Lichens; Moss; Pasture/Hay; Cultivated Crops; Woody Wetlands; Emergent Herbaceous Wetlands; Impervious surface","weight":1},{"access":[{"file_format":"GDB","name":"usgs.gov","url":"https://www.usgs.gov/national-hydrography/nhdplus-high-resolution"},{"file_format":"GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/57645ff2e4b07657d19ba8e8"}],"access_details":null,"bbox":{"east":-64,"north":72,"south":10,"west":-179},"citation":"Moore, R.B., McKay, L.D., Rea, A.H., Bondelid, T.R., Price, C.V., Dewald, T.G., and Johnston, C.M., 2019, User's guide for the national hydrography dataset plus (NHDPlus) high resolution: U.S. Geological Survey Open-File Report 2019-1096, 66 p., https://doi.org/10.3133/ofr20191096","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"NHDPlus HR is a national hydrography dataset of surface water features, including stream segments and their associated elevation-derived catchments, that form a seamless national network of streams. In addition to surface water features, this dataset also includes mean annual streamflow estimates based on the Enhanced Unit Runoff method (EROM), mean annual velocity, hydrologic sequencing, stream order, stream slope, stream elevation, cumulative drainage area, flow withdrawals, transfers, and augmentation, and a group of \u003ca href='https://www.usgs.gov/national-hydrography/value-added-attributes-vaas' target='_blank'\u003eattributes\u003c/a\u003e that enable rapid stream network navigation. NHDPlus HR catchments can be used to associate other landscape attributes, such as land cover, with stream segments.\u003cbr\u003eNHDPlus HR integrates stream network features from the high-resolution National Hydrography Dataset (NHD) with 10 meter gridded land surface elevation data from the 3D Elevation Program (3DEP), and hydrologic unit boundaries from the National Watershed Boundary Dataset (WBD). Since NHDPlus HR is produced from static snapshots of these three datasets, it includes the features of these ingredient datasets as well.\u003cbr\u003eThis is an updated, high-resolution version of the [NHDPlus dataset](/datasets/8a60b6b4-d785-4265-af99-cd1870ea7928) that is actively being developed and updated.","doi_url":"https://doi.org/10.3133/ofr20191096","domain":["Hydrology"],"draft":false,"id":"fd9b5188-5336-4cfd-9c7b-70680001eef0","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[],"name":"NHDPlus HR: National Hydrography Dataset Plus - High Resolution Data","permalink":"/catalog/datasets/fd9b5188-5336-4cfd-9c7b-70680001eef0/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; HI; PR; GU","spatial_resolution":"1:24,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":"append and modify","update_frequency":"irregular","update_type":"Dynamic","variables":["Stream segments","Catchments","Streamflow, mean annual","Stream velocity, mean annual","Hydrologic sequencing","Stream order","Stream slope","Stream elevation","Cumulative drainage area","Flow withdrawals","Flow transfers","Flow augmentation"],"vars":"Stream segments; Catchments; Streamflow, mean annual; Stream velocity, mean annual; Hydrologic sequencing; Stream order; Stream slope; Stream elevation; Cumulative drainage area; Flow withdrawals; Flow transfers; Flow augmentation","weight":1},{"access":[{"file_format":"GDB","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/5e29b87fe4b0a79317cf7df5"}],"access_details":null,"bbox":{"east":-63.279,"north":54.6729,"south":22.338,"west":-129.4134},"citation":"Bock, A.E, Santiago,M., Wieczorek, M.E., Foks, S.S., Norton, P.A., and Lombard, M.A., 2020, Geospatial Fabric for National Hydrologic Modeling, version 1.1 (ver. 3.0, November 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P971JAGF.","creator":[],"creator_project":[],"date_created":"1/1/2000","date_updated":"6/3/2026","description":"This U.S. Geological Survey (USGS) data release consists of two hydrographic datasets with spatial modeling units, two sets of spatial data consistent with the National Hydrologic Model (NHM) Geospatial Fabric for National Hydrologic Modeling (abbreviated within this document as GFv1, Viger and Bock, 2014), and a database of 118 parameters used to run the NHM . These datasets are found as subpages to the ScienceBase landing page as 1) the GIS (geographic information system) features of the United States-Canada Transboundary Geospatial Fabric (TGF, added 08/04/2020), 2) the GIS features of the Geospatial Fabric v1.1 (GFv1.1 or v1_1, added 08/04/2020) which is an update to the GF and includes the TGF, 3) Topographic derivative datasets for the United States-Canada transboundary Geospatial Fabric (added 10/28/2020), 4) Data Layers for the National Hydrologic Model, version 1.1, and 5) National Hydrologic Model's United States-Canada Transboundary Geospatial Fabric Parameter Database (added 11/10/2021).","doi_url":"https://doi.org/10.5066/P971JAGF","domain":["Hydrology"],"draft":false,"id":"fdcb1754-5806-4307-b550-8754a8577113","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"ae9546b9-1eda-407a-9129-8a62b43ac056","rel_type":"IsSourceOf"},{"id":"5cbfe2bf-8e88-476b-9c77-db11f91c8e0d","rel_type":"IsPreviousVersionOf"}],"linked_tools":[{"id":"46c6b85f-0c01-4c71-8572-7ab05b20ab0e","rel_type":"IsSourceOf"},{"id":"dbbc518b-32fc-4de9-9c9b-7da6f2b3dd6c","rel_type":"IsSourceOf"},{"id":"14b6eb88-06dc-4152-b37f-a355c850bc7e","rel_type":"IsSourceOf"}],"linked_usecases":[],"links":[],"name":"Geospatial Fabric for National Hydrologic Modeling, version 1.1","permalink":"/catalog/datasets/fdcb1754-5806-4307-b550-8754a8577113/","project_use_history":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ60U5B","name":"PUMP: Predictive Understanding of Multiscale Processes"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":true,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS; parts of Canada","spatial_resolution":"1:100,000","temporal_coverage":"NA","temporal_frequency":"NA","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Points of interest","Flowpaths","Catchments","Data layers for the National Hydrologic Model v1.1","GIS features of the geospatial fabric for the National Hydrologic Model v1.1","GIS features of the transboundary geospatial fabric (TGF)","National Hydrologic Model's United States-Canada transboundary geospatial fabric parameter database","Topographic derivative datasets for the United States-Canada transboundary geospatial fabric"],"vars":"Points of interest; Flowpaths; Catchments; Data layers for the National Hydrologic Model v1.1; GIS features of the geospatial fabric for the National Hydrologic Model v1.1; GIS features of the transboundary geospatial fabric (TGF); National Hydrologic Model's United States-Canada transboundary geospatial fabric parameter database; Topographic derivative datasets for the United States-Canada transboundary geospatial fabric","weight":1},{"access":[{"file_format":"CSV","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/63adc826d34e92aad3ca5af4"}],"access_details":null,"bbox":{"east":-66.0938,"north":48.9225,"south":24.687,"west":-124.9805},"citation":"Galanter, A.E., Gorman Sanisaca, L.E., Skinner, K.D., Harris, M.A., Diehl, T.H., Chamberlin, C.A., McCarthy, B.A., Halper, A.S., Niswonger, R.G., Stewart, J.S., Markstrom, S.L., Embry, I., and Worland, S., 2023, Thermoelectric-power water use reanalysis for the 2008-2020 period by power plant, month, and year for the conterminous United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9ZE2FVM","creator":[],"creator_project":[{"id":"DJ50UY1","name":"Water Use Model Development"}],"date_created":"5/10/2024","date_updated":"6/3/2026","description":"Previous work by the U.S. Geological Survey (USGS) developed models to estimate the amount of water that is withdrawn and consumed by thermoelectric power plants (Diehl and others, 2013; Diehl and Harris, 2014; Harris and Diehl, 2019 [full citations listed in srcinfo of the metadata file]). This data release presents a historical reanalysis of thermoelectric water use from 2008 to 2020 and includes monthly and annual water withdrawal and consumption estimates, thermodynamically plausible ranges of minimum and maximum withdrawal and consumption estimates, and associated information for 1,360 water-using, utility-scale thermoelectric power plants in the United States.  The term “reanalysis” refers to the process of reevaluating and recalculating water-use data using updated or refined methods, data sources, models, or assumptions. For this case, new estimates of withdrawal and consumption were made using new data sources and methods which involved taking existing historical data and subjecting it to a thorough review and revision to improve accuracy, completeness, and consistency. Reanalysis included incorporating new datasets, refining methodologies, and adjusting for changes in technology, regulations, or knowledge. The goal of reanalysis was to provide more accurate and up-to-date water-use estimates that reflects the most current understanding of water-use patterns and factors affecting water usage in the United States. This historical reanalysis was completed by running thermoelectric water-use models that are based on linked heat-and-water budgets (models contained within this data release). The linked heat-and-water budgets are constrained by the following data (also contained within this data release): power plant generation and cooling system technologies, the quantity of fuels consumed and electricity generated, as well as environmental variables. The heat-budget component of the models calculates the amount of waste heat (fuel heat that is not converted to electricity) that is removed from the steam used to drive the turbines that generate electricity. The waste heat is transferred to the cooling system in a thermoelectric power plant’s condenser, which is defined as the condenser duty (Diehl and others, 2013). The water-budget component of the models calculates the amount of water that is withdrawn and consumed based on plant-specific condenser duty, and environmental variables (air temperatures, water temperatures, wind speed, and elevation). The models were updated using the same formulation previously developed (Diehl and others, 2013) and updates include enhancements of automatic data collectors, nationally consistent and operational environmental variables, and simulated water temperatures for plant intakes provided by the USGS National Hydrologic Model (Regan and others, 2018; Hay and others, 2023). These new features enable reproducibility and are an important step toward an operational modeling framework for making nationally consistent historical and forecasted future water-use estimates that are independent of Federal plant-operator reported water withdrawal and consumption data. Total estimated water withdrawal (including fresh and saline sources) ranged from 132 billion gallons per day (Bgal/d) in 2008 to 80 Bgal/d in 2020. Total estimated water consumption (including only fresh sources; consumption at coastal saline plants was not modeled) ranged from 3.6 Bgal/d in 2008 to 2.7 Bgal/d in 2020. Gorman Sanisaca and others, 2023, provides monthly condenser duty estimates and associated information from 2008 to 2020 that are used by the models reported here for estimating withdrawals and consumption.","doi_url":"https://doi.org/10.5066/P9ZE2FVM","domain":["Water Use"],"draft":false,"id":"fe01735b-62b0-413e-8942-2d977d9a768a","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[],"linked_tools":[],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/63adc826d34e92aad3ca5af4?f=__disk__da%2Fd9%2F78%2Fdad9784363a86225e69035d9b8c7ef7a8d99da02\u0026allowOpen=true#Spatial%20Reference%20Information"}],"name":"Thermoelectric-power water use reanalysis for the 2008-2020 period by power plant, month, and year for the conterminous United States","permalink":"/catalog/datasets/fe01735b-62b0-413e-8942-2d977d9a768a/","project_use_history":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_quarter":null,"release_status":"Released","source":"USGS","spatial_extent":"CONUS","spatial_resolution":"unknown","temporal_coverage":"2008 - 2020","temporal_frequency":"monthly; annual","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["Plausible consumption","Estimated withdrawal","C","n","Range","Approach_600","Approach_3500","WBT","LG","Plant.Code","YEAR","gw_municipal","pond","w_tmp_C","month","t_source","state","water_source","gsf_v11_id","prms_id","elev_ft","pond_acres","latitude","longitude","huc_12","Tdb","Twb","cooling","percentAllocation","CD_MMBtu","DB_C","WB_C","WS_mph","EV_in","WT_C","Plant.Name","County","Name.of.Water.Source","coolingType","ModelType","Plant.level_dom_fuel","general_mover","Net.Generation.Year.To.Date","cu_mgd","cu_lower_mgd","cu_upper_mgd","wd_mgd","wd_lower_mgd","wd_upper_mgd"],"vars":"Plausible consumption; Estimated withdrawal; C; n; Range; Approach_600; Approach_3500; WBT; LG; Plant.Code; YEAR; gw_municipal; pond; w_tmp_C; month; t_source; state; water_source; gsf_v11_id; prms_id; elev_ft; pond_acres; latitude; longitude; huc_12; Tdb; Twb; cooling; percentAllocation; CD_MMBtu; DB_C; WB_C; WS_mph; EV_in; WT_C; Plant.Name; County; Name.of.Water.Source; coolingType; ModelType; Plant.level_dom_fuel; general_mover; Net.Generation.Year.To.Date; cu_mgd; cu_lower_mgd; cu_upper_mgd; wd_mgd; wd_lower_mgd; wd_upper_mgd","weight":1},{"access":[{"file_format":"NC","name":"sciencebase.gov","url":"https://www.sciencebase.gov/catalog/item/66bcb86cd34e03388281a92e"}],"bbox":{"east":-63.1184,"north":52.898,"south":20.1149,"west":-131.1649},"citation":"RafieeiNasab, A., Zhang, Y., Dugger, A., Mazrooei, A., Gochis, D., Srivastava, I., Omani, N., Grim, J., Casali, M., Sampson, K., and LaFontaine, J., 2026, Application of the WRF-Hydro Modeling System for the Conterminous United States Using the Bias Adjusted Version of the CONUS404 Atmospheric Forcings (CONUS404BA), Water Years 1980-2022: U.S. Geological Survey data release, https://doi.org/10.5066/P13ADWKZ.","creator":[],"creator_project":[{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"}],"date_created":"1/20/2026","date_updated":"6/8/2026","description":"This data release contains inputs for and outputs from a hydrologic simulation for the conterminous United States (CONUS) using the WRF-Hydro modeling system version 5.2.0 (Gochis and others, 2020). This simulation was developed to provide water budget estimates for the period 10/1/1979 to 9/30/2022 using the bias adjusted version of the CONUS404 (CONUS404BA) atmospheric forcings dataset (Zhang and others, 2024). The WRF-Hydro model input files are included within this data release and consist of two configuration files, two simulation restart files, nine parameter files, and six types of output files. Each output type has a file for every hour of the model simulation, except for the LDASOUT and RTOUT files, which are output every three hours. The WRF-Hydro model has been executed with two different lake/reservoir routing options: 1) Pass Through - which aggregates the inflows coming to the lake and placing it as the lake outflow (Qin = Qout), representing roughly a natural flow behavior; and 2) Level Pool - using level pool lake/reservoir routing techniques in the presence of a waterbody on the channel network. Therefore, for the lakes and channel outputs, there exists two sets of data. The Entity and Attribute element of the metadata file contains the data descriptions for all the variables in each of the six types of output files. Please refer to the Supplemental Information element of this metadata record for further information on this model application. A schematic of the directory and file structure of the model archive is described by the model_archive_directory_structure.txt file included in this data release below. All model files are archived on the U.S. Geological Survey's Open Storage Network pod and can be accessed using the instructions provided in the Dataset Access section of the U.S. Geological Survey's HyTEST JupyterBook (https://hytest-org.github.io/hytest/doc/About.html#). The report that describes the development of this model application is provided in the External Resources (RafieeiNasab and others, 2024a). A previously published model archive for an 11-year WRF-Hydro simulation for the period 2010 to 2021 which is described in RafieeiNasab and others (2024a) is provided in the External Resources below (RafieeiNasab and others, 2024b).","doi_url":"https://doi.org/10.5066/P13ADWKZ","domain":["Climate","Hydrology"],"draft":false,"id":"ff36c37c-6714-4d18-97a1-c5cd3211b275","iscjklanguage":false,"lastmod":"2026-06-16T21:16:44Z","linked_datasets":[{"id":"f930b17a-c6ea-4623-b89d-d7d85ac698aa","rel_type":"IsDerivedFrom"}],"linked_tools":[{"id":"a69ed792-02ac-4709-9f33-b61848d73ae8","rel_type":"IsDerivedFrom"}],"linked_usecases":[],"links":[{"name":"Metadata","url":"https://www.sciencebase.gov/catalog/file/get/66bcb86cd34e03388281a92e?f=__disk__f5%2Fff%2Fae%2Ff5ffae73b69c3cf0c613ec491244f1c8224b6fbe\u0026allowOpen=true"}],"name":"Application of the WRF-Hydro Modeling System for the Conterminous United States Using the Bias Adjusted Version of the CONUS404 Atmospheric Forcings (CONUS404BA), Water Years 1980-2022","permalink":"/catalog/datasets/ff36c37c-6714-4d18-97a1-c5cd3211b275/","project_use_history":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"project_using":[{"id":"DJ50UM7","name":"National IWAAs: National Integrated Water Availability Assessment"},{"id":"DJ60U30","name":"HyTEST: Hydro-Terrestrial Earth System Testbed"},{"id":"DJ60UX3","name":"MAPPNAT: Model Application for the National IWAAs and NWC"},{"id":"DJ50U2N","name":"IWAAs Trends and Drivers (Phase 2)"}],"ref_fabric":false,"release_status":"Released","source":"USGS; NCAR","spatial_extent":"CONUS","spatial_resolution":"1:100,000","temporal_coverage":"1980 - 2022","temporal_frequency":"hourly","update_detail":null,"update_frequency":null,"update_type":"Static","variables":["crs","elevation","feature_id","latitude","longitude","order","reference_time","streamflow","time","qBtmVertRunoff","qBucket","qSfcLatRunoff","q_lateral","velocity","depth","inflow","outflow","reservoir_type","reservoir_assimilated_value","water_sfc_elev","ACCET, ACSNOM, ACSNOW, ALBEDO","ALBSND","ALBSNI","COSZ","EDIR","FIRA","FSA","FSNO","HFX","LH","QRAIN","QSNOW","SNEQV","SNOWH","SOIL_M","SOIL_W","TRAD","UGDRNOFF","x","y","sfcheadsubrt","zwattablrt"],"vars":"crs; elevation; feature_id; latitude; longitude; order; reference_time; streamflow; time; qBtmVertRunoff; qBucket; qSfcLatRunoff; q_lateral; velocity; depth; inflow; outflow; reservoir_type; reservoir_assimilated_value; water_sfc_elev; ACCET, ACSNOM, ACSNOW, ALBEDO; ALBSND; ALBSNI; COSZ; EDIR; FIRA; FSA; FSNO; HFX; LH; QRAIN; QSNOW; SNEQV; SNOWH; SOIL_M; SOIL_W; TRAD; UGDRNOFF; x; y; sfcheadsubrt; zwattablrt","weight":1}]