Skip to main content

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Beta

Catalog datasets represent a fraction of data in use by active WMA projects. The team is working on including more datasets to provide a complete view of data-in-use. Let us know how your experience is!

Datasets

MODFLOW 6 models used to simulate the long-term average surficial groundwater system for the contiguous United States

Domain: Hydrology; Hydrogeology

Variables: Recharge; Surficial effective transmissivity; Depth-to-the water table

Spatial Resolution: 250 meter

Temporal Frequency: NA

Temporal Coverage: 1985 - 2015

Spatial Extent: CONUS

Source: USGS

Update Type: Static

Update Frequency: NA

Update Detail: NA

Access:

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 ) .

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