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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

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

Domain: Hydrology; Snow; Climate

Variables:
▶ rain_surface; mix_surface; melt_surface; input_seasonality; precipitation_seasonality;
See 6 More Variables...

Spatial Resolution: 4 kilometer

Temporal Frequency: daily; 3 months; annual

Temporal Coverage: 1990 - 2023

Spatial Extent: CONUS

Source: USGS

Update Type: Static

Update Frequency: NA

Update Detail: NA

Access:

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).

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 .