These vector data represent the 8-digit hydrologic unit (HU8) polygons used to define the raster processing extent in the Nevada StreamStats study area.

This data release contains vector and raster geospatial data for delineating drainage basins in the Nevada StreamStats study area. Included are vector streamline, waterbody, and inner wall data used to hydrologically condition the digital elevation model. The hydrologically-conditioned digital elevation model, flow direction, flow accumulation, and stream delineation raster data are also included.

These raster data represent spatially distributed elevation values in the Nevada StreamStats study area for the period of 2024.

These raster data represent spatially distributed slope percentages in the Nevada StreamStats study area for the period of 2024.

This streamgage boundary layers dataset consists of 512 USGS streamgage point locations and polygons delineated to show their drainage areas. The drainage areas were delineated from the streamgage points functioning as pour points, in addition to the Flow Accumulation Raster for Maine StreamStats and Flow Direction Raster for Maine StreamStats in from this data release.

This dataset was produced by the U.S. Geological Survey (USGS) in cooperation with the Wyoming Water Development Office for the purpose of delineating drainage basins and performing hydrologic analysis in the Wyoming StreamStats application. The data are raster representations of the fundamental dataset layers necessary for the functionality of StreamStats application within the Wyoming StreamStats study area. The StreamStats application provides access to spatial analytical tools that are useful for water-resources planning and management, and for engineering and design purposes. The map-based user interface can be used to delineate drainage areas, calculate basin characteristics, and estimates of flow statistics, and more. The study boundary for data in this data release is defined by the extent of all HUC08 watersheds flowing in or out of the state of Wyoming.

This data release contains raster geospatial data for characterizing drainage basins in the Nevada StreamStats study area. Included are precipitation, temperature, elevation, slope, and land cover data.

This vector data set represents the inner wall polylines used to hydrologically condition the digital elevation model within the Louisiana StreamStats study area.

This is a 1:2,000,000 coverage of streams for the conterminous United States.

We projected future streamflow outcomes arising from climate change for the southwestern United States during the 21st century due to climate change under two possible greenhouse gas concentration pathways (RCP4.5 and 8.5). The results inform water managers about the future risks of drought in their water resource regions by providing bounds on the possible locations and extents of streamflow loss. To get to these results, we used downscaled future and historical climate data from seven models to drive a new, calibrated SPAtially Referenced Regression On Watershed attributes (SPARROW) streamflow model (Wise and others, 2019, Miller and others, 2020). Temperature and precipitation data come from the NASA Earth Exchange (NEX) Downscaled Climate Projections (NEX-DCP30, Thrasher and others, 2013 and Thrasher and others, 2015), and actual and potential evapotranspiration come from the NEX-DCP30 temperature and precipitation used in the Monthly Water Balance Model (MWBM, Hostetler and Alder, 2016 and Alder, 2017a,b,c). This data set comprises climate data preprocessing code to convert the gridded, monthly-scale climate data to reach scale multidecadal averages for the intervals 1975-2005, 2020-2049, 2040-2069 and 2070-2099, the model input (data1) and model control files, the model code, model results files, and code to post-process and analyze the streamflow model results. The raw climate data (NEX-DCP30, MWBM), and SPARROW model calibration documentation are publicly available elsewhere and are cross linked with this data release (see crossref section). The full data preparation, modeling, and analysis methods, as well as results are described in Miller and others, (2021)