Skip to main content

Person

Samuel W Saxe

thumbnail
This is accompanying data produced for the study "Implications of Model Selection: Inter-Comparison of Publicly-Available, CONUS-Extent Hydrologic Component Estimates". These datasets were converted from their primary structures (rasters and shapefiles) to EPA Ecoregions Level I. Conversion was performed by averaging timestep layers via mean area weight to produce a single vector of monthly values for each ecoregion, for each of the following hydrologic cycle components: precipitation (P), actual evapotranspiration (AET), runoff (R), snow water equivalent (SWE), rootzone soil moisture in equivalent water depth (RZSME), and rootzone soil moisture in volumetric water content (RZSMV).
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.
thumbnail
A list of stream 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.
As more hydrocarbon production from hydraulic fracturing and other methods produce large volumes of water, innovative methods must be explored for treatment and reuse of these waters. However, understanding the general water chemistry of these fluids is essential to providing the best treatment options optimized for each producing area. Machine learning algorithms can often be applied to datasets to solve complex problems. In this study, we used the U.S. Geological Survey’s National Produced Waters Geochemical Database (USGS PWGD) in an exploratory exercise to determine if systematic variations exist between produced waters and geologic environment that could be used to accurately classify a water sample to a given...
Categories: Data; Tags: Alabama, Alaska, Alaska Region, Arizona, Arkansas, All tags...
thumbnail
Spatial data used in the study "Characterization and Evaluation of Controls on Post-Fire Streamflow Response Across Western U.S. Watersheds".
View more...
ScienceBase brings together the best information it can find about USGS researchers and offices to show connections to publications, projects, and data. We are still working to improve this process and information is by no means complete. If you don't see everything you know is associated with you, a colleague, or your office, please be patient while we work to connect the dots. Feel free to contact sciencebase@usgs.gov.