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Watershed-scale water quality and water availability are affected by the interaction between the landscape and surface and subsurface flows at multiple scales. Wide-spread agriculture leads to diffuse non-point sources of contamination by agricultural chemicals. Localized exchanges of surface water and groundwater through highly reactive streambeds can attenuate the impact of agricultural chemicals on water quality. Thus, understanding the patterns and trends in water quality within a watershed requires analyses at multiple scales to understand hydrologic processes and the integration of hydrology and water quality information. The main objective of my research is to develop a better understanding of the role of...
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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...
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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.
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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...


    map background search result map search result map Streamflow benchmark locations for hydrologic model evaluation within the conterminous United States (cobalt gages) 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) 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) Streamflow benchmark locations for hydrologic model evaluation within the conterminous United States (cobalt gages) 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) 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)