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 bias in the flow duration curve high-segment volume, and the percent bias in flow duration curve low-segment volume. Two climatological KGE benchmarks are included that are calculated using daily mean streamflow observations and interannual daily mean or median flows. Additionally, KGE uncertainty estimates have been added as a separate csv file including the standard error of jackknife, standard error of bootstrap, the 5th, 50th and 95th percentiles of the estimates, the jackknife score, the bias of jackknife, the bias of bootstrap, and the standard error of jackknife after bootstrap.

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 bias in flow duration curve midsegment slope, the percent bias in the flow duration curve high-segment volume, and the percent bias in flow duration curve low-segment volume. Two climatological KGE benchmarks are included that are calculated using daily mean streamflow observations and interannual daily mean or median flows. Additionally, KGE uncertainty estimates have been added as a separate csv file including the standard error of jackknife, standard error of bootstrap, the 5th, 50th and 95th percentiles of the estimates, the jackknife score, the bias of jackknife, the bias of bootstrap, and the standard error of jackknife after bootstrap.

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 bias in the flow duration curve high-segment volume, and the percent bias in flow duration curve low-segment volume. Two climatological KGE benchmarks are included that are calculated using daily mean streamflow observations and interannual daily mean or median flows. Additionally, KGE uncertainty estimates have been added as a separate csv file including the standard error of jackknife, standard error of bootstrap, the 5th, 50th and 95th percentiles of the estimates, the jackknife score, the bias of jackknife, the bias of bootstrap, and the standard error of jackknife after bootstrap.

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 bias in flow duration curve midsegment slope, the percent bias in the flow duration curve high-segment volume, and the percent bias in flow duration curve low-segment volume. Two climatological KGE benchmarks are included that are calculated using daily mean streamflow observations and interannual daily mean or median flows. Additionally, KGE uncertainty estimates have been added as a separate csv file including the standard error of jackknife, standard error of bootstrap, the 5th, 50th and 95th percentiles of the estimates, the jackknife score, the bias of jackknife, the bias of bootstrap, and the standard error of jackknife after bootstrap.

This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Water Model (NWM) Retrospective version 2.1 computed at streamgage benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow (aggregated from an hourly timestep) versus observed daily mean streamflow. Using those errors, the D-score performance benchmark computes the mean squared logarithmic error (MSLE), then decomposes the overall MSLE into orthogonal components such as bias, distribution, and sequence (Hodson and others, 2021). For easier interpretation, the MSLE components can be passed through a scoring function as described in Hodson and others (2021). References: Foks, S.S., Towler, E., Hodson, T.O., Bock, A.R., Dickinson, J.E., Dugger, A.L., Dunne, K.A., Essaid, H.I., Miles, K.A., Over, T.M., Penn, C.A., Russell, A.M., Saxe, S.W., and Simeone, C.E., 2022, Streamflow benchmark locations for conterminous United States (cobalt gages): U.S. Geological Survey data release, https://doi.org/10.5066/P972P42Z. Hodson, T.O., Over, T.M., and Foks, S.S., 2021. Mean squared error, deconstructed. Journal of Advances in Modeling Earth Systems, 13, e2021MS002681. https://doi.org/10.1029/2021MS002681.

This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Water Model (NWM) Retrospective version 2.1 computed at streamgage benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow (aggregated from an hourly timestep) versus observed daily mean streamflow. Using those errors, the D-score performance benchmark computes the mean squared logarithmic error (MSLE), then decomposes the overall MSLE into orthogonal components such as bias, distribution, and sequence (Hodson and others, 2021). For easier interpretation, the MSLE components can be passed through a scoring function as described in Hodson and others (2021). References: Foks, S.S., Towler, E., Hodson, T.O., Bock, A.R., Dickinson, J.E., Dugger, A.L., Dunne, K.A., Essaid, H.I., Miles, K.A., Over, T.M., Penn, C.A., Russell, A.M., Saxe, S.W., and Simeone, C.E., 2022, Streamflow benchmark locations for conterminous United States (cobalt gages): U.S. Geological Survey data release, https://doi.org/10.5066/P972P42Z. Hodson, T.O., Over, T.M., and Foks, S.S., 2021. Mean squared error, deconstructed. Journal of Advances in Modeling Earth Systems, 13, e2021MS002681. https://doi.org/10.1029/2021MS002681.

This data release contains the D-score (version 0.1) 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 (Hay and LaFontaine, 2020) computed at streamflow benchmark locations (version 1.0) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow versus observed daily mean streamflow. Using those errors, the D-score performance benchmark computes the mean squared logarithmic error (MSLE), then decomposes the overall MSLE into orthogonal components such as bias, distribution, and sequence (Hodson and others, 2021). For easier interpretation, the MSLE components can be passed through a scoring function as described in Hodson and others (2021).

This data release contains the D-score (version 0.1) 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 (Hay and LaFontaine, 2020) computed at streamflow benchmark locations (version 1.0) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow versus observed daily mean streamflow. Using those errors, the D-score performance benchmark computes the mean squared logarithmic error (MSLE), then decomposes the overall MSLE into orthogonal components such as bias, distribution, and sequence (Hodson and others, 2021). For easier interpretation, the MSLE components can be passed through a scoring function as described in Hodson and others (2021).

This data release contains daily time series estimates of natural streamflow for 1,385 streamgages in 19 study regions in the conterminous U.S. from October 1, 1980, through September 30, 2017. These estimates are provided for gages from mostly undisturbed watersheds as defined by Falcone (2011), using five statistical techniques: nearest-neighbor drainage area ratio (NNDAR), map-correlation drainage area ratio (MCDAR), nearest-neighbor nonlinear spatial interpolation using flow duration curves (NNQPPQ), map-correlation nonlinear spatial interpolation using flow duration curves (MCQPPQ), and ordinary kriging of the logarithms of discharge per unit area (OKDAR). Location information and basin characteristics for study gages were obtained from the "Reference" gages of the GAGES-II dataset (Falcone, 2011, https://water.usgs.gov/lookup/getspatial?gagesII_Sept2011). Observed daily streamflow data were retrieved from the National Water Information System (NWIS) on September 7, 2018. NNDAR, MCDAR, NNQPPQ, and MCQPPQ estimates were computed following methods described by Farmer and others (2014), with updates to the flow-duration curve modeling which is described by Over and others (2018). OKDAR estimates were computed using pooled variograms for each study region following methods described by Farmer (2016). Daily streamflow estimation was conducted in a leave-one-out-cross-validation approach where each streamgage was treated as if ungaged and all the remaining streamgages in a study region were used to calibrate each method and perform estimations at the "ungaged" site. The observed streamflow records were compared to the five simulated streamflow records to help assess performance of each method. These performance metrics are provided at each gage for all five statistical methods. References cited: Falcone, J.A., 2011, GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow [digital spatial dataset] : U.S. Geological Survey Water Resources NSDI Node web page, https://water.usgs.gov/lookup/getspatial?gagesII_Sept2011. Farmer, W.H., Archfield, S.A., Over, T.M., Hay, L.E., LaFontaine, J.H., and Kiang, J.E., 2014, A comparison of methods to predict historical daily streamflow time series in the southeastern United States: U.S. Geological Survey Scientific Investigations Report 2014–5231, 34 p., http://dx.doi.org/10.3133/sir20145231. Farmer, W. H., 2016, Ordinary kriging as a tool to estimate historical daily streamflow records, Hydrology and Earth System Sciences, 20, 2721-2735, https://doi.org/10.5194/hess-20-2721-2016. Over, T.M., Farmer, W.H., Russell, A.M., 2018, Refinement of a regression-based method for prediction of flow-duration curves of daily streamflow in the conterminous United States; U.S. Geological Survey Scientific Investigations Report 2018–5072, https://doi.org/10.3133/sir20185072.

This data release contains daily time series estimates of natural streamflow for 1,385 streamgages in 19 study regions in the conterminous U.S. from October 1, 1980, through September 30, 2017. These estimates are provided for gages from mostly undisturbed watersheds as defined by Falcone (2011), using five statistical techniques: nearest-neighbor drainage area ratio (NNDAR), map-correlation drainage area ratio (MCDAR), nearest-neighbor nonlinear spatial interpolation using flow duration curves (NNQPPQ), map-correlation nonlinear spatial interpolation using flow duration curves (MCQPPQ), and ordinary kriging of the logarithms of discharge per unit area (OKDAR). Location information and basin characteristics for study gages were obtained from the "Reference" gages of the GAGES-II dataset (Falcone, 2011, https://water.usgs.gov/lookup/getspatial?gagesII_Sept2011). Observed daily streamflow data were retrieved from the National Water Information System (NWIS) on September 7, 2018. NNDAR, MCDAR, NNQPPQ, and MCQPPQ estimates were computed following methods described by Farmer and others (2014), with updates to the flow-duration curve modeling which is described by Over and others (2018). OKDAR estimates were computed using pooled variograms for each study region following methods described by Farmer (2016). Daily streamflow estimation was conducted in a leave-one-out-cross-validation approach where each streamgage was treated as if ungaged and all the remaining streamgages in a study region were used to calibrate each method and perform estimations at the "ungaged" site. The observed streamflow records were compared to the five simulated streamflow records to help assess performance of each method. These performance metrics are provided at each gage for all five statistical methods. References cited: Falcone, J.A., 2011, GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow [digital spatial dataset] : U.S. Geological Survey Water Resources NSDI Node web page, https://water.usgs.gov/lookup/getspatial?gagesII_Sept2011. Farmer, W.H., Archfield, S.A., Over, T.M., Hay, L.E., LaFontaine, J.H., and Kiang, J.E., 2014, A comparison of methods to predict historical daily streamflow time series in the southeastern United States: U.S. Geological Survey Scientific Investigations Report 2014–5231, 34 p., http://dx.doi.org/10.3133/sir20145231. Farmer, W. H., 2016, Ordinary kriging as a tool to estimate historical daily streamflow records, Hydrology and Earth System Sciences, 20, 2721-2735, https://doi.org/10.5194/hess-20-2721-2016. Over, T.M., Farmer, W.H., Russell, A.M., 2018, Refinement of a regression-based method for prediction of flow-duration curves of daily streamflow in the conterminous United States; U.S. Geological Survey Scientific Investigations Report 2018–5072, https://doi.org/10.3133/sir20185072.