This data release contains daily time series estimates of natural streamflow at 5,439 GAGES-II non-reference streamgages in 19 study regions across the conterminous United States from October 1, 1980 through September 30, 2017, 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). NNDAR, MCDAR, NNQPPQ, and MCQPPQ estimates were computed following methods described in Farmer and others (2014), with updates to the flow-duration curve modeling which is described in Over and others (2018). OKDAR estimates were computed using pooled variograms for each study region following methods described in Farmer (2016). Daily streamflow estimation was conducted by study region (hydrologic unit code level-2 regions as defined in Falcone, 2011) by building statistical models using 1,385 GAGES-II reference streamgages from mostly undisturbed watersheds as index gages (Russell and others, 2020). Estimates were then made at GAGES-II non-reference streamgages. Location information and basin characteristics for study gages were obtained from the GAGES-II dataset (Falcone, 2011). Observed daily streamflow data were retrieved from the National Water Information System (USGS, 2019). This data release contains 19 separate zip files; one for each study region. Each zip file contains an individual tab-delimited text file for each non-reference streamgage in the study region. A text file summarizing period of record information for each non-reference streamgage is provided (non-reference_gages_summary.csv). This data release also contains a text file (Model_info.csv) of regional regression equations for 27 flow quantiles that were developed in each study region in order to implement the QPPQ methods and a text file (BC_transformations.csv) describing transformations made to the GAGES-II derived basin characteristics prior to use in the regression equations. The five sets of streamflow estimates represent expected natural streamflow conditions with minimal disturbance by human activities, in other words, without the effects of regulation, diversion, land development, or other anthropogenic activities. The observed streamflow records at the non-reference streamgages were compared to the five simulated streamflow records. These performance metrics are provided at each gage for all five statistical methods (NonRef_PMs_byStation.csv) and as summaries by region (NonRef_PM_summaries_byRegion.csv). 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., and 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, 34 p., https://doi.org/10.3133/sir20185072. Russell, A.M., Over, T.M., and Farmer, W.H., 2020, Cross-validation results for five statistical methods of daily streamflow estimation at 1,385 reference streamgages in the conterminous United States, Water Years

This dataset contains annual metrics quantifying low streamflows, climate, topography, land cover and geology for 325 USGS GAGES-2 watersheds within the Delaware River Basin boundary or with watershed centroids within a 25-mile buffer of the Delaware River Basin boundary.

This data release contains trend results computed on the basis of modeled and observed daily streamflows at 502 reference gages across the conterminous U.S. from October 1, 1983 through September 30, 2016. Modeled daily streamflows were computed using the deterministic Precipitation Runoff Modeling System (PRMS), and five statistical techniques: Nearest-Neighbor Drainage Area Ratio (NNDAR), Map-Correlation Drainage Area Ratio (MCDAR), Ordinary Kriging of the logarithms of discharge per unit area (OKDAR), Nearest-Neighbor nonlinear spatial interpolation using flow duration curves (NNQPPQ), and Map-Correlation nonlinear spatial interpolation using flow duration curves (MCQPPQ). Observed daily streamflow data for the study gages were retrieved from the National Water Information System (NWIS). Study gages were selected from among Hydro-Climatic Data Network 2009 (HCDN-2009) gages in the GAGES-II dataset considered to be minimally affected by regulation, diversion, mining, or other anthropogenic activities. Results include trends in annual and monthly means, annual percentiles (1, 5, 10, 25, 50, 75, 90, 95, 99), annual 1-day high, 3-day high, and 7-day low, and annual snowmelt-related runoff timing for a subset of snowmelt dominated basins. Bias and volumetric efficiency statistics between observed and modeled streamflows also are provided.

This metadata record describes monthly estimates of natural stream flows for greater than 2.5 million stream reaches, defined by the National Hydrography Dataset (NHD) Version 2.0, in the conterminous United States for the period 1950-2015. A statistical machine learning technique - random forest modeling - was applied to estimate natural flows using 200 potential predictor variables. The dataset is organized by level 3 ecoregions, with each ecoregion having its own directory. Separate .csv files for each stream reach in the NHD network belonging to a given ecoregion are contained within the respective ecoregion directories. The stream reach .csv files are named as the NHD COMID corresponding to that stream reach. Methods used to calibrate the random forest models, and references to predictor data sources are detailed in the Journal of the American Water Resources Associate paper "A database of natural monthly flow estimates from 1950-2015 for the conterminous United States" by Miller and others (2018). See the discussion paper link in the "Related External Resources" section for access.

This metadata record describes monthly estimates of natural stream flows for greater than 2.5 million stream reaches, defined by the National Hydrography Dataset (NHD) Version 2.0, in the conterminous United States for the period 1950-2015. A statistical machine learning technique - random forest modeling - was applied to estimate natural flows using 200 potential predictor variables. The dataset is organized by level 3 ecoregions, with each ecoregion having its own directory. Separate .csv files for each stream reach in the NHD network belonging to a given ecoregion are contained within the respective ecoregion directories. The stream reach .csv files are named as the NHD COMID corresponding to that stream reach. Methods used to calibrate the random forest models, and references to predictor data sources are detailed in the Journal of the American Water Resources Associate paper "A database of natural monthly flow estimates from 1950-2015 for the conterminous United States" by Miller and others (2018). See the discussion paper link in the "Related External Resources" section for access.

This metadata record describes the observed and estimated hydrologic metrics for the 1980 to 2019 period for U.S. Geological Survey streamgage locations across the Conterminous United States. The datasets are arranged in four files: (1) CONUS_Observed_Estimated_HMs_Annual_Monthly.csv, (2) CONUS_Bootstrap_Validations_for_Models.csv, (3) CONUS_Streamflow_Gages_for_Models.csv, and (4) Data_Dictionary_Flow_Metrics.csv. The CONUS_Observed_Estimated_HMs_Annual_Monthly.csv file contains the following six attributes: (1) the U.S. Geological Survey streamgage identification number, (2) calendar year, (3) observed hydrologic metric, (4) estimated hydrologic metric, (5) hydrologic metric abbreviation, and (6) aggregated level 2 ecoregion. The observed hydrologic metrics were calculated using collected streamflow daily values from U.S. Geological Survey streamflow gaging stations (U.S. Geological Survey National Water Information System, http://dx.doi.org/10.5066/F7P55KJN), and the estimated hydrologic metrics were estimated by cross-sectional time series random forest modeling methods by Miller, M.P., Carlisle, D.M., Wolock, D.M., and Wieczorek, M., 2018, A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States: Journal of the American Water Resources Association, 54(6), 1258-1269 [Also available at https://doi.org/10.1111/1752-1688.12685]. Forty-seven hydrologic metrics representing magnitude, frequency, duration, and timing were calculated. The hydrologic metric abbreviations, definitions, units, and citations are detailed in the Data_Dictionary_Flow_Metrics.csv file. The low- and high-flow magnitudes were calculated from the 10th and 90th percentile non-exceedence streamflows divided by the drainage area, respectively. The low- and high-flow frequencies were calculated as the number of pulses below the 10th and above the 90th percentile values, respectively. The low- and high-flow durations were calculated from the length of time (in days) that the streamflow was below the 10th percentile or above the 90th percentile, respectively. The low- and high-flow seasonality values were calculated based on frequency of occurrence in different seasons (for more details, please see Eng, K., Carlisle, D.M., Grantham, T.E., Wolock, D.M., and Eng, R.L., 2019, Severity and extent of alterations to natural streamflow regimes based on hydrologic metrics in the conterminous United States, 1980-2014: U.S. Geological Survey Scientific Investigations Report 2019-5001, 25 p. [Also available at https://doi.org/10.3133/sir20195001]. The CONUS_Streamflow_Gages_for_Models.csv file contains the U.S. Geological Survey list of streamflow gaging stations used in cross-sectional time series random forest models. The CONUS_Bootstrap_Validations_for_Models.csv file lists the U.S. Geological Survey streamflow gaging stations used in the bootstrapped validation data sets used to assess model performance. In addition, bootstrap validation also assesses model robustness by testing various calibration configurations. These bootstrap validation data sets may contain random amounts of observations that are outside the range of the observations used in the calibration, and/or observations that are not independent from one another. There are no missing values in any of the files. The three data files are in a comma separated value text format.

This metadata record describes the observed and estimated hydrologic metrics for the 1980 to 2019 period for U.S. Geological Survey streamgage locations across the Conterminous United States. The datasets are arranged in four files: (1) CONUS_Observed_Estimated_HMs_Annual_Monthly.csv, (2) CONUS_Bootstrap_Validations_for_Models.csv, (3) CONUS_Streamflow_Gages_for_Models.csv, and (4) Data_Dictionary_Flow_Metrics.csv. The CONUS_Observed_Estimated_HMs_Annual_Monthly.csv file contains the following six attributes: (1) the U.S. Geological Survey streamgage identification number, (2) calendar year, (3) observed hydrologic metric, (4) estimated hydrologic metric, (5) hydrologic metric abbreviation, and (6) aggregated level 2 ecoregion. The observed hydrologic metrics were calculated using collected streamflow daily values from U.S. Geological Survey streamflow gaging stations (U.S. Geological Survey National Water Information System, http://dx.doi.org/10.5066/F7P55KJN), and the estimated hydrologic metrics were estimated by cross-sectional time series random forest modeling methods by Miller, M.P., Carlisle, D.M., Wolock, D.M., and Wieczorek, M., 2018, A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States: Journal of the American Water Resources Association, 54(6), 1258-1269 [Also available at https://doi.org/10.1111/1752-1688.12685]. Forty-seven hydrologic metrics representing magnitude, frequency, duration, and timing were calculated. The hydrologic metric abbreviations, definitions, units, and citations are detailed in the Data_Dictionary_Flow_Metrics.csv file. The low- and high-flow magnitudes were calculated from the 10th and 90th percentile non-exceedence streamflows divided by the drainage area, respectively. The low- and high-flow frequencies were calculated as the number of pulses below the 10th and above the 90th percentile values, respectively. The low- and high-flow durations were calculated from the length of time (in days) that the streamflow was below the 10th percentile or above the 90th percentile, respectively. The low- and high-flow seasonality values were calculated based on frequency of occurrence in different seasons (for more details, please see Eng, K., Carlisle, D.M., Grantham, T.E., Wolock, D.M., and Eng, R.L., 2019, Severity and extent of alterations to natural streamflow regimes based on hydrologic metrics in the conterminous United States, 1980-2014: U.S. Geological Survey Scientific Investigations Report 2019-5001, 25 p. [Also available at https://doi.org/10.3133/sir20195001]. The CONUS_Streamflow_Gages_for_Models.csv file contains the U.S. Geological Survey list of streamflow gaging stations used in cross-sectional time series random forest models. The CONUS_Bootstrap_Validations_for_Models.csv file lists the U.S. Geological Survey streamflow gaging stations used in the bootstrapped validation data sets used to assess model performance. In addition, bootstrap validation also assesses model robustness by testing various calibration configurations. These bootstrap validation data sets may contain random amounts of observations that are outside the range of the observations used in the calibration, and/or observations that are not independent from one another. There are no missing values in any of the files. The three data files are in a comma separated value text format.

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 is a line coverage of average annual runoff in the conterminous United States, 1951-1980. Surface runoff Average runoff Surface waters United States

This is a line coverage of average annual runoff in the conterminous United States, 1951-1980. Surface runoff Average runoff Surface waters United States