This data set archives all inputs, outputs and scripts needed to reproduce the findings of W.H. Farmer and G.F. Koltun in the 2017 Journal of Hydrology Regional Studies article entitled “Geospatial Tools Effectively Estimated Nonexceedance Probabilities of Daily Streamflow at Ungauged and Intermittently Gauged Locations in Ohio”. Input data includes observed streamflow values, in cubic feet per second, for 152 streamgages in and around Ohio from 01 January 2009 through 31 August 2015. Data from the Ohio Environmental Protection Agency on where and when water quality samples were taken are also provided. Geospatial locations are provided for all streamgages and sampling sites considered. ESRI ArcGIS shapefiles are available for all maps produced in the original publication. Comma-separated-value files contain the output data required to reproduce every figure in the report. This archive also includes an R script capable of reading the input files and producing output files and figures. See the README.txt file for a full description of model application.

A generalized streamflow-variability index coverage was created by interpolating a grid (with 6,066-ft^2 cells) from at-site values of the streamflow-variability index computed for 133 rural, unregulated streamflow-gaging stations in Ohio and nearby areas of adjacent states. Grid interpolation was done by means of the interpolate surface routine contained in the Spatial Analyst extension of ArcView. The inverse distance weighting (IDW) algorithm was used based on the 12 nearest neighbors. The streamflow-variability index at a streamflow-gaging station is defined as the standard deviation of the logarithms of the 19 streamflow values at 5-percent class intervals from 5 to 95 percent on the flow-duration curve (Searcy, 1959) of daily mean streamflow for the analysis period. Searcy, J.K., 1959, Flow-duration curves, manual of hydrology-part 2, low-flow techniques: U.S. Geological Survey Water-Supply Paper 1542-A, 33 p.

A generalized streamflow-variability index coverage was created by interpolating a grid (with 6,066-ft^2 cells) from at-site values of the streamflow-variability index computed for 133 rural, unregulated streamflow-gaging stations in Ohio and nearby areas of adjacent states. Grid interpolation was done by means of the interpolate surface routine contained in the Spatial Analyst extension of ArcView. The inverse distance weighting (IDW) algorithm was used based on the 12 nearest neighbors. The streamflow-variability index at a streamflow-gaging station is defined as the standard deviation of the logarithms of the 19 streamflow values at 5-percent class intervals from 5 to 95 percent on the flow-duration curve (Searcy, 1959) of daily mean streamflow for the analysis period. Searcy, J.K., 1959, Flow-duration curves, manual of hydrology-part 2, low-flow techniques: U.S. Geological Survey Water-Supply Paper 1542-A, 33 p.

In cooperation with the Ohio Department of Transportation and the U.S. Department of Transportation, Federal Highway Administration, the USGS developed a database of geomorphic data for a sampling of unregulated natural alluvial streams in Ohio. The vector point shapefile that these metadata describe is based on this geomorphic database and consists of 50 points and associated attributes. The database was developed as part of a study to develop methods to estimate bankfull characteristics of Ohio streams and to relate geomorphic characteristics of Ohio streams to peak streamflows of selected recurrence intervals. The results of the study are presented in a report by Sherwood and Huitger (2005). The database developed for this study consists of geomorphic, basin, and flood-frequency characteristics for 50 study sites in Ohio, of which 40 sites are near streamflow-gaging stations. Field surveys were done at each site to collect the geomorphic data. Bankfull indicators were identified and evaluated, cross-section and longitudinal profiles were surveyed, and bed- and bank-material were sampled. Field data were analyzed to determine various geomorphic characteristics such as bankfull width, bankfull mean depth, bankfull cross-sectional area, bankfull discharge, streambed slope, and bed- and bank-material particle-size distribution. The various geomorphic characteristics were analyzed by means of a combination of graphical and statistical techniques. These techniques resulted in a set of regional curves, simple-regression (drainage-area only) equations, and multiple-regression equations to estimate bankfull width, bankfull mean depth, bankfull cross-sectional area, and bankfull discharge. Explanatory variables included drainage area, main-channel slope, main- channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope. Average standard errors of prediction for bankfull width equations ranged from 20.6 to 24.8 percent; for bankfull mean depth, 18.8 to 20.6 percent; for bankfull cross-sectional area, 25.4 to 30.6 percent; and for bankfull discharge, 27.0 to 78.7 percent. The simple-regression (drainage-area only) equations have the highest average standard errors of prediction. The multiple-regression equations-in which the explanatory variables included drainage area, main-channel slope, main-channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope-have the lowest average standard errors of prediction. Statistical and graphical analyses were done to investigate development of methods to estimate flood-peak discharges from geomorphic characteristics based on the 40 study sites at streamflow-gaging stations. The logarithms of the annual peak discharges for each site were fit by a Pearson Type III frequency distribution to develop a flood-peak-frequency relation for each site. The peak-frequency data were related to geomorphic, basin, and climatic variables of the 40 study sites by multiple-regression analysis. The analyses resulted in a set of multiple-regression equations to estimate flood-peak discharge having recurrence intervals of 2, 5, 10, 25, 50, and 100 years from bankfull cross-sectional area, in which the average standard errors of prediction are 31.6, 32.6, 35.9, 41.5, 46.2, and 51.2 percent, respectively. Sherwood, J.M. and Huitger, C.A., 2005, Bankfull Characteristics of Ohio Streams and Their Relation to Peak Streamflows: U.S. Geological Survey Scientific Investigations Report 2005-5153

In cooperation with the Ohio Department of Transportation and the U.S. Department of Transportation, Federal Highway Administration, the USGS developed a database of geomorphic data for a sampling of unregulated natural alluvial streams in Ohio. The vector point shapefile that these metadata describe is based on this geomorphic database and consists of 50 points and associated attributes. The database was developed as part of a study to develop methods to estimate bankfull characteristics of Ohio streams and to relate geomorphic characteristics of Ohio streams to peak streamflows of selected recurrence intervals. The results of the study are presented in a report by Sherwood and Huitger (2005). The database developed for this study consists of geomorphic, basin, and flood-frequency characteristics for 50 study sites in Ohio, of which 40 sites are near streamflow-gaging stations. Field surveys were done at each site to collect the geomorphic data. Bankfull indicators were identified and evaluated, cross-section and longitudinal profiles were surveyed, and bed- and bank-material were sampled. Field data were analyzed to determine various geomorphic characteristics such as bankfull width, bankfull mean depth, bankfull cross-sectional area, bankfull discharge, streambed slope, and bed- and bank-material particle-size distribution. The various geomorphic characteristics were analyzed by means of a combination of graphical and statistical techniques. These techniques resulted in a set of regional curves, simple-regression (drainage-area only) equations, and multiple-regression equations to estimate bankfull width, bankfull mean depth, bankfull cross-sectional area, and bankfull discharge. Explanatory variables included drainage area, main-channel slope, main- channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope. Average standard errors of prediction for bankfull width equations ranged from 20.6 to 24.8 percent; for bankfull mean depth, 18.8 to 20.6 percent; for bankfull cross-sectional area, 25.4 to 30.6 percent; and for bankfull discharge, 27.0 to 78.7 percent. The simple-regression (drainage-area only) equations have the highest average standard errors of prediction. The multiple-regression equations-in which the explanatory variables included drainage area, main-channel slope, main-channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope-have the lowest average standard errors of prediction. Statistical and graphical analyses were done to investigate development of methods to estimate flood-peak discharges from geomorphic characteristics based on the 40 study sites at streamflow-gaging stations. The logarithms of the annual peak discharges for each site were fit by a Pearson Type III frequency distribution to develop a flood-peak-frequency relation for each site. The peak-frequency data were related to geomorphic, basin, and climatic variables of the 40 study sites by multiple-regression analysis. The analyses resulted in a set of multiple-regression equations to estimate flood-peak discharge having recurrence intervals of 2, 5, 10, 25, 50, and 100 years from bankfull cross-sectional area, in which the average standard errors of prediction are 31.6, 32.6, 35.9, 41.5, 46.2, and 51.2 percent, respectively. Sherwood, J.M. and Huitger, C.A., 2005, Bankfull Characteristics of Ohio Streams and Their Relation to Peak Streamflows: U.S. Geological Survey Scientific Investigations Report 2005-5153

In 2016, non-interpretive streamflow statistics were compiled for streamgages located throughout the Nation and stored in the StreamStatsDB database for use with StreamStats and other applications. Two previously published USGS computer programs that were designed to help calculate streamflow statistics were updated to better support StreamStats as part of this effort. These programs are named “GNWISQ” (Get National Water Information System Streamflow (Q) files) and “QSTATS” (Streamflow (Q) Statistics). Statistics for 20,438 streamgages that had 1 or more complete years of record during water years 1901 through 2015 were calculated from daily mean streamflow data; 19,415 of these streamgages were within the conterminous United States. About 89 percent of the 20,438 streamgages had 3 or more years of record, and 65 percent had 10 or more years of record. Drainage areas of the 20,438 streamgages ranged from 0.01 to 1,144,500 square miles. The magnitude of annual average streamflow yields (streamflow per square mile) for these streamgages varied by almost six orders of magnitude, from 0.000029 to 34 cubic feet per second per square mile. About 64 percent of these streamgages did not have any zero-flow days during their available period of record. The 18,122 streamgages with 3 or more years of record were included in the StreamStatsDB compilation so they would be available via the StreamStats interface for user-selected streamgages.

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 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

The data and R scripts contained in this data release are provided as support for a manuscript titled, "Copula theory as a generalized framework for flow-duration curve based streamflow estimates in ungaged and partially gaged catchments" (Worland and others, 2019) submitted to Water Resources Research. The dv_input.csv contains the measured daily streamflow values for 37 streamgages in the Mobile-Tombigbee River Basin, 4-digit hydrologic unit code (HUC4) 0316, and 37 gages in the Trinity River Basin, HUC4 codes 1201, 1202, 1203, and 1204. The coord_input.csv contains the coordinates and the basin area (squared meters) for the gages in each basin. The R scripts generate daily streamflow estimates using 16 different estimation methods. Additionally, the R scripts also provide a function for estimating leave-one-out cross validated correlations between sites using an ordinary kriging approach. The dv_output.csv files contains the predicted daily streamflow values using the 16 streamflow estimation methods.

The data and R scripts contained in this data release are provided as support for a manuscript titled, "Copula theory as a generalized framework for flow-duration curve based streamflow estimates in ungaged and partially gaged catchments" (Worland and others, 2019) submitted to Water Resources Research. The dv_input.csv contains the measured daily streamflow values for 37 streamgages in the Mobile-Tombigbee River Basin, 4-digit hydrologic unit code (HUC4) 0316, and 37 gages in the Trinity River Basin, HUC4 codes 1201, 1202, 1203, and 1204. The coord_input.csv contains the coordinates and the basin area (squared meters) for the gages in each basin. The R scripts generate daily streamflow estimates using 16 different estimation methods. Additionally, the R scripts also provide a function for estimating leave-one-out cross validated correlations between sites using an ordinary kriging approach. The dv_output.csv files contains the predicted daily streamflow values using the 16 streamflow estimation methods.