The U.S. Geological Survey (USGS) operates a dense network of streamgages within the Delaware River Basin (DRB) that provides near real-time and daily mean streamflow values, many of which have a decades long period of record. This long-term, historical dataset of daily mean streamflows is crucial for prediction of flows at ungaged stream reaches by means of parameter-based regression equations and can assist in informing water management and use decisions within the DRB. The USGS computed updated flow-duration exceedance probabilities for 98 current (2022) reference streamgages within the DRB with at least 10 years of record and minimally altered (nominal anthropogenic activity such as mining, diversion, or impoundment) streamflow regimes. The Make Plotting Position (MkPP; Granato, 2009) program with Weibull plotting position was used to calculate flow-duration exceedance probabilities for 21 streamflow percentiles. Percent difference comparison between these resulting flow-duration exceedances were made with observed and regression equation predicted streamflow values published by Stuckey (2016) for the 1960 through 2010 time period. A time-series trend analysis of 1979–2022 daily mean streamflows at 19 percentiles was computed using a non-parametric Mann-Kendall trend test (Hirsch and others, 2015). Associated basin characteristic raster layers that are currently used in StreamStats for the DRB, including percentage sand in soil, percentage poorly drained soils, percentage urban land use, mean annual precipitation, mean winter precipitation (December–February), and soil hydraulic conductivity, are included in this data release. Citations: Granato, G.E., 2009, Computer programs for obtaining and analyzing daily mean streamflow data from the U.S. Geological Survey National Water Information System Web Site: U.S. Geological Survey Open-File Report 2008–1362, 123 p. on CD-ROM, 5 appendixes, https://pubs.usgs.gov/publication/ofr20081362 Hirsch R.M., DeCicco L.A., 2015, User Guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R Packages for Hydrologic Data: U.S. Geological Survey Techniques and Methods 4-A10, https://pubs.usgs.gov/tm/04/a10/ Stuckey, M.H., 2016, Estimation of daily mean streamflow for ungaged stream locations in the Delaware River Basin, water years 1960–2010: U.S. Geological Survey Scientific Investigations Report 2015–5157, 42 p., http://dx.doi.org/10.3133/sir20155157

This USGS data release contains daily-timestep and monthly-timestep estimates of baseflow at 49 reference stream gages located within 25 miles of the Delaware River basin watershed boundary. Estimates are provided for the available period of record of streamflow data at each site between 1950 and 2015. A two-parameter recursive digital filter was used to estimate baseflow at the selected stream gaging stations using U.S. Geological Survey Groundwater Toolbox (Barlow and others, 2017; Eckhardt, 2005). References cited: Barlow, P.M., Cunningham, W.L., Zhai, T., and Gray, M., 2017, U.S. Geological Survey Groundwater Toolbox version 1.3.1, a graphical and mapping interface for analysis of hydrologic data: U.S. Geological Survey Software Release, 26 May 2017, https://doi.org/10.5066/F7R78C9G. Eckhardt, K., 2005, How to construct recursive digital filters for baseflow separation: Hydrological Processes - An International Journal, v. 19, p. 50-515, https://doi.org/10.1002/hyp.5675.

This USGS data release contains daily-timestep and monthly-timestep estimates of baseflow at 49 reference stream gages located within 25 miles of the Delaware River basin watershed boundary. Estimates are provided for the available period of record of streamflow data at each site between 1950 and 2015. A two-parameter recursive digital filter was used to estimate baseflow at the selected stream gaging stations using U.S. Geological Survey Groundwater Toolbox (Barlow and others, 2017; Eckhardt, 2005). References cited: Barlow, P.M., Cunningham, W.L., Zhai, T., and Gray, M., 2017, U.S. Geological Survey Groundwater Toolbox version 1.3.1, a graphical and mapping interface for analysis of hydrologic data: U.S. Geological Survey Software Release, 26 May 2017, https://doi.org/10.5066/F7R78C9G. Eckhardt, K., 2005, How to construct recursive digital filters for baseflow separation: Hydrological Processes - An International Journal, v. 19, p. 50-515, https://doi.org/10.1002/hyp.5675.

This metadata record describes monthly estimates of natural baseflow for 15,866 stream reaches, defined by the National Hydrography Dataset Plus Version 2.0 (NHDPlusV2), in the Delaware River Basin for the period 1950-2015. A statistical machine learning technique - random forest modeling (Liaw and Wiener, 2018; R Core Team, 2020) - was applied to estimate natural flows using about 150 potential predictor variables (Miller and others, 2018). Calibration data used for the random forest model are available from (Foks and others, 2020). Each model was run twice, first using all potential predictor variables, which represents a "full" model run, and a second time using the top 20 predictors from the original run, which represents the "partial" model run. Model performance of the full and partial models were compared and identified to be similar. Therefore, predictions for all NHDPlusV2 stream reaches were made using the partial model. Methods used to calibrate the random forest models, and references to predictor data sources are detailed in (Miller and others, 2018). The R scripts used and directions to run the scripts are included in this data release. References cited: Liaw, A., and Wiener, M., 2018, Package 'randomForest': The Comprehensive R Archive Network, https://cran.r-project.org/web/packages/randomForest/randomForest.pdf. 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, v. 54, no. 6, p. 1258-1269, https://doi.org/10.1111/1752-1688.12685. Foks, S.S., Miller, M.P., and Hopple, J.A., 2020, Daily-timestep and monthly-timestep estimates of baseflow at 49 reference stream gages located within 25 miles of the Delaware River basin watershed boundary for the years 1950 through 2015: U.S. Geological Survey data release, https://doi.org/10.5066/P9XY70L4. R Core Team, 2020, R-A language and environment for statistical computing: R Foundation for Statistical Computing, https://www.eea.europa.eu/data-and-maps/indicators/oxygen-consuming-substances-in-rivers/r-development-core-team-2006.

This metadata record describes monthly estimates of natural baseflow for 15,866 stream reaches, defined by the National Hydrography Dataset Plus Version 2.0 (NHDPlusV2), in the Delaware River Basin for the period 1950-2015. A statistical machine learning technique - random forest modeling (Liaw and Wiener, 2018; R Core Team, 2020) - was applied to estimate natural flows using about 150 potential predictor variables (Miller and others, 2018). Calibration data used for the random forest model are available from (Foks and others, 2020). Each model was run twice, first using all potential predictor variables, which represents a "full" model run, and a second time using the top 20 predictors from the original run, which represents the "partial" model run. Model performance of the full and partial models were compared and identified to be similar. Therefore, predictions for all NHDPlusV2 stream reaches were made using the partial model. Methods used to calibrate the random forest models, and references to predictor data sources are detailed in (Miller and others, 2018). The R scripts used and directions to run the scripts are included in this data release. References cited: Liaw, A., and Wiener, M., 2018, Package 'randomForest': The Comprehensive R Archive Network, https://cran.r-project.org/web/packages/randomForest/randomForest.pdf. 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, v. 54, no. 6, p. 1258-1269, https://doi.org/10.1111/1752-1688.12685. Foks, S.S., Miller, M.P., and Hopple, J.A., 2020, Daily-timestep and monthly-timestep estimates of baseflow at 49 reference stream gages located within 25 miles of the Delaware River basin watershed boundary for the years 1950 through 2015: U.S. Geological Survey data release, https://doi.org/10.5066/P9XY70L4. R Core Team, 2020, R-A language and environment for statistical computing: R Foundation for Statistical Computing, https://www.eea.europa.eu/data-and-maps/indicators/oxygen-consuming-substances-in-rivers/r-development-core-team-2006.

This data release makes available three data tables supporting a spatiotemporal analysis of riverine conductivity and streamflow trends within the Delaware River Basin. The listed datasets include baseflow and total flow time series for selected gaged basins, watershed attributes, water quality information and trend analysis results.

This data release makes available three data tables supporting a spatiotemporal analysis of riverine conductivity and streamflow trends within the Delaware River Basin. The listed datasets include baseflow and total flow time series for selected gaged basins, watershed attributes, water quality information and trend analysis results.

This dataset contains four tables: (1) site, regression and weighted peak flow estimates (peak_flow_estimates.csv) (2) site, regression and weighted low flow estimates (low_flow_estimates.csv) (3) basin characteristics for peak flow sites (peak_basin_stats.csv) (4) basin characteristics for low flow sites (low_basin_stats.csv). These tables support the USGS report "Magnitude and Frequency of Peak Flows and Low Flows on Nontidal Streams in Delaware."

This dataset contains four tables: (1) site, regression and weighted peak flow estimates (peak_flow_estimates.csv) (2) site, regression and weighted low flow estimates (low_flow_estimates.csv) (3) basin characteristics for peak flow sites (peak_basin_stats.csv) (4) basin characteristics for low flow sites (low_basin_stats.csv). These tables support the USGS report "Magnitude and Frequency of Peak Flows and Low Flows on Nontidal Streams in Delaware."

Tables are presented listing parameters and fit statistics for 25,453 maximum likelihood logistic regression (MLLR) models describing hydrological drought probabilities at 324 gaged locations on rivers and streams in the Delaware River Basin (DRB). Data from previous months are used to estimate chance of hydrological drought during future summer months. Models containing 1 explanatory variable use monthly mean daily streamflow data (DV) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV from the previous 11 months. Outcomes are estimated 1 to 12 months ahead of their occurrence. Models containing 2 explanatory variables use monthly mean daily streamflow data (DV) and monthly mean precipitation data (P) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV and monthly mean P from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Models containing 3 explanatory variables use monthly mean daily streamflow data (DV), monthly mean precipitation data (P), and monthly mean maximum daily air temperature (T) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV, monthly mean P, and monthly mean maximum T from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Models containing 4 explanatory variables use monthly mean daily streamflow data (DV), monthly mean precipitation data (P), monthly mean maximum daily air temperature (T), and monthly mean potential evapotranspiration data (PET) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV, monthly mean P, monthly mean maximum T, and monthly mean PET from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Explanatory variable selections for multiparameter models were optimized using random forest statistical methods. Selected single-parameter and multi-parameter models are provided. Overall correct classification rates tend to improve and models become more complex as the number of model explanatory variables increases from 1 to 4. Parameters for models with 1 explanatory variable are listed in the table labeled: “DRB-1_Variable_Equations.” Parameters for models with 2 explanatory variable are listed in the table labeled: “DRB-2_Variable_Equations.” Parameters for models with 3 explanatory variable are listed in the table labeled: “DRB-3_Variable_Equations.” Parameters for models with 4 explanatory variable are listed in the table labeled: “DRB-4_Variable_Equations.” Parameters describing models containing 1 explanatory variable may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, DV is a factor variable describing monthly mean daily streamflow (ft3/s). Parameters describing models containing 2 explanatory variables may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV+ β2• P)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, β2 is a slope parameter, DV is a factor variable describing monthly mean daily streamflow (ft3/s), P is a factor variable describing monthly mean precipitation (in/day). Parameters describing models containing 3 explanatory variables may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV+ β2• P+ β3• T)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, β2 is a slope parameter, β3 is a slope parameter DV is a factor variable describing monthly mean daily