Understanding how changing climatic conditions affect streamflow volume and timing is critical for effective water management. In the Rio Grande Basin of the southwest U.S., decreasing snowpack, increasing minimum temperatures, and decreasing streamflow have been observed in recent decades, but the effects of hydroclimatic changes on baseflow, or groundwater discharge to streams, have not been investigated. The dataset created in this data release was used to help support a study to determine how trends in precipitation, snowpack accumulation, and snowmelt rate relate to streamflow, baseflow, and the hydrologic partitioning of baseflow and runoff at 12 sites in the Upper Rio Grande Basin (URGB) during 1980 to 2015. Streamflow was partitioned into baseflow and runoff components at a daily time step using conductivity mass balance hydrograph separation. Trends in annual streamflow, baseflow, runoff, baseflow index, precipitation, snowmelt rate, and peak snow water equivalent (SWE) were evaluated from 1980 to 2015 using the non-parametric Mann-Kendall trend test.

The supplemental data presented here contain tabular data (in .csv format) including measured and estimated daily and water-year (1982–2016) streamflow for selected watersheds and estimated springflow at Page Springs in Harney Basin. Daily streamflow data are a composite of measured streamflow and extended streamflow records from short-term streamgages in gaged watersheds. Short-term or discontinuous records in gaged watersheds were extended to the period 1982–2016 using the Kendal-Thiel Robust Line (KTRL) method (Helsel and Hirsch, 2020) and ordinary-least squares (OLS) linear regression. Springflow estimates were provided by the U.S. Fish and Wildlife Service.

The supplemental data presented here contain tabular data (in .csv format) including measured and estimated daily and water-year (1982–2016) streamflow for selected watersheds and estimated springflow at Page Springs in Harney Basin. Daily streamflow data are a composite of measured streamflow and extended streamflow records from short-term streamgages in gaged watersheds. Short-term or discontinuous records in gaged watersheds were extended to the period 1982–2016 using the Kendal-Thiel Robust Line (KTRL) method (Helsel and Hirsch, 2020) and ordinary-least squares (OLS) linear regression. Springflow estimates were provided by the U.S. Fish and Wildlife Service.

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 set provides estimated and measured streamflow data and hydrograph-separation results for five sites located in northwest Mississippi. Streamflow data were collected by the U.S. Geological Survey (USGS) and the U.S. Army Corps of Engineers. Hydrograph-separation results provide runoff and baseflow estimates at each site that were calculated using four methods: PART, HYSEP Fixed, HYSEP Local Minimum, and BFI Standard, as well as an average base flow index (BFI) for all four methods.

East Canyon Creek is a perennial snowmelt-dominated stream that lies in the Snyderville Basin of Summit and Morgan Counties, Utah. Its headwaters begin as McLeod Creek on the eastern slopes of the Wasatch Mountains before joining Kimball Creek to form East Canyon Creek, proper, below the Interstate 80 overpass where it flows north-northwest into East Canyon Reservoir. The reach between the headwaters and East Canyon Reservoir includes three U.S. Geological Survey streamgages that monitor streamflow and specific conductance. The Snyderville Basin Water Reclamation District provides wastewater collection and reclamation services for Park City, Utah, and the surrounding areas and operates a water reclamation facility on East Canyon Creek near Jeremy Ranch. This data release includes daily, monthly, and annual streamflow and estimated baseflow data from three streamgages (10133650, 10133800, 10133980), monthly and annual climatological data from two snow telemetry stations (684, 814), and results of monthly and annual trend and correlation analyses between the 2011 and 2022 water years.

This dataset represents the base flow index values within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies. Catchment boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-catchment boundaries and then links them through an off-network flow table. The base-flow index (BFI) grid for the conterminous United States was developed to estimate (1) BFI values for ungaged streams, and (2) ground-water recharge throughout the conterminous United States (see Source_Information). Estimates of BFI values at ungaged streams and BFI-based ground-water recharge estimates are useful for interpreting relations between land use and water quality in surface and ground water. The bfi (%) was summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents the base flow index values within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies. Catchment boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-catchment boundaries and then links them through an off-network flow table. The base-flow index (BFI) grid for the conterminous United States was developed to estimate (1) BFI values for ungaged streams, and (2) ground-water recharge throughout the conterminous United States (see Source_Information). Estimates of BFI values at ungaged streams and BFI-based ground-water recharge estimates are useful for interpreting relations between land use and water quality in surface and ground water. The bfi (%) was summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

Baseflow is the portion of streamflow derived from groundwater flow. It is an important component of the groundwater budget, and can be estimated using known total streamflow at given points through time. Daily streamflow data was collected from 25 streamflow gaging stations across the northern High Plains Groundwater Availability Study (NHPGAS) area from the National Water Information System (NWIS) and the Nebraska Department of Natural Resources Stream Gaging Data Bank. The data from each site was processed using the BFI program, version 4.15 (Wahl and Wahl, 2007) to determine the baseflow component of total streamflow at each site. The average daily baseflow was computed for the year of 1940. The resulting baseflow values were applied as calibration targets to the end of the predevelopment model.