Located in south-central Texas, the Geronimo Creek and Plum Creek watersheds have long been characterized by elevated nitrate concentrations. From April 2015 through March 2016, an investigation was done by the U.S. Geological Survey, in cooperation with the Guadalupe-Blanco River Authority (GBRA) and the Texas State Soil and Water Conservation Board (TSSWCB), to assess nitrate (as nitrogen) concentrations and to document possible sources of elevated nitrate as nitrogen in these two watersheds. Water-quality samples were collected from 20 groundwater, spring, and stream sites across the two watersheds, along with precipitation samples and wastewater treatment plant (WWTP) effluent samples from the Plum Creek watershed. Stream, spring, and groundwater samples were collected from both watersheds during four synoptic sampling events to characterize spatial and temporal variations in water quality and chemical loadings. Water-quality and quantity data from the wastewater treatment plants and stream discharge data were also considered. Samples were analyzed for major ions, selected trace elements, nutrients, stable isotopes of water and nitrogen isotopes. Data collected for this project was used to calculate selected chemical loadings and used in interpretation of sources of nitrate in both watersheds.

This data release contains environmental tracer concentrations, modeled recharge conditions (water temperature, excess air), and resulting estimated groundwater residence times. This dataset supports an integrated hydrogeochemical investigation of solute sources, groundwater recharge processes, and groundwater flow in the Fountain Creek alluvial aquifer. The data release contains five comma separated value (CSV) files. The CSV files contain the model inputs (gas and tracer concentrations) and the model outputs (simulated recharge temperature, excess air, apparent groundwater age, and mean groundwater residence time). Data were collected in cooperation with the U.S. Air Force Civil Engineering Center.

This data release contains five comma separated value (csv) files that describe the location and water-quality data for wells, springs, and streams compiled for the U.S. Geological Survey (USGS) and Oregon Water Resources Department (OWRD) investigation of the groundwater resources of the Harney Basin, Oregon. The data included are site IDs, various site location information, well-construction details and hydrostratigraphy, monitoring status, spring elevation, summarized historic spring discharge, date of each spring discharge measurement, source of the discharge measurement, and results of geochemical analyses for sites sampled as part of the associated USGS Scientific Investigations Report. Some of the data presented here references sections of the the larger work which can be found here: Gingerich, S.B., Johnson, H.M., Boschmann, D.E., Grondin, G.H., and Garcia, C.A., 2022, Groundwater resources of the Harney Basin, southeastern Oregon: U.S. Geological Survey Scientific Investigations Report 2021–5103, 118 p., https://doi.org/10.3133/sir20215103.

This data release contains five comma separated value (csv) files that describe the location and water-quality data for wells, springs, and streams compiled for the U.S. Geological Survey (USGS) and Oregon Water Resources Department (OWRD) investigation of the groundwater resources of the Harney Basin, Oregon. The data included are site IDs, various site location information, well-construction details and hydrostratigraphy, monitoring status, spring elevation, summarized historic spring discharge, date of each spring discharge measurement, source of the discharge measurement, and results of geochemical analyses for sites sampled as part of the associated USGS Scientific Investigations Report. Some of the data presented here references sections of the the larger work which can be found here: Gingerich, S.B., Johnson, H.M., Boschmann, D.E., Grondin, G.H., and Garcia, C.A., 2022, Groundwater resources of the Harney Basin, southeastern Oregon: U.S. Geological Survey Scientific Investigations Report 2021–5103, 118 p., https://doi.org/10.3133/sir20215103.

Data release containing geospatial data and metadata for select hydrogeologic characteristics of the alluvium in the Lower Arkansas River Valley, Southeast Colorado, 2002, 2008, and 2015. This data release accompanies U.S. Geological Survey Scientific Investigations Map 3378 (https://doi.org/10.3133/sim3378). Geospatial datasets and metadata include: - Rasters showing estimated thickness of the alluvium; fall-to-fall and spring-to-spring water-table altitude change, 2002 to 2008, 2008 to 2015, and 2002 - 2015; and estimated saturated thickness in the alluvium, fall and spring 2002, 2008, and 2015. - Shapefiles showing bedrock contours underlying the alluvium; the outline of the study area and John Martin Reservoir in Bent County, Colorado; well locations and measured water-level altitude in those wells in the fall and spring of 2002, 2008, and 2015. For the purposes of this data release, "fall" is defined as June 1 through November 30, and "spring" is defined as January 1 through May 31 and December 1 through 31 of the same year.

This data set is a compilation of discrete and high frequency water quality data from sites on Allequash Creek in Wisconsin, and within the Allequash Creek watershed, for the water years (WY) 2019-2021.

The U.S. Geological Survey National Real-Time Water Quality (NRTWQ) Data Service (https://nrtwq.usgs.gov) provides computed concentrations and loads for sediment, nutrients, bacteria, and many additional constituents; uncertainty values and probabilities for exceeding drinking water or recreational criteria; frequency distribution curves; and all historical in-stream sensor measurements.

This data release contains eight datasets and metadata related to streamwater constituent load estimation and E. coli bacteria concentration predictions at 15 watersheds in DeKalb County, Georgia for 2012 to 2016 (the water-quality model calibration data goes through 9/22/2017 and the water-quality assurance samples goes through 11/7/2017). Loads were estimated for 15 constituents: biochemical oxygen demand, chemical oxygen demand, total suspended solids, suspended sediment concentration, total nitrogen, total nitrate plus nitrite, total phosphorus, dissolved phosphorus, total organic carbon, total calcium, total magnesium, total copper, total lead, total zinc, and total dissolved solids. The data release includes the following eight datasets: (1) daily base-flow separation results that were used as explanatory variables in the load estimation models; (2) water-quality assurance sample concentrations; (3) laboratory standard reference sample concentrations; (4) water-quality outliers that were excluded from the calibration datasets used in regression models for estimating streamwater constituent loads and E. coli bacteria concentrations; (5) calibration datasets containing explanatory variables for modeling constituent loads; (6) model coefficients and model diagnostic statistics used to estimate streamwater constituent loads, including portable document format files (pdf) with reports and plots for evaluating model fits; (7) time-step data used for estimating loads from the model coefficients; and (8) annual and period of record streamwater constituent load and yield estimates, including the 95-percent confidence intervals of the estimates.

This data release contains eight datasets and metadata related to streamwater constituent load estimation and E. coli bacteria concentration predictions at 15 watersheds in DeKalb County, Georgia for 2012 to 2016 (the water-quality model calibration data goes through 9/22/2017 and the water-quality assurance samples goes through 11/7/2017). Loads were estimated for 15 constituents: biochemical oxygen demand, chemical oxygen demand, total suspended solids, suspended sediment concentration, total nitrogen, total nitrate plus nitrite, total phosphorus, dissolved phosphorus, total organic carbon, total calcium, total magnesium, total copper, total lead, total zinc, and total dissolved solids. The data release includes the following eight datasets: (1) daily base-flow separation results that were used as explanatory variables in the load estimation models; (2) water-quality assurance sample concentrations; (3) laboratory standard reference sample concentrations; (4) water-quality outliers that were excluded from the calibration datasets used in regression models for estimating streamwater constituent loads and E. coli bacteria concentrations; (5) calibration datasets containing explanatory variables for modeling constituent loads; (6) model coefficients and model diagnostic statistics used to estimate streamwater constituent loads, including portable document format files (pdf) with reports and plots for evaluating model fits; (7) time-step data used for estimating loads from the model coefficients; and (8) annual and period of record streamwater constituent load and yield estimates, including the 95-percent confidence intervals of the estimates.

This section of the data release supports the data used in models for the associated publication. The U.S. Geological Survey and the University of Wisconsin – Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at Mainstem Plum Creek (USGS site ID: 04084911) and West Plum Creek (USGS site ID: 04084927) in northeastern Wisconsin. Monitoring data from 2010–2020 at Mainstem Plum and 2013–2020 at West Plum were used to detect changes in hydrologic and water-quality responses during runoff events. Runoff events were defined by hydrographers and used to compute event loads and event flow-weighted mean concentrations of total phosphorus and total suspended solids – all of which are included in this data release. The data included in this release serve as model inputs for the associated model archive. Models in the associated archive were used to assess changes in water quality between two time periods (“initial” and “post-CMP implementation”) while controlling for environmental factors, such as weather and the conditions preceding events to elucidate water-quality changes more directly associated with CMP implementation.