This USGS data release contains 2013 streamflow, baseflow, and precipitation data from three hydrologically-diverse streams in the United States used to develop a conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities. The framework combined generalized concepts on the movement of water, the environmental behavior of chemicals and eroded soil, and the designed functions of various agricultural activities. The framework addresses the impacts on water quality of a broad range of agricultural chemicals and sediment across a variety of hydrologic settings. • Chesterville Branch near Crumpton, Maryland, (USGS site ID - 01493112) had substantial baseflow throughout the year with increased streamflow within a day of rainfall. • Indian Creek at State Line RD, Leawood, Kansas (USGS site ID - 06893390) was a fastflow-dominated urban steam that was not well connected to shallow groundwater. • The watershed of Leary-Weber Ditch at Mohawk, Indiana (USGS site ID - 03361638) has an extensive subsurface drainage network within its watershed. These data support the following publication: Capel, P.D., Wolock, D.M., Coupe, R.H., and Roth, J.L., 2017, A conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities: U.S. Geological Survey Scientific Investigations Report 2017-5095, 35 p., https://doi.org/10.3133/sir20175095.

This data release contains site information and potential explanatory factor data for 1,899 groundwater sites. These sites were used to assess groundwater quality in aquifers used for domestic and public drinking water supply in the southeastern San Joaquin Valley. The southeastern San Joaquin Valley (SESJV) study unit consists of five study areas whose boundaries are defined by the eponymous California Department of Water Resources groundwater subbasins of the San Joaquin Valley groundwater basin: Madera-Chowchilla, Kings, Kaweah, Tule, and Tulare Lake. The sites consist of 198 wells representing the domestic-supply aquifer and 1,701 wells representing the public-supply aquifer. The domestic-supply aquifer wells were sampled in 2013-2015 by the USGS for either the California Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP) or the USGS National Water Quality Assessment project (NAWQA). The public-supply aquifer wells were either sampled by the USGS for the GAMA-PBP in 2005-2018 or have water-quality data in the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) public database. The data types in this data release include site identification and location, well construction and lithology data, land use characteristics, groundwater age and oxidation-reduction classifications and aridity indices. Not all sites have data for all fields. Water-quality data for the sites are available from U.S. Geological Survey (2023), and California State Water Resources Control Board Division of Drinking Water (2023). The study design and the assessment results are presented in Burow and others (2024).

We developed four 2011-2050 land cover change scenarios and modeled the impact of projected land cover change on influent water quality to support long-term planning for the city of Minneapolis, MN. Baseline land cover was from the NLCD2011 database. IPCC SRES scenarios (https://www.ipcc.ch/site/assets/uploads/2018/03/sres-en.pdf) interpreted by Sohl et al. (2014) (https://doi.org/10.1890/13-1245.1) for the conterminous United States were downscaled from 250 meters to 30 meters. The baseline and scenario land cover data were used as input into the Soil Water and Assessment Tool (SWAT) model to assess the effect of outyear projected land cover change on the source of raw water for the city of Minneapolis, MN. SWAT was used to assess the effect of land cover change on raw water concentrations of sediment, total nitrogen, and total phosphorus. The IPCC SRES evaluated were A1B, A2, B1, and B2. The four scenarios were implemented with (A1Bf) and without (A1B) forest recovery (e.g., conversion of cropland (2011) to forest (2050). Data on treatment of raw (source) water quality, provided by the city of Minneapolis, MN, were used in autoregressive models to determine if there was a temporal trend in mass of treatment chemicals applied. Models were run separately for each treatment chemical. Data are monthly application rates from 2008 through 2017. The day of the month for the date variable was nominally set to one (1). Data for alum were incomplete from 2008 through 2011, which were set to zero (0) and treated as missing in the autoregressive model. Water volume treated is in megagallons (Mg); 1 Mg = 1000 gallons. A dummy variable for change in management philosphy was included in the model. The dummy variable was set to zero (0) for the period 2008 - 2014 and one (1) afterward. The dummy variable is not included in the file. It had a significant effect only for the CO2 treatment chemical.

We developed four 2011-2050 land cover change scenarios and modeled the impact of projected land cover change on influent water quality to support long-term planning for the city of Minneapolis, MN. Baseline land cover was from the NLCD2011 database. IPCC SRES scenarios (https://www.ipcc.ch/site/assets/uploads/2018/03/sres-en.pdf) interpreted by Sohl et al. (2014) (https://doi.org/10.1890/13-1245.1) for the conterminous United States were downscaled from 250 meters to 30 meters. The baseline and scenario land cover data were used as input into the Soil Water and Assessment Tool (SWAT) model to assess the effect of outyear projected land cover change on the source of raw water for the city of Minneapolis, MN. SWAT was used to assess the effect of land cover change on raw water concentrations of sediment, total nitrogen, and total phosphorus. The IPCC SRES evaluated were A1B, A2, B1, and B2. The four scenarios were implemented with (A1Bf) and without (A1B) forest recovery (e.g., conversion of cropland (2011) to forest (2050). Data on treatment of raw (source) water quality, provided by the city of Minneapolis, MN, were used in autoregressive models to determine if there was a temporal trend in mass of treatment chemicals applied. Models were run separately for each treatment chemical. Data are monthly application rates from 2008 through 2017. The day of the month for the date variable was nominally set to one (1). Data for alum were incomplete from 2008 through 2011, which were set to zero (0) and treated as missing in the autoregressive model. Water volume treated is in megagallons (Mg); 1 Mg = 1000 gallons. A dummy variable for change in management philosphy was included in the model. The dummy variable was set to zero (0) for the period 2008 - 2014 and one (1) afterward. The dummy variable is not included in the file. It had a significant effect only for the CO2 treatment chemical.

The U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Natural Resources (MDNR), collects data pertaining to the surface-water resources of Missouri. These data are collected as part of the Missouri Ambient Water-Quality Monitoring Network (AWQMN) and are stored and maintained by the USGS National Water Information System (NWIS) database. These data constitute a valuable source of reliable, impartial, and timely information for developing an improved understanding of the water resources of the State. Water-quality data collected between 1993 and 2017 were analyzed for long term trends and the network was investigated to identify data gaps or redundant data to assist MDNR on how to optimize the network in the future. This is a companion data release product to the Scientific Investigation Report: Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079. The following selected tables are included in this data release in compressed (.zip) format: AWQMN_EGRET_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for network analysis of the Missouri AWQMN AWQMN_R-QWTREND_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for analysis of flow-weighted trends for selected sites in the Missouri AWQMN AWQMN_R-QWTREND_outliers.xlsx -- Data flagged as outliers during analysis of flow-weighted trends for selected sites in the Missouri AWQMN AWQMN_R-QWTREND_outliers_quarterly.xlsx -- Data flagged as outliers during analysis of flow-weighted trends using a simulated quarterly sampling frequency dataset for selected sites in the Missouri AWQMN AWQMN_descriptive_statistics_WY1993-2017.xlsx -- Descriptive statistics for selected water-quality parameters at selected sites in the Missouri AWQMN

The U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Natural Resources (MDNR), collects data pertaining to the surface-water resources of Missouri. These data are collected as part of the Missouri Ambient Water-Quality Monitoring Network (AWQMN) and are stored and maintained by the USGS National Water Information System (NWIS) database. These data constitute a valuable source of reliable, impartial, and timely information for developing an improved understanding of the water resources of the State. Water-quality data collected between 1993 and 2017 were analyzed for long term trends and the network was investigated to identify data gaps or redundant data to assist MDNR on how to optimize the network in the future. This is a companion data release product to the Scientific Investigation Report: Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079. The following selected tables are included in this data release in compressed (.zip) format: AWQMN_EGRET_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for network analysis of the Missouri AWQMN AWQMN_R-QWTREND_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for analysis of flow-weighted trends for selected sites in the Missouri AWQMN AWQMN_R-QWTREND_outliers.xlsx -- Data flagged as outliers during analysis of flow-weighted trends for selected sites in the Missouri AWQMN AWQMN_R-QWTREND_outliers_quarterly.xlsx -- Data flagged as outliers during analysis of flow-weighted trends using a simulated quarterly sampling frequency dataset for selected sites in the Missouri AWQMN AWQMN_descriptive_statistics_WY1993-2017.xlsx -- Descriptive statistics for selected water-quality parameters at selected sites in the Missouri AWQMN

The United States Geological Survey’s (USGS) SPAtially Referenced Regressions On Watershed attributes (SPARROW) model was developed to aid in the interpretation of monitoring data and simulate water-quality conditions in streams across large spatial scales. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based streamflow and water-quality load estimates. Streamflow and load estimates considered for use in regional SPARROW model applications (2012 base year) are described in Saad and others, 2019 (https://dx.doi.org/10.3133/sir20195069). Load estimation methods described in this report include the Beale Ratio Estimator and Fluxmaster models. This USGS data release contains all of the input and output files necessary to reproduce the load estimates considered for inclusion in the 2012 regional SPARROW models. Data preparation for input to the load estimation models is also fully described in the above-mentioned report.

The United States Geological Survey’s (USGS) SPAtially Referenced Regressions On Watershed attributes (SPARROW) model was developed to aid in the interpretation of monitoring data and simulate water-quality conditions in streams across large spatial scales. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based streamflow and water-quality load estimates. Streamflow and load estimates considered for use in regional SPARROW model applications (2012 base year) are described in Saad and others, 2019 (https://dx.doi.org/10.3133/sir20195069). Load estimation methods described in this report include the Beale Ratio Estimator and Fluxmaster models. This USGS data release contains all of the input and output files necessary to reproduce the load estimates considered for inclusion in the 2012 regional SPARROW models. Data preparation for input to the load estimation models is also fully described in the above-mentioned report.