This dataset includes input and output data from streamflow and water-quality regression models used to characterize streamflow and water-quality conditions in the Upper Yampa River Basin, Colorado, from 1999 to 2018. All input data, including discrete and continuous streamflow records and discrete concentrations of suspended sediment, Kjeldahl nitrogen, and total phosphorus, were compiled from the U.S. Geological Survey (USGS) National Water Information System (NWIS) database. Input data were used in multiple models including Maintenance of Variance Extension Type 2 (MOVE.2), LOAD ESTimator (LOADEST), and Weighted Regressions on Time, Discharge, and Season (WRTDS) to estimate continuous streamflow records, daily concentrations and loads, and streamflow-normalized annual mean concentrations and loads of selected water-quality constituents.

This dataset includes input and output data from streamflow and water-quality regression models used to characterize streamflow and water-quality conditions in the Upper White River Basin, Colorado, from 2000 to 2020. All input data, including discrete and continuous streamflow records and discrete concentrations of inorganic nitrogen, total nitrogen, and total phosphorus, were compiled from the U.S. Geological Survey (USGS) National Water Information System (NWIS) database. Input data were used in multiple models including Maintenance of Variance Extension Type 2 (MOVE.2) and Weighted Regressions on Time, Discharge, and Season (WRTDS) to estimate continuous streamflow records, daily concentrations and loads, and streamflow-normalized annual mean concentrations and loads of selected water-quality constituents.

This dataset includes input and output data from streamflow and water-quality regression models used to characterize streamflow and water-quality conditions in the Upper White River Basin, Colorado, from 2000 to 2020. All input data, including discrete and continuous streamflow records and discrete concentrations of inorganic nitrogen, total nitrogen, and total phosphorus, were compiled from the U.S. Geological Survey (USGS) National Water Information System (NWIS) database. Input data were used in multiple models including Maintenance of Variance Extension Type 2 (MOVE.2) and Weighted Regressions on Time, Discharge, and Season (WRTDS) to estimate continuous streamflow records, daily concentrations and loads, and streamflow-normalized annual mean concentrations and loads of selected water-quality constituents.

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.

This data release contains the input and output used to assess the potential effect of climate on streamflow and salinity (measured as total dissolved solids [TDS]) yields across the Upper Colorado River Basin from water years 1986 to 2021. This analysis included estimation of the spatiotemporal variability in mean annual climatic variables (air temperature, snow water equivalent, precipitation and antecedent precipitation), mean annual streamflow yields, and mean annual TDS yields at 34 sites within the basin. Generalized Additive Models (GAMs) were used to look at non-linear trends in streamflow and TDS yields in the Upper Colorado River Basin. GAMs were also used to create attribution models that explain temporal variability in streamflow and TDS using climate variables (precipitation, snow, and air temperature). A detailed description of the analysis is provided in the associated journal article.

For about 10 years, the U.S. Geological Survey (USGS) has monitored water quality and streamflow in three agricultural drainage ditches in an effort to evaluate the influence of best management practices on water quality. These ditches are small tributaries to oxbow lakes located in the Mississippi Alluvial Plain of northwestern Mississippi--two sites (LWSR and LWT2) drain to Lake Washington and one site (BLT1) drains to Bee Lake. Streamflow was intermittent at these sites and the ditches were dry much of the year. When streamflow was present, flows were measured on 15-minute intervals and water-quality samples were collected over the course of the flow event using an automated sampler. These datasets were aggregated by flow event and include various flow statistics (mean flow, peak flow, total flow volume, and event duration), flow-weighted mean concentration (total constituent load divided by total flow volume) and total constituent load for each flow event. The water-quality constituents include total nitrogen, organic nitrogen, ammonia, ammonia plus organic nitrogen (total Kjeldahl nitrogen), nitrate plus nitrite, total phosphorus, organic carbon, chloride and suspended sediment; USGS parameter codes 00600, 00605, 00610, 00625, 00630, 00665, 00680, 99220, and 80154. All samples were unfiltered. Data were collected from approximately 2007-2016, depending on the site.

For about 10 years, the U.S. Geological Survey (USGS) has monitored water quality and streamflow in three agricultural drainage ditches in an effort to evaluate the influence of best management practices on water quality. These ditches are small tributaries to oxbow lakes located in the Mississippi Alluvial Plain of northwestern Mississippi--two sites (LWSR and LWT2) drain to Lake Washington and one site (BLT1) drains to Bee Lake. Streamflow was intermittent at these sites and the ditches were dry much of the year. When streamflow was present, flows were measured on 15-minute intervals and water-quality samples were collected over the course of the flow event using an automated sampler. These datasets were aggregated by flow event and include various flow statistics (mean flow, peak flow, total flow volume, and event duration), flow-weighted mean concentration (total constituent load divided by total flow volume) and total constituent load for each flow event. The water-quality constituents include total nitrogen, organic nitrogen, ammonia, ammonia plus organic nitrogen (total Kjeldahl nitrogen), nitrate plus nitrite, total phosphorus, organic carbon, chloride and suspended sediment; USGS parameter codes 00600, 00605, 00610, 00625, 00630, 00665, 00680, 99220, and 80154. All samples were unfiltered. Data were collected from approximately 2007-2016, depending on the site.

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.