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.

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 and West Plum Creek 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. Additionally, changes in these parameters were assessed between two time periods (“initial” and “post-CMP implementation”) using the R scripts included in this model archive. Because event discharges, loads, and concentrations are influenced by factors such as weather and the conditions preceding events, random-forest and regression models were developed to control for these factors and to elucidate water-quality changes more directly associated with CMP implementation. Residuals from random-forest models were used to detect changes between the two time periods via Wilcoxon signed-rank tests, and multiple linear regression models were used to determine percent change in responses via time-period dummy variable coefficients. Results indicate statistically insignificant changes in most responses during runoff events. This parent page serves as a landing page for two child items associated with Water Quality and Estimated Changes in the Plum Creek Watershed 2010-2020. Data release child page: contains the event times, event loads, and flow-weighted mean concentrations used for modeling purposes. Model archive child page: contains the inputs, scripts, and outputs used and produced to evaluate changes in water quality associated with conservation management practice implementation.

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 and West Plum Creek 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. Additionally, changes in these parameters were assessed between two time periods (“initial” and “post-CMP implementation”) using the R scripts included in this model archive. Because event discharges, loads, and concentrations are influenced by factors such as weather and the conditions preceding events, random-forest and regression models were developed to control for these factors and to elucidate water-quality changes more directly associated with CMP implementation. Residuals from random-forest models were used to detect changes between the two time periods via Wilcoxon signed-rank tests, and multiple linear regression models were used to determine percent change in responses via time-period dummy variable coefficients. Results indicate statistically insignificant changes in most responses during runoff events. This parent page serves as a landing page for two child items associated with Water Quality and Estimated Changes in the Plum Creek Watershed 2010-2020. Data release child page: contains the event times, event loads, and flow-weighted mean concentrations used for modeling purposes. Model archive child page: contains the inputs, scripts, and outputs used and produced to evaluate changes in water quality associated with conservation management practice implementation.

This section of the data release supports an archive of the models used in 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 and West Plum Creek 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 the associated data release. Additionally, changes in these parameters were assessed between two time periods (“initial” and “post-CMP implementation”) using the models included in this model archive. Because event discharges, loads, and concentrations are influenced by factors such as weather and the conditions preceding events, random-forest and regression models were developed to control for these factors and to elucidate water-quality changes more directly associated with CMP implementation. Residuals from random-forest models were used to detect changes between the two time periods via Wilcoxon signed-rank tests, and multiple linear regression models were used to determine percent change in responses via time-period dummy variable coefficients. Results indicate statistically insignificant changes in most responses during runoff events.

This section of the data release supports an archive of the models used in 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 and West Plum Creek 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 the associated data release. Additionally, changes in these parameters were assessed between two time periods (“initial” and “post-CMP implementation”) using the models included in this model archive. Because event discharges, loads, and concentrations are influenced by factors such as weather and the conditions preceding events, random-forest and regression models were developed to control for these factors and to elucidate water-quality changes more directly associated with CMP implementation. Residuals from random-forest models were used to detect changes between the two time periods via Wilcoxon signed-rank tests, and multiple linear regression models were used to determine percent change in responses via time-period dummy variable coefficients. Results indicate statistically insignificant changes in most responses during runoff events.

This data release contains a data compilation and analysis of the hydrogeology in the U.S. portion of the Lake Superior watershed, for the purpose of providing background data for future study and modeling of groundwater and contaminant movement in the watershed. The data support an analysis of groundwater contributions to the water budget of Lake Superior and provide hydrogeologic context for future modeling and sampling of groundwater in the region. The data release contains three child items: Baseflow analysis for tributaries to Lake Superior from 1946 to 2020; Geohydrology data for groundwater analysis in the Lake Superior Watershed; and Groundwater wells from Minnesota, Wisconsin, and Michigan state databases and the U.S. Geological (USGS) National Water Information System (NWIS) database with static water level data within 10km of the Lake Superior watershed.

This data release contains a data compilation and analysis of the hydrogeology in the U.S. portion of the Lake Superior watershed, for the purpose of providing background data for future study and modeling of groundwater and contaminant movement in the watershed. The data support an analysis of groundwater contributions to the water budget of Lake Superior and provide hydrogeologic context for future modeling and sampling of groundwater in the region. The data release contains three child items: Baseflow analysis for tributaries to Lake Superior from 1946 to 2020; Geohydrology data for groundwater analysis in the Lake Superior Watershed; and Groundwater wells from Minnesota, Wisconsin, and Michigan state databases and the U.S. Geological (USGS) National Water Information System (NWIS) database with static water level data within 10km of the Lake Superior watershed.

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.

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.