This posting contains the stream flow and load data used to run the SWAT model in the Tuckahoe subwatershed of the Choptank River watershed, Maryland.

This posting contains the source stream flow and load data used to run the SWAT model in the Greensboro subwatershed of the Choptank River watershed, Maryland.

This posting contains the source stream flow and load data used to run the SWAT model in the Greensboro subwatershed of the Choptank River watershed, Maryland.

This posting contains the source climate data used to run the SWAT model in the Tuckahoe and Greensboro subwatersheds of the Choptank River watershed, Maryland.

This posting contains the source climate data used to run the SWAT model in the Tuckahoe and Greensboro subwatersheds of the Choptank River watershed, Maryland.

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.

During 2015-17, the U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Agriculture Forest Service (USDA FS), carried out a study to characterize the hydrology and water chemistry in the Rock Creek and Cumberland River areas of the Daniel Boone National Forest. The study areas were historically mined for coal and have since been the focus of remediation efforts. The study examined the contributions of tributaries and gains/losses in both areas, and continuous water-quality and base flow estimates at Rock Creek, so the USDA FS can thoroughly evaluate the current conditions and move forward with well-informed remediation efforts.

During 2015-17, the U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Agriculture Forest Service (USDA FS), carried out a study to characterize the hydrology and water chemistry in the Rock Creek and Cumberland River areas of the Daniel Boone National Forest. The study areas were historically mined for coal and have since been the focus of remediation efforts. The study examined the contributions of tributaries and gains/losses in both areas, and continuous water-quality and base flow estimates at Rock Creek, so the USDA FS can thoroughly evaluate the current conditions and move forward with well-informed remediation efforts.

In 2019, the U.S. Geological Survey (USGS), in cooperation with the National Park Service(NPS), initiated a study using surrogate technology to predict real-time metallic-contaminant concentrations (MCCs) in the Clark Fork at two USGS streamgages that bracket Grant-Kohrs Ranch National Historic Site (GRKO) near Deer Lodge, Montana. Clark Fork at Deer Lodge (streamgage 12324200), Mont., about one mile upstream from GRKO, and Clark Fork above Little Blackfoot River near Garrison (streamgage 12324400), Mont., about 12 miles downstream from GRKO property were instrumented with turbidity and acoustic sensors for monitoring the Clark Fork during NPS Superfund remediation activities. Time-series data from backscatter signals from fixed-point turbidity and acoustic sensors were correlated with discrete MCC samples collected from the Clark Fork and were used as surrogates for estimating real-time cadmium, copper, iron, lead, manganese, zinc, and the metalloid trace element arsenic. A stepwise regression approach was used to develop statistical models to predict MCCs based on instantaneous values of turbidity and acoustic backscatter. Simple linear regression (SLR) models using turbidity as the sole explanatory variable produced the best models with R-squared values exceeding 0.90 in 9 of 12 models. Nash-Sutcliffe Efficiency values were used to evaluate the effectiveness of predictive models to approximate measured MCCs, and model biases were calculated as an additional check on model accuracy. The R-LOADEST statistical package was used to compute annual and daily metallic-contaminant loads (MCLs) along with 95-percent prediction intervals. R-LOADEST loads were compared to time-series computed loads to evaluate the applicability of time-series data for calculating daily and annual MCLs. Results from annual load estimates indicated an increase in loads for all metallic contaminants between the two monitoring sites. These results provided real-time information to NPS management for evaluating variation in water quality during Superfund remediation, comparing MCC values relative to aquatic life standards, and will help quantify benefits from NPS remediation activities.