Weighted Regression on Time, Discharge and Season (WRTDS) and WRTDS with Kalman filtering (WRTDS_K) models were developed for total and dissolved cadmium, zinc and lead; total phosphorus and nitrogen; and dissolved orthophosphate at twelve sites in the Spokane River watershed, northern Idaho, for water years 1990 to 2018. The data table contains the annual mean concentrations and annual total loads estimated by WRTDS_K, and the flow-normalized annual mean concentrations and flow-normalized annual total loads estimated by WRTDS for each modeled site and constituent.

Weighted Regression on Time, Discharge and Season (WRTDS) models were developed for total and dissolved cadmium, lead and zinc; total phosphorus and nitrogen; and dissolved orthophosphate at twelve sites in the Spokane River watershed for water years 1990 to 2018. Bootstrapped model replicates were run to determine the statistical likelihoods of trends in flow-normalized concentrations and loads over the period of record and water years 2009-2018 for each constituent at each site. The data table contains the change in concentrations and loads and the numeric and descriptive statistical likelihoods associated with the trends over the two time periods for each constituent and site.

Weighted Regression on Time, Discharge and Season (WRTDS) models were developed for total and dissolved cadmium, lead and zinc; total phosphorus and nitrogen; and dissolved orthophosphate at twelve sites in the Spokane River watershed for water years 1990 to 2018. Bootstrapped model replicates were run to determine the statistical likelihoods of trends in flow-normalized concentrations and loads over the period of record and water years 2009-2018 for each constituent at each site. The data table contains the change in concentrations and loads and the numeric and descriptive statistical likelihoods associated with the trends over the two time periods for each constituent and site.

In 2019, the U.S. Geological Survey (USGS), in cooperation with the National Park Service, 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 National Park Service 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 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 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 metallic-contaminant loads. 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 National Park Service management for evaluating variation in water quality during Superfund remediation, comparing MCC values relative to aquatic life standards, and will help quantify benefits from Superfund remediation activities.

In 2019, the U.S. Geological Survey (USGS), in cooperation with the National Park Service, 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 National Park Service 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 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 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 metallic-contaminant loads. 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 National Park Service management for evaluating variation in water quality during Superfund remediation, comparing MCC values relative to aquatic life standards, and will help quantify benefits from Superfund remediation activities.

Total dissolved solids (TDS) in surface waters affect water quality and useability and are of particular concern in the Colorado River Basin. Estimates of current and past TDS concentration and flux in rivers support appropriate management and salinity control measures. In this study we estimated the total dissolved solid concentration and flux at 11 sites on tributaries of the Colorado River, starting as early as 1938 until 2021. Of these sites, eight were not affected by dams. For these we estimated daily and water year flow normalized TDS concentration and flux using the Weighted Regressions on Time Discharge and Season (WRTDS) water quality modeling framework. For the three sites situated below dams, we used flow-weighted concentrations to estimate water year mean TDS concentrations. Along with these estimates, we include information about the timing of construction, and capacity of dams in the Lower Colorado River Basin.

Total dissolved solids (TDS) in surface waters affect water quality and useability and are of particular concern in the Colorado River Basin. Estimates of current and past TDS concentration and flux in rivers support appropriate management and salinity control measures. In this study we estimated the total dissolved solid concentration and flux at 11 sites on tributaries of the Colorado River, starting as early as 1938 until 2021. Of these sites, eight were not affected by dams. For these we estimated daily and water year flow normalized TDS concentration and flux using the Weighted Regressions on Time Discharge and Season (WRTDS) water quality modeling framework. For the three sites situated below dams, we used flow-weighted concentrations to estimate water year mean TDS concentrations. Along with these estimates, we include information about the timing of construction, and capacity of dams in the Lower Colorado River Basin.

As part of the Great Lakes Restoration Initiative, the U.S. Department of Agriculture, Natural Resources Conservation Service; U.S. Environmental Protection Agency; and the U.S. Geological Survey (USGS) have partnered to evaluate agricultural conservation practices focused on nutrient management. Monitoring methods allow for rapid assessment of water-quality changes in response to conservation efforts by focusing on subsurface-tile drainage and direct surface runoff from fields. Estimated daily loads presented within this dataset are from one surface-runoff monitoring station (USGS station identification number 0414826544; approximated drainage area of 60.6 hectare) and one tile-runoff monitoring station (USGS station identification number 0414826545; approximated drainage area of 28.6 hectare). The monitored field is a row-crop parcel planted in a biennial corn-soybean crop rotation. Best-management practices were applied during part of the monitoring period: cover crops were planted in the fall of each year from 2014 to 2016; nutrient management methods were employed and a filter strip was installed on the field each spring from 2015 to 2017.

As part of the Great Lakes Restoration Initiative, the U.S. Department of Agriculture, Natural Resources Conservation Service; U.S. Environmental Protection Agency; and the U.S. Geological Survey (USGS) have partnered to evaluate agricultural conservation practices focused on nutrient management. Monitoring methods allow for rapid assessment of water-quality changes in response to conservation efforts by focusing on subsurface-tile drainage and direct surface runoff from fields. Estimated daily loads presented within this dataset are from one surface-runoff monitoring station (USGS station identification number 0414826544; approximated drainage area of 60.6 hectare) and one tile-runoff monitoring station (USGS station identification number 0414826545; approximated drainage area of 28.6 hectare). The monitored field is a row-crop parcel planted in a biennial corn-soybean crop rotation. Best-management practices were applied during part of the monitoring period: cover crops were planted in the fall of each year from 2014 to 2016; nutrient management methods were employed and a filter strip was installed on the field each spring from 2015 to 2017.

This data release provides water-quality trends for rivers and streams in the Delaware River Basin determined using the Weighted Regressions on Time, Discharge, and Season (WRTDS) model and the Seasonal Kendall Trend (SKT) test. Sixteen water-quality parameters were assessed, including nutrients (ammonia, nitrate, filtered orthophosphate, total nitrogen, total phosphorus, and unfiltered orthophosphate), major ions (calcium, chloride, magnesium, potassium, sodium, and sulfate), salinity indicators (total dissolved solids and specific conductance), and sediment (total suspended solids and suspended sediment concentration). The child items include the input and output data used in the modeling and testing of water-quality trends. The attached files include the scripts used in these analyses, a readMe files for these scripts and tables summarizing information about the sites used in the analysis. These trends build off the national efforts of Oelsner and others (2017) and Murphy and others (2018), with some variations in data screening and processing. One major divergence from these previous efforts was that screened site-parameter combinations were screened for the longest period of record that passed various temporal and seasonal criteria ("maximum calibration" approach) instead of screening by pre-defined trend periods. An additional difference was that water-quality data were combined from multiple monitoring locations and collecting organizations using hierarchical clustering based on the distance between monitoring locations on the same stream reach (as determined by the National Hydrography Dataset comid). Data that were a part of these "cluster sites" were manually reviewed prior to running SKT and WRTDS. Input data for SKT includes 124 sites (including individual sites and cluster sites) and 1,208 site-parameter combinations. Input data for WRTDS, which required additional screening beyond those used for the SKT test and a paired streamflow gage, includes 62 sites and 476 site-parameter combinations. For both methods, some site-parameter combinations were not run due to the amount of censored data, or the results were rejected due to poor model fit. Trends are reported for four trend periods (1978-2018, 1998-2018, 2003-2018, and 2008-2018), as the available screened data allow, and for the entire screened period of record for each parameter at each site. This collection of trend results leverages the monitoring efforts of many collecting organizations across the Delaware River Basin and can serve to better understand changing water-quality conditions across this basin. References Cited: Murphy, J.C., Farmer, W.H., Sprague, L.A., De Cicco, L.A., and Hirsch, R.M., 2018, Water-quality trends and trend component estimates for the Nation's rivers and streams using Weighted Regressions on Time, Discharge, and Season (WRTDS) models and generalized flow normalization, 1972-2012: U.S. Geological Survey data release, https://doi.org/10.5066/F7TQ5ZS3. Oelsner, G.P., Sprague, L.A., Murphy, J.C., Zuellig, R.E., Johnson, H.M., Ryberg, K.R., Falcone, J.A., Stets, E.G., Vecchia, A.V., Riskin, M.L., De Cicco, L.A., Mills, T.J., Farmer, W.H., 2017, Water-quality trends in the Nation’s rivers and streams 1972–2012—Data preparation, statistical methods, and trend results: U.S. Geological Survey Scientific Investigations Report, http://dx.doi.org/10.3133/sir20175006. Shoda, M.E., Murphy, J.C., Falcone, J.A., and Duris, J.W., 2019, Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin: U.S. Geological Survey data release, https://doi.org/10.5066/P9PX8LZO. National Water Quality Monitoring Council, Water Quality Portal (WQP), https://www.waterqualitydata.us/. Accessed 2020-11-03.