Long-term water-quality trends for rivers and streams within the contiguous United States using Weighted Regressions on Time, Discharge, and Season (WRTDS)
Dates
Publication Date
2023-02-12
Start Date
1900-10-20
End Date
2023-08-01
Citation
Goodling, P.J., Oelsner, G.P., Hecht, J.S., Cherry, M.L., Johnson, Z.C., Koenig, L. E., and Headman, A.O., 2024, Long-term water-quality trends for rivers and streams within the contiguous United States using Weighted Regressions on Time, Discharge, and Season (WRTDS): U.S. Geological Survey data release, https://doi.org/10.5066/P914BQYS.
Summary
The U.S. Geological Survey (USGS) Water Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand. It is also improving understanding of the natural and human factors affecting the balance between supply and demand. A key part of these national assessments is identifying long-term trends in water availability, including groundwater and surface water quantity, quality, and use. To describe the long-term trends in the surface water quality component of water availability, data from the USGS and other Federal, State, and local agencies were accessed primarily through the US [...]
Summary
The U.S. Geological Survey (USGS) Water Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand. It is also improving understanding of the natural and human factors affecting the balance between supply and demand. A key part of these national assessments is identifying long-term trends in water availability, including groundwater and surface water quantity, quality, and use. To describe the long-term trends in the surface water quality component of water availability, data from the USGS and other Federal, State, and local agencies were accessed primarily through the US EPA's Water Quality Portal (https://www.waterqualitydata.us/) in 2023 and used in trend analyses. This USGS data release contains all the input and output files necessary to reproduce the results from the Weighted Regressions on Time, Discharge, and Season (WRTDS) models, using data preparation methods described in Oelsner and others, 2017 for individual monitoring locations. Models were calibrated for each combination of site and parameter using the screened input data. Models were run on Tallgrass, the USGS supercomputer, in separate run for each parameter. Once calibrated, the WRTDS models were initially evaluated using a logistic regression equation that estimated a probability of acceptance for each model (e.g., "a good fit") based on a set of diagnostic metrics derived from the observed, estimated, and residual values from each model and data set (Murphy and Chanat, 2023). Each WRTDS model was assigned to one of three categories: “auto-accept,” “auto-reject,” or “manual evaluation". Models assigned to the latter category were visually evaluated for appropriate model fit using residual and diagnostic plots. Models assigned to the first two categories were automatically included or rejected from the final results, respectively. Seven water-quality parameters were assessed, including nutrients (nitrate, filtered orthophosphate, total nitrogen, and total phosphorus), salinity indicators (chloride and specific conductance), and sediment (suspended sediment concentration). Trends are reported for three trend periods: 1980-2020, 2000-2020, and the longest period of record at each site.
Water Quality Portal. Washington (DC): National Water Quality Monitoring Council, United States Geological Survey (USGS), Environmental Protection Agency (EPA); 2023.
Murphy, J., and Chanat, J., 2023, Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies: Environmental Modelling & Software, v. 170, p. 105864.
The purpose of this data release is to provide the best available information on trends in seven water quality constituents in streams. The data can be used to infer the causes of these trends and to predict the effect of these trends at monitored locations.
