This data is for 60 water quality monitoring sites in the Right Fork of Beaver Creek watershed in Eastern Kentucky where specific conductivity (SC) was measured quarterly for two years from December 2012 to August 2014. SC was modeled as a function of land use covariates and spatial autocorrelation between sites on the stream network, and by doing so we could compare predictions of the average SC for different portions of the network and identify areas of low and high SC. The htmls files can be opened with a browser such as Internet Explorer or Chrome. This dataset is associated with the following publication: McManus, M., E. DAmico, E. Smith, R. Polinsky, J. Ackerman, and K. Tyler. Variation in stream network relationships and geospatial predictions of watershed conductivity. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 39(4): 1-18, (2020).

This data is for 60 water quality monitoring sites in the Right Fork of Beaver Creek watershed in Eastern Kentucky where specific conductivity (SC) was measured quarterly for two years from December 2012 to August 2014. SC was modeled as a function of land use covariates and spatial autocorrelation between sites on the stream network, and by doing so we could compare predictions of the average SC for different portions of the network and identify areas of low and high SC. The htmls files can be opened with a browser such as Internet Explorer or Chrome. This dataset is associated with the following publication: McManus, M., E. DAmico, E. Smith, R. Polinsky, J. Ackerman, and K. Tyler. Variation in stream network relationships and geospatial predictions of watershed conductivity. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 39(4): 1-18, (2020).

This dataset shows the observed and predicted values of specific conductivity at 60 water quality monitoring sites in the Right Fork of Beaver Creek watershed in Eastern Kentucky where specific conductivity (SC) was measured quarterly for two years from December 2012 to August 2014. SC was modeled as a function of land use covariates and spatial autocorrelation between sites on the stream network, and by doing so we could compare predictions of the average SC for different portions of the network and identify areas of low and high SC. This dataset is associated with the following publication: McManus, M., E. DAmico, E. Smith, R. Polinsky, J. Ackerman, and K. Tyler. Variation in stream network relationships and geospatial predictions of watershed conductivity. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 39(4): 1-18, (2020).

This data release makes available three data tables supporting a spatiotemporal analysis of riverine conductivity and streamflow trends within the Delaware River Basin. The listed datasets include baseflow and total flow time series for selected gaged basins, watershed attributes, water quality information and trend analysis results.

This data release makes available three data tables supporting a spatiotemporal analysis of riverine conductivity and streamflow trends within the Delaware River Basin. The listed datasets include baseflow and total flow time series for selected gaged basins, watershed attributes, water quality information and trend analysis results.

This data compares spatial autocovariance models used in the modeling and prediction of specific conductivity over 8 sampling periods in an Eastern Kentucky watershed. This dataset is associated with the following publication: McManus, M., E. DAmico, E. Smith, R. Polinsky, J. Ackerman, and K. Tyler. Variation in stream network relationships and geospatial predictions of watershed conductivity. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 39(4): 1-18, (2020).

In 2013, the first of several Regional Stream Quality Assessments (RSQA) was done in the Midwest United States. The Midwest Stream Quality Assessment (MSQA) was a collaborative study by the U.S. Geological Survey (USGS) National Water Quality Assessment (NAWQA), the USGS Columbia Environmental Research Center, and the U.S. Environmental Protection Agency (USEPA) National Rivers and Streams Assessment (NRSA). One of the objectives of the RSQA, and thus the MSQA, is to characterize the relationships between water-quality stressors and stream ecology and to determine the relative effects of these stressors on aquatic biota within the streams (U.S. Geological Survey, 2012a). To meet this objective, a framework of fundamental geospatial data was required to develop physical and anthropogenic characteristics of the study region, sampled sites and corresponding watersheds, and riparian zones. This dataset represents the boundaries for the 100 watersheds studied in the MSQA, and is one of the four fundamental geospatial data layers that were developed for the Midwest study.

In 2013, the first of several Regional Stream Quality Assessments (RSQA) was done in the Midwest United States. The Midwest Stream Quality Assessment (MSQA) was a collaborative study by the U.S. Geological Survey (USGS) National Water Quality Assessment (NAWQA), the USGS Columbia Environmental Research Center, and the U.S. Environmental Protection Agency (USEPA) National Rivers and Streams Assessment (NRSA). One of the objectives of the RSQA, and thus the MSQA, is to characterize the relationships between water-quality stressors and stream ecology and to determine the relative effects of these stressors on aquatic biota within the streams (U.S. Geological Survey, 2012). To meet this objective, a framework of fundamental geospatial data was required to develop physical and anthropogenic characteristics of the study region, sampled sites and corresponding watersheds, and riparian zones. This dataset defines the geographic extent of the MSQA, and is one of the four fundamental geospatial data layers that were developed for the Midwest study.

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.