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).

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 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 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.

Wadeable stream habitat data from four long-term monitoring programs (AIM, AREMP, NRSA, PIBO MP) were obtained, pre-processed, transformed, and combined using R code following the Stream Habitat Metrics Integration (SHMI) Data Exchange Standard (Scully et al., 2023b). The dataset includes 26 stream habitat metrics collected between 2000 and 2022 across the United States at ~12,000 locations from ~19,000 data collection events for a total of ~200,000 measurements. Measurements include reach characteristics (sampled reach length, channel gradient, sinuosity), channel dimensions (bankfull width and height, average bankfull width to depth ratio, mean thalweg depth, average wetted width), channel substrate particle sizes (percent fines, percent bedrock, fine sediment percentiles), pools (residual pool depth, pool tail fines), bank characterizations (angle), and water quality/chemistry (specific conductance, pH, specific conductance, turbidity, total nitrogen, total phosphorous). The dataset consists of 4 csv files: 'RecordLevel.csv', 'Location.csv', 'Event.csv', and 'MeasurementOrFact.csv'. The 4 csv data tables may be linked in a database structure using the 'entity relationship diagram.jpg' or by linking the following: Join RecordLevel primary key 'datasetID' to Location foreign key 'datasetID'. Join Location primary key 'locationID to Event foreign key 'locationID'. Join Event primary key 'eventID' to MeasurementOrFact foreign key 'eventID'. An analysis-ready file ('AnalysisStreamHabitatMonitoringMetricDataset.csv') is also published for user convenience.

Wadeable stream habitat data from four long-term monitoring programs (AIM, AREMP, NRSA, PIBO MP) were obtained, pre-processed, transformed, and combined using R code following the Stream Habitat Metrics Integration (SHMI) Data Exchange Standard (Scully et al., 2023b). The dataset includes 26 stream habitat metrics collected between 2000 and 2022 across the United States at ~12,000 locations from ~19,000 data collection events for a total of ~200,000 measurements. Measurements include reach characteristics (sampled reach length, channel gradient, sinuosity), channel dimensions (bankfull width and height, average bankfull width to depth ratio, mean thalweg depth, average wetted width), channel substrate particle sizes (percent fines, percent bedrock, fine sediment percentiles), pools (residual pool depth, pool tail fines), bank characterizations (angle), and water quality/chemistry (specific conductance, pH, specific conductance, turbidity, total nitrogen, total phosphorous). The dataset consists of 4 csv files: 'RecordLevel.csv', 'Location.csv', 'Event.csv', and 'MeasurementOrFact.csv'. The 4 csv data tables may be linked in a database structure using the 'entity relationship diagram.jpg' or by linking the following: Join RecordLevel primary key 'datasetID' to Location foreign key 'datasetID'. Join Location primary key 'locationID to Event foreign key 'locationID'. Join Event primary key 'eventID' to MeasurementOrFact foreign key 'eventID'. An analysis-ready file ('AnalysisStreamHabitatMonitoringMetricDataset.csv') is also published for user convenience.

Geographic patterns and time trends of water-quality, modeled streamflow, and ecological data were compared along the Canadian River and selected tributaries in northeastern New Mexico to Lake Eufaula in Oklahoma to determine effects of climate change on water quality, streamflows, fish populations and ecological flows in this watershed from 1939 to 2013. Project participants included staff from the Oklahoma Cooperative Fish and Wildlife Research Unit, Vieux and Associates, USGS New Jersey Water Science Center and the USGS Oklahoma Water Science Center. Principal project funding was by the South Central Climate Science Center, with in-kind matching from the project participant organizations.

Geographic patterns and time trends of water-quality, modeled streamflow, and ecological data were compared along the Canadian River and selected tributaries in northeastern New Mexico to Lake Eufaula in Oklahoma to determine effects of climate change on water quality, streamflows, fish populations and ecological flows in this watershed from 1939 to 2013. Project participants included staff from the Oklahoma Cooperative Fish and Wildlife Research Unit, Vieux and Associates, USGS New Jersey Water Science Center and the USGS Oklahoma Water Science Center. Principal project funding was by the South Central Climate Science Center, with in-kind matching from the project participant organizations.