Freshwater salinization is an emerging water quality issue for non-tidal streams and rivers in the Chesapeake Bay watershed (CBW), USA region. A model was developed to predict specific conductance (SC; a proxy for salinity) conditions across the CBW and departures from background SC. Discrete observations of SC from 1999-2016 were acquired from a published SC data inventory and explanatory variables describing sources of SC were compiled from several sources. Random forests modeling was conducted to predict SC at four time periods (1999-2001, 2004-2006, 2009-2011, and 2014-2016) at all non-tidal National Hydrography Dataset Plus Version 2.1 (NHDPlusV2.1; 1:100K scale) stream reaches. These predictions were then compared to a national background SC dataset to determine relative departures from background SC for each NHDPlusV2.1 reach ID. This data release contains model input data, model output data, and predictions of SC. This data release contains the following three files: 1."Model_input.csv": Contains SC observations, explanatory variables, and additional columns relevant to the model application. 2. "Model_output.csv": Contains predicted SC values for the reaches contained in either the testing or training datasets, as well as the feature contributions for each explanatory variable. 3. "Model_predictions.csv": Contains predicted SC, predicted/expected (or P/E) ratios, and departure categories for all non-tidal reach IDs in the CBW for the four time periods.

Freshwater salinization is an emerging issue for freshwater environments in the Chesapeake Bay, USA region. Salinization is often described by measurements of specific conductance (SC). This data release contains specific conductance observations collected by multiple regional agencies for streams within the Chesapeake Bay Watershed. This inventory compiles and harmonizes data from the Water Quality Portal (WQP), which is a data repository developed by the National Water Quality Monitoring Council and supported by the U.S. Environmental Protection Agency and U.S. Geological Survey, and the U.S. Geological Survey National Water Information System (NWIS). Both discrete measures of SC, which are single measures taken on a particular time and day, and continuous measures of SC, which are repeated measures of SC taken at regular, short intervals, such as 15-minute or hourly intervals, were compiled for this data release. The discrete data were also processed to screen out non-relevant observations and harmonize units. The WQP uses "MonitoringLocationIdentifier" to identify each unique site and monitoring activities, and this term is used throughout the data release to differentiate among unique sites and monitoring activities as well. The data release includes four items: 1. ["Site_inventory_for_specific_conductance_measures.csv"]: This is a site inventory of all locations where SC data had been collected and compiled for the data release. This file includes information on the monitoring location (coordinates, state, and county), the organization responsible for the data collection, the type of data available (discrete, continuous, or both) and its unique monitoring location and activity. 2. ["Discrete_specific_conductance_results.txt"]: This file contains all discrete SC observations. Identifying information (coordinates, monitoring location name and identifier), along with the observation value, units, and multiple flagging columns which denoted whether any changes were made to the observation or units during the processing steps. Full details are included in the "readme_file_for_Ches_Bay_specific_conductance_inventory.pdf" file. 3. ["Continuous_specific_conductance_results.zip"]: This zipped folder contains 89 .csv files for all the continuous USGS SC data available in the Chesapeake Bay watershed. Each file name includes each unique MonitoringLocationIdentifier. 4. ["readme_file_for_Ches_Bay_specific_conductance_inventory.pdf"]: This document describes all the processing and harmonization steps to generate the site inventory and discrete SC dataset, and for downloading the high frequency SC datasets.

The ecological implications of rising stream temperatures are a concern to resource managers throughout the Chesapeake Bay watershed. This dataset provides analysis results for the recent status and trends of water temperature for select streams within the Chesapeake Bay watershed. An existing data compilation effort (https://doi.org/10.5066/P92SHG66) was used to gather continuous and discrete temperature data for the status and trend analysis. The status of stream temperature was computed as the change in 2022 in degree Celsius above or below the mean temperature for the reference period (trend interval). Site level trends were developed using generalized additive modeling (GAMs) in the R package “mgcv” for 31 sites with an average of 346 daily value stream temperature measurements per site collected from contemporary periods (2013-2022, 2014-2022, 2013-2021, 2013-2020). Discrete data were evaluated with linear mixed-models (LMMs) to produce a population-level trend for stream temperature. Detailed data preparation information, analytical methods and results are presented and discussed in the associated Scientific Investigative Report (https://doi.org/10.3133/sir20255072).

The Chesapeake Bay is greatly affected by freshwater flows from streams and rivers draining its watershed. Variations in the amount and timing of streamflow can change water temperature, salinity, suspended sediment and contaminants levels, and affect the amount of nutrients in freshwater streams. Additionally freshwater flows affect the amount of in-stream habitat available to freshwater flora and fauna. Metrics characterizing streamflow extremes such as magnitude, frequency and duration of high and low flows are helpful indicators of long-term patterns, shifts in streamflow regimes, and the interpretation of their effects on stream biota. Generalized additive models were used to analyze long term changes in annual metrics of streamflow extrema from water year 1985 through water year 2022. Detailed data preparation information, analytical methods and results are presented and discussed in the associated Scientific Investigative Report (https://doi.org/10.3133/sir20255072).

This is a Data Release containing conductivity and water temperature data for selected springs in the headwaters of the Potomac River basin. This work is supported by USGS Chesapeake Bay studies.

This dataset provides analysis results for status values representing the period of 2015 through 2017 for six indicators of river and stream condition in the Chesapeake Bay watershed. Status (condition at a point in time) was calculated yearly for metrics of the following indicators: stream nutrients and suspended sediment, salinity, temperature, hydromorphology, streamflow and biological assemblages. These yearly status values were then averaged to create mean status values for each indicator and metric. The mean status values were then used to score indicator sites as high quality, low quality, or intermediate quality. Additionally, the relationship between mean status values and three land use/land cover types (developed, agriculture and urban) were analyzed via simple linear regression. Detailed data preparation information, analytical methods and results are presented and discussed in the associated Scientific Investigative Report (https://doi.org/10.3133/sir20255072).

This U.S. Geological Survey (USGS) data release contains estimated daily streamflow and base flow for HUC12 in the nontidal areas of the Chesapeake Bay watershed, monthly average streamflow and base flow, flow statistics, MATLAB scripts, and a document that describes how to create similar datasets in other watersheds. Daily streamflow was estimated for all the nontidal parts of the Chesapeake Bay watershed with the program "Unit Flows in Networks of Channels" (UFINCH; Holtschlag, 2016), together with the observations of measured streamflow at gages at the downstream ends of major rivers. The estimated streamflow was aggregated at the HUC12 level and reformatted as an Optimal Hydrograph Separation (OHS) input file using MATLAB scripts. Base flow was calculated at each HUC12 outlet using the base flow index (BFI) hydrograph separation methods developed by Wahl and Wahl (Wahl and Wahl, 1988; Wahl and Wahl, 1995) and by Eckhardt (Eckhardt, 2005) with the parameter estimation method developed by Collischonn and Fan (Collischonn and Fan, 2013) which are incorporated into the OHS program (Raffensperger and others, 2017). This data release supports the following publication: • Buffington, P.C., and Capel, P.D., 2020, Estimating streamflow and base flow within the nontidal Chesapeake Bay riverine system: U.S. Geological Survey Scientific Investigations Report 2020-5055, 26 p., https://doi.org/10.3133/sir20205055. References cited: • Collischonn, W. and Fan, F.M., 2013, Defining parameters for Eckhardt's digital baseflow filter: Hydrological Processes, v. 27, no. 18, p. 2614-2622, https://doi.org/10.1002/hyp.9391. • Eckhardt, K., 2005, How to construct recursive digital filters for baseflow separation: Hydrological Processes, v. 19, no. 2, p. 507-515, https://doi.org/10.1002/hyp.5675. • Holtschlag, D.J., 2016, UFINCH-A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages: U.S. Geological Survey Scientific Investigations Report 2016-5074, 17 p., https://doi.org/10.3133/sir20165074. • Raffensperger, J.P., Baker, A.C., Blomquist, J.D., and Hopple, J.A., 2017, Optimal hydrograph separation using a recursive digital filter constrained by chemical mass balance, with application to selected Chesapeake Bay watersheds: U.S. Geological Survey Scientific Investigations Report 2017-5034, 51 p., https://doi.org/10.3133/sir20175034. • Wahl, K.L., and Wahl, T.L., 1988, Effects of regional ground water declines on streamflows in the Oklahoma Panhandle, in Symposium on Water-Use Data for Water Resources Management, Tucson, Arizona, American Water Resources Association, p. 239-249. • Wahl, K.L., and Wahl, T.L., 1995, Determining the flow of Comal Springs at New Braunfels, Texas, Texas Water '95: San Antonio, Texas, American Society of Civil Engineers, p. 77-86, http://www.usbr.gov/tsc/techreferences/hydraulics_lab/pubs/PAP/PAP-0708.pdf.

This U.S. Geological Survey (USGS) data release contains estimated daily streamflow and base flow for HUC12 in the nontidal areas of the Chesapeake Bay watershed, monthly average streamflow and base flow, flow statistics, MATLAB scripts, and a document that describes how to create similar datasets in other watersheds. Daily streamflow was estimated for all the nontidal parts of the Chesapeake Bay watershed with the program "Unit Flows in Networks of Channels" (UFINCH; Holtschlag, 2016), together with the observations of measured streamflow at gages at the downstream ends of major rivers. The estimated streamflow was aggregated at the HUC12 level and reformatted as an Optimal Hydrograph Separation (OHS) input file using MATLAB scripts. Base flow was calculated at each HUC12 outlet using the base flow index (BFI) hydrograph separation methods developed by Wahl and Wahl (Wahl and Wahl, 1988; Wahl and Wahl, 1995) and by Eckhardt (Eckhardt, 2005) with the parameter estimation method developed by Collischonn and Fan (Collischonn and Fan, 2013) which are incorporated into the OHS program (Raffensperger and others, 2017). This data release supports the following publication: • Buffington, P.C., and Capel, P.D., 2020, Estimating streamflow and base flow within the nontidal Chesapeake Bay riverine system: U.S. Geological Survey Scientific Investigations Report 2020-5055, 26 p., https://doi.org/10.3133/sir20205055. References cited: • Collischonn, W. and Fan, F.M., 2013, Defining parameters for Eckhardt's digital baseflow filter: Hydrological Processes, v. 27, no. 18, p. 2614-2622, https://doi.org/10.1002/hyp.9391. • Eckhardt, K., 2005, How to construct recursive digital filters for baseflow separation: Hydrological Processes, v. 19, no. 2, p. 507-515, https://doi.org/10.1002/hyp.5675. • Holtschlag, D.J., 2016, UFINCH-A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages: U.S. Geological Survey Scientific Investigations Report 2016-5074, 17 p., https://doi.org/10.3133/sir20165074. • Raffensperger, J.P., Baker, A.C., Blomquist, J.D., and Hopple, J.A., 2017, Optimal hydrograph separation using a recursive digital filter constrained by chemical mass balance, with application to selected Chesapeake Bay watersheds: U.S. Geological Survey Scientific Investigations Report 2017-5034, 51 p., https://doi.org/10.3133/sir20175034. • Wahl, K.L., and Wahl, T.L., 1988, Effects of regional ground water declines on streamflows in the Oklahoma Panhandle, in Symposium on Water-Use Data for Water Resources Management, Tucson, Arizona, American Water Resources Association, p. 239-249. • Wahl, K.L., and Wahl, T.L., 1995, Determining the flow of Comal Springs at New Braunfels, Texas, Texas Water '95: San Antonio, Texas, American Society of Civil Engineers, p. 77-86, http://www.usbr.gov/tsc/techreferences/hydraulics_lab/pubs/PAP/PAP-0708.pdf.

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.

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.