As part of a larger study examining stream conditions and the effect of Best Management Practices in the Chesapeake Bay watershed, thirty small streams on the Delmarva Peninsula were instrumented and monitored for gage height (water level), water temperature, and air temperature using Onset HOBO sensors from March to September 2022. In addition, two discrete discharge measurements were made at baseflow at each site. This data release contains four .csv files with time-series for gage height, water temperature, and air temperature for all thirty monitoring locations and a table of discrete discharge measurements and associated field measurement metadata: Delmarva_2022_Continuous_Air_Temperature.csv Delmarva_2022_Continuous_Gage_Height.csv Delmarva_2022_Continuous_Water_Temperature.csv Delmarva_2022_Discharge_Measurements.csv

As part of a larger study examining stream conditions and the effect of best management practices in the Chesapeake Bay watershed, thirty small streams across the Pennsylvania and Maryland piedmont physiographic province were instrumented and monitored for gage height (water level), water temperature, and air temperature using Onset HOBO sensors from March to September 2023. In addition, discrete discharge measurements were made at baseflow at each site. This data release contains four .csv files with time-series for gage height, water temperature, and air temperature for all thirty monitoring locations and a table of discrete discharge measurements and associated field measurement metadata. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

As part of a larger study examining stream conditions and the effect of best management practices in the Chesapeake Bay watershed, thirty small streams across the Pennsylvania and Maryland piedmont physiographic province were instrumented and monitored for gage height (water level), water temperature, and air temperature using Onset HOBO sensors from March to September 2023. In addition, discrete discharge measurements were made at baseflow at each site. This data release contains four .csv files with time-series for gage height, water temperature, and air temperature for all thirty monitoring locations and a table of discrete discharge measurements and associated field measurement metadata. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

The datasets were generated for the Upper Choptank River Watershed (UCRW) in Maryland and Delaware. Depressions and the total stream network were derived from a lidar DEM collected during April to June 2003 (vertical accuracy root mean square error (RMSE) = 14.3 cm) and March to April 2006 (vertical accuracy RMSE = 18.5 cm) for Maryland (1 m resolution) and April 2007 (vertical accuracy RMSE = 18.5 cm) for Delaware (3 m resolution)). Depressions were identified using the Stochastic Depression Analysis Tool in Whitebox GAT. A random sample from the potential error values was iteratively added to the original DEM prior to depressions being filled and identified. Cells were considered part of a depression if identified as such in 80% of the 20 iterations. An edge preserving smoothing filter with a 3-pixel by 3-pixel window was applied to reduce noise, or random error, within the final depression raster. Depressions smaller than 50 m2 were removed. To map the stream network, flow accumulation was calculated on the filled lidar DEM using the FD8 flow accumulation algorithm. Flow accumulation was thresholded at 50,000 m2, a decision point guided by field-based points. Three Landsat images were used to create a surface-water extent map from Landsat including Landsat-8 images collected on April 4, 2015 (p14r33) and April 11, 2015 (p15r33) and a Landsat-7 ETM+ image collected on April 12, 2015 (p14r33). Surface-water was identified using the Matched Filtering algorithm. The output values were linearly stretched and a Frost filter with a 3-pixel by 3-pixel window was applied. Landsat pixels with a per-pixel fraction of >0.4 were classified as inundated.

The datasets were generated for the Upper Choptank River Watershed (UCRW) in Maryland and Delaware. Depressions and the total stream network were derived from a lidar DEM collected during April to June 2003 (vertical accuracy root mean square error (RMSE) = 14.3 cm) and March to April 2006 (vertical accuracy RMSE = 18.5 cm) for Maryland (1 m resolution) and April 2007 (vertical accuracy RMSE = 18.5 cm) for Delaware (3 m resolution)). Depressions were identified using the Stochastic Depression Analysis Tool in Whitebox GAT. A random sample from the potential error values was iteratively added to the original DEM prior to depressions being filled and identified. Cells were considered part of a depression if identified as such in 80% of the 20 iterations. An edge preserving smoothing filter with a 3-pixel by 3-pixel window was applied to reduce noise, or random error, within the final depression raster. Depressions smaller than 50 m2 were removed. To map the stream network, flow accumulation was calculated on the filled lidar DEM using the FD8 flow accumulation algorithm. Flow accumulation was thresholded at 50,000 m2, a decision point guided by field-based points. Three Landsat images were used to create a surface-water extent map from Landsat including Landsat-8 images collected on April 4, 2015 (p14r33) and April 11, 2015 (p15r33) and a Landsat-7 ETM+ image collected on April 12, 2015 (p14r33). Surface-water was identified using the Matched Filtering algorithm. The output values were linearly stretched and a Frost filter with a 3-pixel by 3-pixel window was applied. Landsat pixels with a per-pixel fraction of >0.4 were classified as inundated.

This data release contains summary metrics describing stream stage, stream water temperature, and short-term climate conditions (daily precipitation and air temperature) for 30 streams spanning gradients of forest and row-crop land uses and agricultural best management practice implementation in the Delmarva Peninsula of Delaware, Maryland, and Virginia, USA. This setting is the second of four settings, or "typologies," that will be assessed for the U.S. Geological Survey (USGS) Chesapeake Stream Team project. High-frequency stage, water temperature, and air temperature (approximately 15-minute data) were measured by the USGS from March 2022 to September 2022 and are available at McFarland and others (2024). Additional daily air temperature and precipitation data were acquired from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate data website (PRISM Climate Group, 2014). Air temperature and water temperature data were used to compute stream water temperature metrics describing stream temperature conditions in each stream during the monitoring period. Stream stage and precipitation data were used to compute stream stage metrics describing stage conditions (a surrogate for streamflow) for each stream during the monitoring period. Precipitation and air temperature data from PRISM were also used to compute air temperature metrics and precipitation metrics describing climate conditions during the monitoring period. The metrics include: Air temperature metrics - Mean daily minimum, mean, and maximum air temperature Precipitation metrics - Total precipitation depth - Maximum daily precipitation depth - Average precipitation depth per days with precipitation - Frequency of precipitation days Stream stage metrics - Number of runoff events - Frequency of runoff events - Standard deviation in unit-value stage Stream water temperature metrics - Coefficient of variation of mean and maximum daily water temperatures - Number of days with temperatures of 20 or 25 degrees Celsius or greater - Duration of time above 20 or 25 degrees Celsius or greater - Maximum of seven-day moving average of daily maximum temperature - Mean of daily minimum, maximum, and daily water temperature range - A thermal sensitivity metric, which is the slope estimate from linear regression model of mean daily water temperature versus mean daily air temperature This data release contains six files: 1. "Readme.pdf": This is an expanded narrative describing the methods by which the input data were compiled and screened, and metrics were computed. 2. "typology_2_temperature_stage_climate_metric_data_dictionary.csv": This file contains descriptions of each metric and the time periods for which they were computed in the “typology_2_temperature_stage_climate_summary_metrics.csv" file. 3. "typology_2_temperature_stage_climate_summary_metrics.csv": This file contains stream temperature metrics, stage metrics, and climate summary metrics for each of the 30 stream sites for different time periods within the overall monitoring period. 4. "typology_2_input_data_high_frequency_temperature_and_stage.zip": This zipped folder contains 30 .csv files, which contain the high-frequency stage, water temperature, and air temperature data collected at each of the 30 stream sites. The file names include the SiteID, a unique four-letter site identification listed in the "typology_2_temperature_stage_climate_summary_metrics.csv" file. 5. "typology_2_input_data_daily_climate.csv": This file contains daily climate estimates (precipitation depth, daily minimum, mean, and maximum air temperatures) from PRISM paired to each of the 30 sites. 6. "typology_2_runoff_events.csv": This file contains the stage rise and precipitation data used for some of the stage metric computations.

This data release contains summary metrics describing stream stage, stream water temperature, and short-term climate conditions (daily precipitation and air temperature) for 30 streams spanning gradients of forest and row-crop land uses and agricultural best management practice implementation in the Delmarva Peninsula of Delaware, Maryland, and Virginia, USA. This setting is the second of four settings, or "typologies," that will be assessed for the U.S. Geological Survey (USGS) Chesapeake Stream Team project. High-frequency stage, water temperature, and air temperature (approximately 15-minute data) were measured by the USGS from March 2022 to September 2022 and are available at McFarland and others (2024). Additional daily air temperature and precipitation data were acquired from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate data website (PRISM Climate Group, 2014). Air temperature and water temperature data were used to compute stream water temperature metrics describing stream temperature conditions in each stream during the monitoring period. Stream stage and precipitation data were used to compute stream stage metrics describing stage conditions (a surrogate for streamflow) for each stream during the monitoring period. Precipitation and air temperature data from PRISM were also used to compute air temperature metrics and precipitation metrics describing climate conditions during the monitoring period. The metrics include: Air temperature metrics - Mean daily minimum, mean, and maximum air temperature Precipitation metrics - Total precipitation depth - Maximum daily precipitation depth - Average precipitation depth per days with precipitation - Frequency of precipitation days Stream stage metrics - Number of runoff events - Frequency of runoff events - Standard deviation in unit-value stage Stream water temperature metrics - Coefficient of variation of mean and maximum daily water temperatures - Number of days with temperatures of 20 or 25 degrees Celsius or greater - Duration of time above 20 or 25 degrees Celsius or greater - Maximum of seven-day moving average of daily maximum temperature - Mean of daily minimum, maximum, and daily water temperature range - A thermal sensitivity metric, which is the slope estimate from linear regression model of mean daily water temperature versus mean daily air temperature This data release contains six files: 1. "Readme.pdf": This is an expanded narrative describing the methods by which the input data were compiled and screened, and metrics were computed. 2. "typology_2_temperature_stage_climate_metric_data_dictionary.csv": This file contains descriptions of each metric and the time periods for which they were computed in the "typology_2_temperature_stage_climate_summary_metrics.csv" file. 3. "typology_2_temperature_stage_climate_summary_metrics.csv": This file contains stream temperature metrics, stage metrics, and climate summary metrics for each of the 30 stream sites for different time periods within the overall monitoring period. 4. "typology_2_input_data_high_frequency_temperature_and_stage.zip": This zipped folder contains 30 .csv files, which contain the high-frequency stage, water temperature, and air temperature data collected at each of the 30 stream sites. The file names include the SiteID, a unique four-letter site identification listed in the "typology_2_temperature_stage_climate_summary_metrics.csv" file. 5. "typology_2_input_data_daily_climate.csv": This file contains daily climate estimates (precipitation depth, daily minimum, mean, and maximum air temperatures) from PRISM paired to each of the 30 sites. 6. "typology_2_runoff_events.csv": This file contains the stage rise and precipitation data used for some of the stage metric computations.

This data release collates stream water temperature observations across the Chesapeake Bay watershed from the USGS National Water Information System (NWIS), Water Quality Portal (WQP) and the USGS Aquarius (AQ) Time-Series database. Data retrieved from NWIS consists of aggregate (minimum, maximum and mean) daily values and continuous data from USGS monitoring stations. Values from the WQP contain discrete data from multiple agencies. The dataset compiled from AQ includes miscellaneous stream temperature observations collected during discharge measurements. This stream temperature data release was completed to support the USGS goal to make scientific data publicly usable, easily discoverable, and widely available. A subset of these data will be used and published in the future to assess the status and trends of key indicators of stream health in the Chesapeake Bay watershed.

This data release collates stream water temperature observations across the Chesapeake Bay watershed from the USGS National Water Information System (NWIS), Water Quality Portal (WQP) and the USGS Aquarius (AQ) Time-Series database. Data retrieved from NWIS consists of aggregate (minimum, maximum and mean) daily values and continuous data from USGS monitoring stations. Values from the WQP contain discrete data from multiple agencies. The dataset compiled from AQ includes miscellaneous stream temperature observations collected during discharge measurements. This stream temperature data release was completed to support the USGS goal to make scientific data publicly usable, easily discoverable, and widely available. A subset of these data will be used and published in the future to assess the status and trends of key indicators of stream health in the Chesapeake Bay watershed.

This data release contains measured streamflow data from U.S. Geological Survey (USGS) streamgages and reported wastewater data from wastewater treatment plants (WWTP) discharge monitoring reports (DMRs) within the Potomac River watershed between October 1, 2021 and September 30, 2024. Mean monthly streamflow data was obtained from 117 USGS streamgages (Table1_Streamgages.csv). Average monthly reported wastewater discharge volumes to surface water were obtained from National Pollutant Discharge Elimination System (NPDES) permits using the United States Environmental Protection Agency’s (USEPA) Environment and Compliance History Online (ECHO) database to obtain DMRs from the Integrated Compliance Information System National Pollutant Discharge Elimination System (ICIS-NPDES). Quality assurance procedures that were used to avoid inclusion of inaccurate data that can be reported on DMRs (Table2_WWTP_DMRs.csv) are documented within the Process Step fields of the metadata. At each streamgage the average monthly accumulated wastewater percentage (ACCWW) was calculated by dividing the total amount of reported wastewater upstream of the streamgage by the measured amount of streamflow (Table3_Streamgage_ACCWW.csv) following similar methods described in Miller and others (2024) and Barber and others (2025). The ACCWW calculations were computed monthly at each streamgage using reported total wastewater discharge, municipal wastewater discharge, and municipal-plus-industrial per- and polyfluoroalkyl substances (PFAS) wastewater discharge which includes municipal wastewater in addition to wastewater from industrial WWTPs that are potential PFAS handling industry sectors defined by the USEPA (2023). The term ‘municipal’ is used here to represent NPDES-permitted facilities with the Standard Industrial Classification code 4952 (‘sewerage systems’) and 'industrial' refers to permitted facilities with Standard Industrial Classification codes other than 4952. Monthly predicted environmental concentrations and constituent loads (i.e. mass fluxes) of eight PFAS and 14 pesticides were estimated at each streamgage following methodology presented by Barber and others (2025) and Miller and others (2024). Monthly PFAS loads were computed by multiplying the discharge volumes from municipal and industrial WWTPs that are potential PFAS handling industry sectors by the median PFAS concentrations measured and reported in Barber and others (2025). Monthly pesticide loads were computed by multiplying the discharge volumes from municipal WWTPs by the median pesticide concentrations reported in Miller and others (2024). Wastewater effluent concentrations from Miller and others (2024) and Barber and others (2025) are provided in Table4_Parameters.csv. Monthly predicted constituent loads from wastewater were summed from WWTPs that discharged to every National Hydrography Dataset Version 2.1 (NHDPlus V2; USEPA, 2012) stream segment Common Identifier (COMID) upstream of each streamgage, not including the COMID where the streamgage was located, to calculate the predicted monthly load at each streamgage (Table5_Streamgage_Parameter_Predictions.csv). Predicted monthly concentrations from wastewater were calculated by dividing the predicted monthly load by measured monthly streamflow at each streamgage (Table5_Streamgage_Parameter_Predictions.csv).