This dataset describes in stream turbidity for watersheds included in a paired watershed study including a forested reference watershed and three urban watersheds with centralized or decentralized stormwater management in Clarksburg, Maryland USA. Turbidity was monitoring from June 2010 to December 2010, March 2011 to December 2011 and June 2012 to October 2012 in For-MD and Cent-MD, and only the 2011-2012 time period in Dist-MD. Mean turbidity in NTU was recorded at 5-minute intervals during the 2010-2012 monitoring seasons with a Forest Technology Systems, Digital Turbidity Sensor DTS-12. Mean turbidity was computed from 100 instantaneous recordings during the time interval. Sensor accuracy is ±2% of reading (0-399 NTU) and ±4% of reading (400-1,600 NTU), with a turbidity range of 0 to 1,600 NTU.

This dataset describes in stream turbidity for watersheds included in a paired watershed study including a forested reference watershed and three urban watersheds with centralized or decentralized stormwater management in Clarksburg, Maryland USA. Turbidity was monitoring from June 2010 to December 2010, March 2011 to December 2011 and June 2012 to October 2012 in For-MD and Cent-MD, and only the 2011-2012 time period in Dist-MD. Mean turbidity in NTU was recorded at 5-minute intervals during the 2010-2012 monitoring seasons with a Forest Technology Systems, Digital Turbidity Sensor DTS-12. Mean turbidity was computed from 100 instantaneous recordings during the time interval. Sensor accuracy is ±2% of reading (0-399 NTU) and ±4% of reading (400-1,600 NTU), with a turbidity range of 0 to 1,600 NTU.

This dataset describes storm event loads (sediment and total particulate phosphorus), hydrologic metrics, and precipitation characteristics for storm events occurring between 2010-2012. Loads were estimated for four watersheds included in a paired watershed study; a forested reference watershed and three urban watersheds with centralized or decentralized stormwater management in Clarksburg, Maryland USA or Fairfax County, Virginia USA. Storm event loads were estimated from surrogate relations between turbidity and the water quality parameter of interest. Hydrologic metrics were determined for each storm event using the USGS stream gage instantaneous discharge record for each watershed. Precipitation event characteristics were determined from rain gage data obtained from Montgomery County Department of Environmental Protection.

This dataset describes streamflow and precipitation event statistics for four watersheds located in Clarksburg, Maryland, USA. Streamflow and precipitation events were identified from fourteen years of sub-daily (5- and 15-minute) monitoring data spanning October 1, 2004 through September 30, 2018. A 6-hour inter-event window was used to define discrete streamflow and precipitation events. The following streamflow metrics were calculated for each event area normalized peak streamflow, runoff yield, runoff ratio, streamflow duration, time to peak, and rise rate. Precipitation event metrics include total precipitation depth and precipitation event duration.

This dataset describes streamflow and precipitation event statistics for four watersheds located in Clarksburg, Maryland, USA. Streamflow and precipitation events were identified from fourteen years of sub-daily (5- and 15-minute) monitoring data spanning October 1, 2004 through September 30, 2018. A 6-hour inter-event window was used to define discrete streamflow and precipitation events. The following streamflow metrics were calculated for each event area normalized peak streamflow, runoff yield, runoff ratio, streamflow duration, time to peak, and rise rate. Precipitation event metrics include total precipitation depth and precipitation event duration.

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.

The U.S. Geological Survey and the University of Wisconsin – Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at Mainstem Plum Creek and West Plum Creek in northeastern Wisconsin. Monitoring data from 2010–2020 at Mainstem Plum and 2013–2020 at West Plum were used to detect changes in hydrologic and water-quality responses during runoff events. Runoff events were defined by hydrographers and used to compute event loads and event flow-weighted mean concentrations of total phosphorus and total suspended solids – all of which are included in this data release. Additionally, changes in these parameters were assessed between two time periods (“initial” and “post-CMP implementation”) using the R scripts included in this model archive. Because event discharges, loads, and concentrations are influenced by factors such as weather and the conditions preceding events, random-forest and regression models were developed to control for these factors and to elucidate water-quality changes more directly associated with CMP implementation. Residuals from random-forest models were used to detect changes between the two time periods via Wilcoxon signed-rank tests, and multiple linear regression models were used to determine percent change in responses via time-period dummy variable coefficients. Results indicate statistically insignificant changes in most responses during runoff events. This parent page serves as a landing page for two child items associated with Water Quality and Estimated Changes in the Plum Creek Watershed 2010-2020. Data release child page: contains the event times, event loads, and flow-weighted mean concentrations used for modeling purposes. Model archive child page: contains the inputs, scripts, and outputs used and produced to evaluate changes in water quality associated with conservation management practice implementation.

Nitrogen (N), phosphorus (P), and total suspended solids (TSS) loads, in Hampton Roads, Virginia stormwater conveyance systems have been calculated using monitoring data from 12 intensively monitored watersheds for the period from water year (October - September) 2016 through 2020. Nutrient and TSS loads were computed using a surrogate (multiple-linear regression) approach with lab analyzed N, P, and TSS samples as the response variable and basic water-quality parameters (e.g. turbidity, specific conductance, water temperature), streamflow, a baseflow separation Boolean term, and time and seasonal terms as predictor (surrogate) variables. Load results represent the mass of N, P, and TSS exported from each of the Hampton Roads watersheds. Coefficients are provided for each unique station-constituent model. Loads are provided for the following constituents: TSS, total N, total P, nitrate plus nitrite, total Kjeldahl N, total organic N, and orthophosphate. Streamflow is an important driver of water-quality conditions; therefore, datasets are provided describing stream flashiness as well as a suite of event-based metrics, which include stormflow volume, peak flow, lag to peak, storm event duration, time to peak, runoff ratio, and rise rate. Streamflows are driven by precipitation patterns; therefore, rainfall data collected at 10 Hampton Roads Sanitation District (HRSD) gages are also provided. These data were used to evaluate rainfall patterns during the study period as well as compute the lag to peak and runoff ratio metrics for each storm event. This data release contains six comma-delimited (.csv) files and one zip file with corresponding data dictionary files (.csv). HRSD.Rain.csv contains 15-minute interval rainfall data collected at 10 precipitation gages operated by the HRSD. HR_Loads_CalibrationData.csv contains all data used in the calibration of surrogate regression models for the computation of N, P, and TSS loads. HR_Loads_SurrogateData.csv contains 5-minute interval measurements of streamflow, water-quality parameters, and a Boolean baseflow separation identifier “BASE.” These data were used to compute 5-minute interval measurements of N, P, and TSS loads using surrogate regression models, which were calibrated with the data provided in HR_Loads_CalibrationData.csv. Model_Coefficients.csv contains the coefficients for each station-constituent specific model. Storm_Events.csv contains an identification number and mean timestamp for each storm extracted using the methods described in Porter, 2022, and seven metrics describing the hydrograph. RBI.csv contains an average Richards-Baker Flashiness (RBI) index score for 41 streamgaging stations operated by the United States Geological Survey: Virginia and West Virginia Water Science Center. UV_Loads.zip contains 12 identically formatted data tables with 5-minute unit value predictions of N, P, and TSS loads from the beginning of water year (October 1 - September 30) 2016 through 2020 at 12 stormwater monitoring stations in the Hampton Roads region of Virginia. For each file entity and attributes are described in data.dictionary.csv that shares the same name. The data dictionary file "UV_Loads_Data.dictionary..csv" applies to all .csv files in the UV_Loads.zip. A README text file is also attached, which contains descriptions of each data table and supplementary information.

This dataset describes baseflow and stormflow concentration data for the constituents of nitrogen and phosphorus and suspended sediments for watersheds included in a paired watershed study including a forested reference watershed and three urban watersheds with centralized or decentralized stormwater management in Clarksburg, Maryland USA. Surface water samples were collected between the years 2004-2016. These data are interpreted in a journal article published in the Journal of Environmental Management.