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 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.

Data were obtained in order to evaluate differences among watersheds that vary in stormwater management practice arrangement by assessing differences in baseflow nutrient fluxes and stormflow export of suspended sediments and total particulate phosphorus. The study area is located the Piedmont in Clarksburg, Montgomery, County Maryland. Watersheds included a forested watershed (For-MD), centralized stormwater management watershed (Cent-MD), and distributed stormwater management watershed (Dist-MD).

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.

The datasets herein are the observed instantaneous values of streamflow for the titled U.S. Geological Survey streamgage with precipitation metadata added. Days where streamflow is directly affected by precipitation and the day afterwards is identified with a "B". Two days after a precipitation event is identified with a "C". If the streamflow was affected by snow or ice, the data is identified with a "D". Any data that is not precipitation influenced is identified with an "A". This metadata was applied on the basis of numerous rain gages in the vicinity of the streamgage and radar images obtained from the National Oceanic and Atmospheric Administration website https://www.ncdc.noaa.gov/data-access/radar-data/radar-map-tool (accessed various times throughout the project).

The datasets herein are the observed instantaneous values of streamflow for the titled U.S. Geological Survey streamgage with precipitation metadata added. Days where streamflow is directly affected by precipitation and the day afterwards is identified with a "B". Two days after a precipitation event is identified with a "C". If the streamflow was affected by snow or ice, the data is identified with a "D". Any data that is not precipitation influenced is identified with an "A". This metadata was applied on the basis of numerous rain gages in the vicinity of the streamgage and radar images obtained from the National Oceanic and Atmospheric Administration website https://www.ncdc.noaa.gov/data-access/radar-data/radar-map-tool (accessed various times throughout the project).

Peak-flow frequency analysis is crucial in various water-resources management applications, including floodplain management and critical structure design. Federal guidelines for peak-flow frequency analyses, provided in Bulletin 17C, assume that the statistical properties of the hydrologic processes driving variability in peak flows do not change over time and so the frequency distribution of annual peak flows is stationary. Better understanding of long-term climatic persistence and further consideration of potential climate and land-use changes have caused the assumption of stationarity to be reexamined. This data release contains input data and results of a study investigating hydroclimatic trends in peak streamflow (peak flow) in the Central United States, including nine states (Iowa, Illinois, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin). Peak flow records from unregulated U.S. Geological Survey (USGS) streamgages were used to evaluate changes over 30-, 50-, 75-, and 100-year trend periods, all ending in water year 2020. This data release contains station lists of the streamgages used in each of the nine states, the peak streamflow input data and peak streamflow analysis results, and the climate input data and climate analysis results. See "Station_Lists.zip" on the landing page for station lists (in text file format) for each state included in the study.

Peak-flow frequency analysis is crucial in various water-resources management applications, including floodplain management and critical structure design. Federal guidelines for peak-flow frequency analyses, provided in Bulletin 17C, assume that the statistical properties of the hydrologic processes driving variability in peak flows do not change over time and so the frequency distribution of annual peak flows is stationary. Better understanding of long-term climatic persistence and further consideration of potential climate and land-use changes have caused the assumption of stationarity to be reexamined. This data release contains input data and results of a study investigating hydroclimatic trends in peak streamflow (peak flow) in the Central United States, including nine states (Iowa, Illinois, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin). Peak flow records from unregulated U.S. Geological Survey (USGS) streamgages were used to evaluate changes over 30-, 50-, 75-, and 100-year trend periods, all ending in water year 2020. This data release contains station lists of the streamgages used in each of the nine states, the peak streamflow input data and peak streamflow analysis results, and the climate input data and climate analysis results. See "Station_Lists.zip" on the landing page for station lists (in text file format) for each state included in the study.

This dataset represents the wetness index within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the Composite Topographic Index (See Supplementary Info for Glossary of Terms). The Composite Topographic Index (CTI) is based on contributing area, slope, and overland flow and has been developed internally at the EPA for the EnviroAtls (http://edg.epa.gov/data/Public/ORD/EnviroAtlas/National/). As defined for use in EnviroAtlas datasets and as used here, wet areas are typically created by runoff from natural land cover when rain falls on saturated soil. Surface and rill (or small channel) runoff carries excess water to lowland depressions or wet areas. Runoff collects in wet areas until they fill and overflow downstream. In this way, stream networks can be extended into new areas that would not be hydrologically connected during drier times. Wet area expansion and watershed hydrological connectivity differ between humid temperate and semi-arid and arid climates (where drought and soil crusts limit infiltration and produce flashier runoff) (from https://enviroatlas.epa.gov/enviroatlas/datafactsheets/pdf/ESN/PercentForestonWetAreas.pdf). The Mean Composite Topographic Index (CTI)[Wetness Index] were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.