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Data

The USGS collects multiple types of data in the Chesapeake Bay watershed. The Data Catalog contains a list and links of all the types of Chesapeake information from USGS. Some of the most relevant data related to streams and rivers is highlighted in the “tracking status and trends indicators of stream health”. Land cover, use, and change data are also highlighted.

Filter Total Items: 151

Chesapeake Bay Nontidal Network 1985 - 2018: Daily High-Flow and Low-Flow Concentration and Load Estimates Chesapeake Bay Nontidal Network 1985 - 2018: Daily High-Flow and Low-Flow Concentration and Load Estimates

Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in rivers across the Chesapeake Bay watershed have been calculated using monitoring data from the Chesapeake Bay Nontidal Network (NTN) stations for the period 1985 through 2018. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS...

Stream cross-section, benthic macroinvertebrate and fish taxa counts and abundance, and water chemistry data for the Clarksburg study area in Montgomery County, Maryland, 1992 - 2020 Stream cross-section, benthic macroinvertebrate and fish taxa counts and abundance, and water chemistry data for the Clarksburg study area in Montgomery County, Maryland, 1992 - 2020

Montgomery County, Maryland Department of Environmental Protection has collected datasets to assess the health of streams since the early 1990s. Datasets include geomorphic stream cross-sectional surveys, fish and benthic macroinvertebrate counts and taxa abundance, and water chemistry data collected at the time of benthic and fish sampling (dissolved oxygen, pH, specific conductance...

Nitrogen sources to and export from the Chesapeake Bay watershed, 1950 to 2050 Nitrogen sources to and export from the Chesapeake Bay watershed, 1950 to 2050

This U.S. Geological Survey data release contains datasets that combine past data with future projections of nitrogen sources and nitrogen export to the Chesapeake Bay watershed for the years 1950-2050. To help understand the effect of human and environmental changes over this time period, data for nitrogen sources from wastewater, agricultural fertilizer and manure, and atmospheric...

Estimates of atmospheric inorganic nitrogen deposition to the Chesapeake Bay watershed, 1950-2050 Estimates of atmospheric inorganic nitrogen deposition to the Chesapeake Bay watershed, 1950-2050

Reactive nitrogen is transported from the atmosphere to the landscape as wet and dry deposition that contributes to annual nitrogen loads to the Chesapeake Bay. Estimates of atmospheric inorganic nitrogen deposition to the Chesapeake Bay watershed during 1950 to 2050 are presented, and are based on field measurements, model simulations, statistical relations, and surrogate constituents...

Tidal Datums, Tidal Range, and Nuisance Flooding Levels for Chesapeake Bay and Delaware Bay Tidal Datums, Tidal Range, and Nuisance Flooding Levels for Chesapeake Bay and Delaware Bay

This U.S. Geological Survey data release provides data on spatial variations in tidal datums, tidal range, and nuisance flooding in Chesapeake Bay and Delaware Bay. Tidal datums are standard elevations that are defined based on average tidal water levels. Datums are used as references to measure local water levels and to delineate regions in coastal environments. Nuisance flooding refers...

Modeled estimates of altered hydrologic metrics for all NHDPlus v21 reaches in the Chesapeake Bay watershed Modeled estimates of altered hydrologic metrics for all NHDPlus v21 reaches in the Chesapeake Bay watershed

Data are modeled estimates of flow status (inclined, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize main components of flow regimes (duration, frequency, magnitude, timing, and rate of change). Model estimates came from random forest models independently built for each HM that predict flow status category using drainage area and previously...

Inputs and Selected Predictions of a Differential Spatially Referenced Regression Model for 20-year Changes in Total Nitrogen in the Chesapeake Bay Watershed Inputs and Selected Predictions of a Differential Spatially Referenced Regression Model for 20-year Changes in Total Nitrogen in the Chesapeake Bay Watershed

The core equations of the SPARROW model (Schwarz and others, 2006) were implemented in differential form using the R programming language (R Core Team, 2017), as the basis of a tool for empirically relating a regional pattern of changes in constituent flux, over a multi-year period, to spatially referenced changes in explanatory variables over the same period. A pilot implementation was...

Nitrogen, phosphorus, and suspended-sediment loads and trends measured at the Chesapeake Bay Nontidal Network stations: Water years 1985-2018 (ver. 2.0, May 2020) Nitrogen, phosphorus, and suspended-sediment loads and trends measured at the Chesapeake Bay Nontidal Network stations: Water years 1985-2018 (ver. 2.0, May 2020)

Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in major rivers across the Chesapeake Bay watershed have been calculated using monitoring data from the Chesapeake Bay Nontidal Network (NTN) stations for the period 1985 through 2018. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS...

Inputs and Selected Outputs Used to Assess Spatial and Temporal Patterns in Streamflow, Water-Chemistry, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007-2018 Inputs and Selected Outputs Used to Assess Spatial and Temporal Patterns in Streamflow, Water-Chemistry, and Aquatic Macroinvertebrates of Selected Streams in Fairfax County, Virginia, 2007-2018

Nitrogen (N), phosphorus (P), and suspended-sediment (SS) loads, in Fairfax County, Virginia streams have been calculated using monitoring data from five intensively monitored watersheds for the period from water year (October - September) 2008-2017. Nutrient and suspended-sediment loads were computed using a surrogate (multiple-linear regression) approach with lab analyzed N, P, and SS...

Lidar-derived digital elevation models in Clarksburg, MD representing the years 2002, 2008, 2013, and 2018 Lidar-derived digital elevation models in Clarksburg, MD representing the years 2002, 2008, 2013, and 2018

This is a collection of 3 ft resolution digital elevation model derived from light detection and ranging (lidar). Lidar was collected in 2002, 2008, 2013, and 2018. Coverage includes a portion of Clarksburg, Maryland, focused around the Clarksburg Special Protection Area (CSPA). Overall extent varies between each dataset, but all digital elevation models are aligned together with...

Ephemeral channel heads and digital elevation models used to extract stream networks in Clarksburg, MD (ver. 2.0, October 2021) Ephemeral channel heads and digital elevation models used to extract stream networks in Clarksburg, MD (ver. 2.0, October 2021)

This dataset describes the location of channel heads survyed in two headwater watersheds in Clarksburg, Montgomery County, Maryland, and the digital elevation models derived from light detection and ranging (lidar) covering the two watersheds. The digital elevation models were used to derive topographic attributes used to delineate drainage networks that were then assessed for accuracy...

Nitrogen, phosphorus, and suspended-sediment loads and trends measured at the Chesapeake Bay River Input Monitoring stations: Water years 1985-2020 Nitrogen, phosphorus, and suspended-sediment loads and trends measured at the Chesapeake Bay River Input Monitoring stations: Water years 1985-2020

Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in major rivers across the Chesapeake Bay watershed have been calculated using monitoring data from the Chesapeake Bay River Input Monitoring (RIM) Network stations for the period 1985 through 2020. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach...
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