Data Releases
The data collected and the techniques used by USGS scientists should conform to or reference national and international standards and protocols if they exist and when they are relevant and appropriate. For datasets of a given type, and if national or international metadata standards exist, the data are indexed with metadata that facilitates access and integration.
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Imagery, soil temperature and humidity profiles, and meteorological data from December 2020 to April 2021, Grand Falls Dune Field, Arizona
Grand Falls dune field (GFDF) is located on the Navajo Nation, ~70 km NE of Flagstaff, AZ. This active dune field displays a range of morphologies, including barchans, smaller dunes, and ripples, and is bimodal in composition. The felsic component is likely derived from the Little Colorado River, and the mafic component (basaltic grains) is locally sourced from nearby cinder cones [1]. GFDF is an
Environmental DNA data, fish abundance data, and stream habitat data from northwest Montana and northeast Washington and southern British Columbia, Canada
Field estimates of the abundance of two trout species (bull trout and westslope cutthroat trout) in Montana and rainbow trout in Washington and British Columbia were collected in concert with environmental DNA samples (eDNA) to evaluate if eDNA copy numbers correlated with abundance of trout. In addition, stream habitat data including channel units (pools, riffles), substrate, large woody debris,
Point Sampling Data for Eelgrass (Zostera marina) and Seaweed Distribution and Abundance in Bays Adjacent to the Izembek National Wildlife Refuge, Alaska
These data are in nine tables relating to surveys of eelgrass beds in Izembek and Kinzarof Lagoons, Izembek National Wildlife Refuge, Alaska. The tables provide environmental conditions, eelgrass abundance, distribution, and measurements used to estimate overall biomass.
Digital Elevation, Flow Direction, and Flow Accumulation GIS data for West Virginia StreamStats
The U.S. Geological Survey (USGS), prepared geographic information systems (GIS) layers for use in the West Virginia StreamStats application. The Digital Elevation Model and associated data were hydrologically conditioned, which is the process of burning in single line streams at the 1:24,000 scale into a digital elevation model to produce flow direction and flow accumulation grids. This data incl
Terms, Statistics, and Performance Measures for Maximum Likelihood Logistic Regression Models Estimating Hydrological Drought Probabilities in the Northeastern United States (2019)
Tables are presented listing parameters used in logistic regression equations describing drought streamflow probabilities in the Northeastern United States. Streamflow daily data, streamflow monthly mean data, maximum likelihood logistic regression (MLLR) equation explanatory parameters, equation goodness of fit parameters, and Receiver Operating Characteristic (ROC) AUC values identifying the uti
Shenandoah River Accumulated Wastewater Ratio
De facto wastewater reuse from Waste Water Treatment Facilities (WWTF) has the potential to be a significant contributor of Endocrine Disrupting Chemicals. An ArcGIS model of WWTFs, NHDPlus Version 2 stream networks (USGS and EPA 2012), and gage stations across the Shenandoah River watershed was created to calculate accumulated wastewater ratio. Virginia Pollutant Discharge Elimination System (VPD
Basin Characteristics Rasters for West Virginia StreamStats 2021
The U.S. Geological Survey (USGS) has calculated over 25 different basin characteristics as part of preparing the West Virginia StreamStats 2021 application. These datasets are raster representations of various environmental, geological, and land use attributes within the West Virginia StreamStats 2021 study area and will be served in the West Virginia StreamStats 2021 application to describe deli
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 developed
Terms, Statistics, and Performance Measures for Maximum Likelihood Logistic Regression Models Estimating Hydrological Drought Probabilities in the Delaware River Basin (2020)
Tables are presented listing parameters and fit statistics for 25,453 maximum likelihood logistic regression (MLLR) models describing hydrological drought probabilities at 324 gaged locations on rivers and streams in the Delaware River Basin (DRB). Data from previous months are used to estimate chance of hydrological drought during future summer months. Models containing 1 explanatory variable use
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 (Weighted
Fort Belvoir, Virginia, stream-water, streambed-sediment, and soil data collected in 2019
Field parameters and chemical-analysis results of stream water, streambed sediment, and soil data collected during 2019 at Fort Belvoir, Virginia are presented.
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 samples as