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Filter Total Items: 13479

Ellsworth Huntington’s (1914) Giant Sequoia Ages and Tree-Ring Measurements from 458 Stumps in Sequoia National Forest and Mountain Home Grove

     These data include giant sequioa (Sequoiadendron giganteum) age and tree-ring data from measurements on 458 stumps.  The original data were recorded on paper data sheets by Ellsworth Huntington and his assistants in 1911 and 1912; their methods are elaborated in Huntington (1914), and further details can be found in Stephenson and Demetry (1995).  We entered the data from photocopies of the o

Daily reference and potential evapotranspiration, and supporting meteorological data from the Weather Research and Forecasting (WRF) model, solar insolation data from the GOES satellite, and blue-sky albedo data from the MODIS satellite, Southeastern Unit

Potential evapotranspiration (PET), and reference evapotranspiration (ETo) are estimated at an approximately 1-kilometer spatial grid and a daily time-scale from January 1, 2023 to December 31, 2023 for Florida, Alabama, Georgia, South Carolina, and parts of Mississippi, North Carolina, and Tennessee. PET and ETo were computed, using the Priestley-Taylor equation and the FAO Penman-Monteith method

Daily reference and potential evapotranspiration, and supporting meteorological data from weather stations, solar insolation data from the GOES satellite, and blue-sky albedo data from the MODIS satellite, Florida, 2023

Potential evapotranspiration (PET), and reference evapotranspiration (ETo) are estimated at an approximately 2-kilometer (approximately 0.019 degrees longitude and 0.018 degrees latitude) spatial grid and daily time-scale from January 1, 2023 to December 31, 2023 for the entire State of Florida. PET and ETo were computed,using the Priestley-Taylor equation and the FAO Penman-Monteith method, respe

Data and model code used to evaluate a process-guided deep learning approach for in-stream dissolved oxygen prediction

This model archive contains data and code used to assess the use of process-informed multi-task deep learning models for predicting in-stream dissolved oxygen concentrations. Three holdout experiments were run to assess model performance, including a temporal holdout experiment, a spatial holdout experiment with similar sites held out, and a spatial holdout experiment with dissimilar sites held ou

High resolution natural color imagery collected by uncrewed aircraft system (UAS) for mapping channel and vegetation change along a 7-mile reach of the Middle Green River at Horseshoe Bend near Jensen, Utah from 2020-2023

The U.S. Geological Survey collected low-altitude airborne natural color imagery via a fixed-wing uncrewed aircraft system (UAS) for mapping channel and vegetation change along a 7-mile reach of the Middle Green River at Horseshoe Bend near Jensen, UT. Visual imagery was collected in jpg format and Structure-from-Motion (SfM) photogrammetry techniques were applied (Over et al., 2021) using Agisoft

Laser Rangefinder Data for Surficial Mass Movements in the Cascades

A laser rangefinder was used to record surficial mass movements at Cascades volcanoes and an experimental debris flow flume. Mass movements such as large lahars and smaller seasonal debris flows can occur at volcanoes in the Cascades. A combination of seismic, infrasound, tripwires, and webcams can be used to detect and characterize these flows. A laser rangefinder can be placed on the banks of th

Monthly and Annual population and water withdrawal maps of Rhode Island 2014-2021

This data release consists of multi-band 30-meter x 30-meter pixel rasters of estimated population and domestic self-supplied water withdrawals in Rhode Island between July 2014 and June 2021. Population raster data were generated using a national data product of 2010 population spatially distributed across land cover data and U.S. Census Bureau data of population growth estimates to adjust popula

Lower Salinas Valley Hydrologic Models: Future Climate Data

This digital dataset contains the gridded future climate data used for the Lower Salinas Valley Hydrologic Models. The monthly climate data for Lower Salinas Valley Hydrologic Models are based on the Salinas and Carmel River Basins Study (SCRBS) future climate scenarios [Henson and others, 2024). SCRBS considers one baseline climate scenario that represents recent historical climate conditions and

Data for Gull-billed Tern and Black Skimmer Bayesian Network Model - Soil Textures and Topography Index

This U.S. Geological Survey (USGS) data release represents tabular and geospatial data for characterizing the soil texture and topography that may be relevant to Black Skimmer and Gull-billed tern nesting on bare ground sites across the U.S. portion of the Gulf of Mexico. These data characterize soil texture (e.g., sand or shell, loam), elevation, and distance to mean higher high water (MHHW) at 3

Data for Gull-billed Tern and Black Skimmer Bayesian Network Model

This U.S. Geological Survey (USGS) data release represents tabular and geospatial data for the creation and application of a Bayesian network model that predicts Black Skimmer (Rynchops niger) and Gull-billed Tern (Gelochelidon nilotica) on bare ground sites across the U.S. portion of the Gulf of Mexico. Management plans with clear priorities can help to achieve Black Skimmer and Gull-billed Tern

Environmental Sampling and Modeling Results to Characterize Surface-Water Quality at 32 Sites Across the Potomac River Watershed, 2022 (ver. 2.0, September 2024)

This data release presents chemical results from investigations of surface-water quality in the Potomac River watershed (encompassing Washington, D.C. and parts of West Virginia, Virginia, Pennsylvania, and Maryland) conducted during low-flow conditions in July through September of 2022. This sampling campaign was conducted at 32 stream sites throughout the watershed (Table 1). A suite of field pa

Chesapeake Bay Tidal Shoreline, Chesapeake Bay Program Phase 7 Model

This polygon dataset represents the tidal water shoreline of the Chesapeake Bay and its tributaries. It is based primarily on shoreline data with horizontal positional accuracy of +/- 2meters provided by the Virginia Institute of Marine Science (VIMS) Center for Coastal Resources Management (CCRM), with minor modifications in seven locations. In six instances, the modifications were made using the
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