Data

The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core) and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). Three general types of data are contained within this database: 1) Modern Field Data (2008-present), 2) Master list of location information on all modern sites, and 3) Core data - l

The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, bottom type, etc.), and published core data. Three general types of data are contained within this database: 1) Modern Field Data (1995-2007), 2) Master list of location information on all moder

Corescan Hyperspectral Core Imager Mark III (HCI-III) system data were acquired for hand samples, and subsequent billets made from the hand samples, collected during the U.S. Geological Survey (USGS) 2014, 2015, and 2016 field seasons in the Nabesna area of the eastern Alaska Range. This area contains exposed porphyry deposits and hand samples were collected throughout the region in support of the

This database was developed for the Water Availability Tool for Environmental Resources (WATER) for the Delaware River Basin (DRB), a decision support tool that provides a consistent and objective method of simulating streamflow under historical, forecasted, and managed conditions (Williamson and others, 2015). This database provides historical spatial and climatic data for simulating streamflow f

Hi-Desert Water District (HDWD) is constructing a wastewater treatment plant as part of a groundwater replenishment and reuse project (GRRP) in the east hydrogeologic unit (Nishikawa, and others, 2003) of the Warren Valley Basin (7-012) (California Department of Water Resources, 2016) in Yucca Valley, CA. The HDWD plans to use reclaimed wastewater for managed aquifer recharge by spreading treated

We created a probabilistic classification model using the nonparametric machine learning technique 'Random Forests' for oil and gas development potential from low (0) to high (1) across the western US. The six predictor variables used in the model were: geophysical data showing aeromagnetic, isostatic gravity, and Bouguer gravity anomalies, geology, topography and bedrock depth. Our binary respons

The data release presents observations of riparian vegetation, topography, and ground cover in two river reaches of the Upper Colorado River within a river segment extending 208 river kilometers (rkm), from near the Colorado/Utah border to the confluence of the Green River. Methods included field observations and analysis of the plant community five times over eight years in the fall of 2010, 2012

The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC), collected multibeam bathymetry data off the coast of Santa Rosa Island, Florida in June of 2019. The data was collected as part of the USGS Resource Evaluation for Management Applications (CREMA) project. This USGS Data Release includes the resulting processed elevation point data (XYZ) as derived from a 1-m

Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center in St. Petersburg, Florida (USGS SPCSMC), conducted a geophysical survey of Cedar Island, Virginia, August 2019. During this study, multibeam bathymetry and backscatter data were collected aboard the R/V Sallenger, while single-beam bathymetry were collected aboard a towed seismic sled. Efforts were support

Groundwater is a vital resource to the Mississippi embayment region of the central United States. Regional and integrated assessments of water availability that link physical flow models and water quality in principal aquifer systems provide context for the long-term availability of these water resources. An innovative approach using machine learning was employed to predict groundwater pH across d

Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS)