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)...

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...

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...

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...

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...

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...