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Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large-scale numerical models can be resource intensive. With the data and computer codes in this data release users can (1) reconstruct and run 115 General Simulation Models (GSMs) of groundwater flow, (2) calculate groundwater age metrics at selected GSM cells, (3) train a boosted regression tree model using the provided data, (4) predict...
Widespread nitrate contamination of groundwater in agricultural areas poses a major challenge to sustainable water resources. Efficient analysis of nitrate fluxes across large regions also remains difficult. This study introduces a method of characterizing nitrate transport processes continuously across regional unsaturated zones and groundwater based on surrogate, machine-learning metamodels of an N flux process-based model. The metamodels used boosted regression trees (BRTs) to relate mappable variables to parameters and outputs of a “vertical flux method” (VFM) applied in the Fox-Wolf-Peshtigo (FWP) area in Wisconsin. In this context, the metamodels are upscaling the VFM results throughout the region, and the...
Groundwater samples were collected from domestic and public-supply wells in the Eagle Ford study area in 2015–16, in the Fayetteville study area in 2015, and in the Haynesville study area in 2014–15. One sample of produced water was collected from a gas well in the Haynesville Shale in Rusk County, Texas in 2010, and 5 samples of produced water were collected from oil and condensate wells in the Eagle Ford Shale in Gonzales and Lavaca Counties, Texas in 2015. Groundwater samples were analyzed for major ions, nutrients, and trace elements; methane, methane H and C isotopic compositions, and C1-C5 gas composition; H and O isotopic composition of water; noble gas concentrations and isotopic compositions; tritium,...
The ascii grids associated with this data release are model inputs representing the Central Valley aquifer, California, and predicted nitrate concentrations (as NO3-N, mg/L) at two depth zones associated with private and public drinking water supply wells, respectively. The model input and prediction grids are bound by the alluvial bed boundary that defines the Central Valley. The prediction grids were produced with Boosted Regression Tree (BRT) modeling methods within a statistical modeling framework using the statistical modeling software R (R Core Team, https://www.r-project.org/) and linear interpolation within Oasis Montaj software (Geosoft, version 9.0.2). The response variable was a set of nitrate concentrations...
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