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Christopher T Green

Research Hydrologist
Email: ctgreen@usgs.gov
Office Phone: 650-439-2728
Fax: 650-329-4463
ORCID: 0000-0002-6480-8194
Evaluate the hydrologic and geochemical processes that control nitrate fluxes in agricultural settings. Important questions remain about the overall regional and global importance of groundwater nitrogen fluxes, denitrification (microbial reduction of NO 3 − to N 2), and the sources of electron donors contributing to this microbial reaction. Studies are needed that apply robust methods for measuring nitrogen fluxes and denitrification among multiple sites to evaluate important factors affecting N fluxes. These results, in combination with novel methods for efficient estimation of fluxes in groundwater, facilitate estimates of N fluxes in across large regions such as the Corn Belt. Quantify the effects of complex...
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These data comprise the target and non-target VOC measurements collected from 1999 to 2016 from the shallow and deep unsaturated zone at the Amargosa Desert Research Site adjacent to a low-level radio­active waste and hazardous chemical waste facility near Beatty, Nevada. All data are compiled in electronic text files that are included with this release.
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Data to support a Groundwater Nitrate Decision Support Tool for Wisconsin
A Groundwater Nitrate Decision Support Tool (GW-NDST) for wells in Wisconsin was developed to assist resource managers with assessing how legacy and possible future nitrate leaching rates, combined with groundwater lag times and potential denitrification, influence nitrate concentrations in wells (Juckem et al. 2024). Running and using the GW-NDST software involves downloading the software code (version 1.1.0) from the code repository (https://doi.org/10.5066/P13ETB4Q), downloading GIS data for the machine learning support models (child data release "GIS files required to run the Groundwater Nitrate Decision Support Tool for Wisconsin"), downloading the parameter uncertainty file (child data release "Parameter ensemble...
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A multivariate regression model was developed to predict zero-order oxygen reduction rates (mg/L/yr) in aquifers across the State of Wisconsin. The model used a combination of dissolved oxygen concentrations and mean groundwater ages estimated with sampled age tracers from wells in the U.S. Geological Survey National Water Information System and previously published project reports from state agencies and universities. The multivariate regression model was solved using the Microsoft Excel solver, with 461 wells used for training and 46 wells held-out for validation. A total of 31 predictor variables were used for model development (56 were tested), including basic well characteristics, soil properties, aquifer properties,...
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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...
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