Christopher T Green, Ph.D.
Christopher Green is a Research Hydrologist with the USGS Water Resources Mission Area.
Research Interests
- Flow and reactive transport in complex geological media
- Nitrogen cycling and fluxes in groundwater in agricultural areas
- Flow and solute transport in the unsaturated zone
- Gas transport in the unsaturated zone
- Effects of biofuels crops on groundwater quality
Science and Products
Scripts for projecting stream water quality using Weighted Regression on Time Discharge and Season (WRTDS) in the Delaware River Basin Scripts for projecting stream water quality using Weighted Regression on Time Discharge and Season (WRTDS) in the Delaware River Basin
This data release provides R scripts for estimating future stream water quality using Weighted Discharge on Time Discharge and Season for Projection (WRTDS-P). The technique is tested against hold-out data and is applied to four drought scenarios in the Delaware River Basin (DRB). The scripts retrieve WRTDS models from an existing data release for 39 sites in the DRB with observations...
Atmospheric fallout radionuclide data and geochemical data for soil cores from four uranium mine sites, Mohave County, Arizona, April 2022 Atmospheric fallout radionuclide data and geochemical data for soil cores from four uranium mine sites, Mohave County, Arizona, April 2022
The data represent sediment depth profiles of gamma-emitting radionuclides and major, minor, and trace elements in native soil locations around four mineralized sites in Mohave County, Arizona. The four sites represent breccia pipe uranium deposits in the Grand Canyon Region in various lifecycle stages of mining: EZ2 complex (exploration), Arizona 1 mine (standby), Pinenut mine (closed...
Model and Data Resources Supporting Water-Quality Modeling of Hydrologic Systems Model and Data Resources Supporting Water-Quality Modeling of Hydrologic Systems
This dataset provides detailed information on availability of model resources (including models and datasets) that support the modeling of six key water-quality constituents (or constituent categories) across the hydrologic system. In addition, resources associated with nine “cross-cutting” topics for modeling water quality are included, with “cross-cutting” defined herein as having...
Atmospheric fallout radionuclide data for soil cores from four uranium mine sites, Mohave County, Arizona, November 2018 Atmospheric fallout radionuclide data for soil cores from four uranium mine sites, Mohave County, Arizona, November 2018
The data represent sediment depth profiles of gamma-emitting radionuclides cesium-137 (Cs-137), lead-210 (Pb-210), lead-214 (Pb-214), and bismuth-214 (Bi-214) in native soil locations around four mineralized sites in Mohave County, Arizona. The four sites represent breccia pipe uranium deposits in the Grand Canyon Region in various lifecycle stages of mining: EZ2 complex (exploration)...
Histogram-based gradient boosted regression tree model of mean ages of shallow well samples in the Great Lakes Basin, USA Histogram-based gradient boosted regression tree model of mean ages of shallow well samples in the Great Lakes Basin, USA
Green and others (2021) developed a gradient boosted regression tree model to predict the mean groundwater age, or travel time, for shallow wells across a portion of the Great Lakes basin in the United States. Their study applied machine learning methods to predict ages in wells using well construction, well chemistry, and landscape characteristics. For a dataset of age tracers in 961...
Multivariate regression model for predicting oxygen reduction rates in groundwater for the State of Wisconsin Multivariate regression model for predicting oxygen reduction rates in groundwater for the State of Wisconsin
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...
Filter Total Items: 52
Projecting stream water quality using Weighted Regression on Time, Discharge, and Season (WRTDS): An example with drought conditions in the Delaware River Basin Projecting stream water quality using Weighted Regression on Time, Discharge, and Season (WRTDS): An example with drought conditions in the Delaware River Basin
Future water availability depends on understanding the responses of constituent concentrations to hydrologic change. Projecting future water quality remains a methodological challenge, particularly when using discrete observations with limited temporal resolution. This study introduces Weighted Regression on Time, Discharge, and Season for Projection (WRTDS-P), a novel, computationally...
Authors
Christopher Green, Robert M. Hirsch, Hedeff Essaid, Ward E. Sanford
Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses
Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities)...
Authors
Hossein Gholizadeh, T. Prabhakar Clement, Christopher Green, Geoffrey R. Tick, Alain Plattner, Yong Zhang
Gaps in water quality modeling of hydrologic systems Gaps in water quality modeling of hydrologic systems
This review assesses gaps in water quality modeling, emphasizing opportunities to improve next-generation models that are essential for managing water quality and are integral to meeting goals of scientific and management agencies. In particular, this paper identifies gaps in water quality modeling capabilities that, if addressed, could support assessments, projections, and evaluations...
Authors
Lisa Lucas, Craig J. Brown, Dale M. Robertson, Nancy T. Baker, Zachary Johnson, Christopher Green, Jong Cho, Melinda L. Erickson, Allen C. Gellis, Jeramy Roland Jasmann, Noah Knowles, Andreas Prein, Paul E. Stackelberg
Ranking river basins for stream temperature research and monitoring in the contiguous United States Ranking river basins for stream temperature research and monitoring in the contiguous United States
There is a need to prioritize research and data collection in river basins by integrating information from environmental, ecological, and socioeconomic datasets to maintain acceptable water quality for human uses and ecosystem health. Multiple anthropogenic and natural stressors are responsible for driving changes in stream temperatures that can alter ecosystems and degrade water quality...
Authors
Ramon C. Naranjo, Zachary Johnson, Lisa Lucas, Nancy T. Baker, Christopher Green
Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model
Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the...
Authors
Bahareh KarimiDermani, Christopher Green, Geoffrey Tick, Hossein Gholizadeh, Wei Wei, Yong Zhang
Prioritizing US Geological Survey science on salinization and salinity in candidate and selected priority river basins Prioritizing US Geological Survey science on salinization and salinity in candidate and selected priority river basins
The US Geological Survey (USGS) is selecting and prioritizing basins, known as Integrated Water Science basins, for monitoring and intensive study. Previous efforts to aid in this selection process include a scientifically defensible and quantitative assessment of basins facing human-caused water resource challenges (Van Metre et al. in Environmental Monitoring and Assessment, 192(7)...
Authors
Christopher H. Conaway, Nancy T. Baker, Craig J. Brown, Christopher T. Green, Douglas B. Kent
GW-NDST software v 1.1.2 GW-NDST software v 1.1.2
This version 1.1.2 release of the Groundwater Nitrate Decision Support Tool (GW-NDST) for Wisconsin supersedes the version 1.1.1 release. This version 1.1.2 release includes updated instructions in the readme file and fixes minor bugs.
GW-NDST software v 1.1.0 GW-NDST software v 1.1.0
Official version 1.1.0 software release of the Groundwater-Nitrate Decision Support Tool (GW-NDST) for Wisconsin. This release (v1.1.0) is matched with and described by Juckem, P.F., Corson-Dosch, N.T., Schachter, L.A., Green, C.T., Ferin, K.M., Booth, E.G., Kucharik, C.J., Austin, B.P., and Kauffman, L.J., 2024, Design and calibration of a Nitrate Decision Support Tool for Groundwater...
NO3GWT version 1.0.0 NO3GWT version 1.0.0
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). The GW-NDST was reviewed and approved by the journal via this official...
Science and Products
Scripts for projecting stream water quality using Weighted Regression on Time Discharge and Season (WRTDS) in the Delaware River Basin Scripts for projecting stream water quality using Weighted Regression on Time Discharge and Season (WRTDS) in the Delaware River Basin
This data release provides R scripts for estimating future stream water quality using Weighted Discharge on Time Discharge and Season for Projection (WRTDS-P). The technique is tested against hold-out data and is applied to four drought scenarios in the Delaware River Basin (DRB). The scripts retrieve WRTDS models from an existing data release for 39 sites in the DRB with observations...
Atmospheric fallout radionuclide data and geochemical data for soil cores from four uranium mine sites, Mohave County, Arizona, April 2022 Atmospheric fallout radionuclide data and geochemical data for soil cores from four uranium mine sites, Mohave County, Arizona, April 2022
The data represent sediment depth profiles of gamma-emitting radionuclides and major, minor, and trace elements in native soil locations around four mineralized sites in Mohave County, Arizona. The four sites represent breccia pipe uranium deposits in the Grand Canyon Region in various lifecycle stages of mining: EZ2 complex (exploration), Arizona 1 mine (standby), Pinenut mine (closed...
Model and Data Resources Supporting Water-Quality Modeling of Hydrologic Systems Model and Data Resources Supporting Water-Quality Modeling of Hydrologic Systems
This dataset provides detailed information on availability of model resources (including models and datasets) that support the modeling of six key water-quality constituents (or constituent categories) across the hydrologic system. In addition, resources associated with nine “cross-cutting” topics for modeling water quality are included, with “cross-cutting” defined herein as having...
Atmospheric fallout radionuclide data for soil cores from four uranium mine sites, Mohave County, Arizona, November 2018 Atmospheric fallout radionuclide data for soil cores from four uranium mine sites, Mohave County, Arizona, November 2018
The data represent sediment depth profiles of gamma-emitting radionuclides cesium-137 (Cs-137), lead-210 (Pb-210), lead-214 (Pb-214), and bismuth-214 (Bi-214) in native soil locations around four mineralized sites in Mohave County, Arizona. The four sites represent breccia pipe uranium deposits in the Grand Canyon Region in various lifecycle stages of mining: EZ2 complex (exploration)...
Histogram-based gradient boosted regression tree model of mean ages of shallow well samples in the Great Lakes Basin, USA Histogram-based gradient boosted regression tree model of mean ages of shallow well samples in the Great Lakes Basin, USA
Green and others (2021) developed a gradient boosted regression tree model to predict the mean groundwater age, or travel time, for shallow wells across a portion of the Great Lakes basin in the United States. Their study applied machine learning methods to predict ages in wells using well construction, well chemistry, and landscape characteristics. For a dataset of age tracers in 961...
Multivariate regression model for predicting oxygen reduction rates in groundwater for the State of Wisconsin Multivariate regression model for predicting oxygen reduction rates in groundwater for the State of Wisconsin
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...
Filter Total Items: 52
Projecting stream water quality using Weighted Regression on Time, Discharge, and Season (WRTDS): An example with drought conditions in the Delaware River Basin Projecting stream water quality using Weighted Regression on Time, Discharge, and Season (WRTDS): An example with drought conditions in the Delaware River Basin
Future water availability depends on understanding the responses of constituent concentrations to hydrologic change. Projecting future water quality remains a methodological challenge, particularly when using discrete observations with limited temporal resolution. This study introduces Weighted Regression on Time, Discharge, and Season for Projection (WRTDS-P), a novel, computationally...
Authors
Christopher Green, Robert M. Hirsch, Hedeff Essaid, Ward E. Sanford
Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses
Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities)...
Authors
Hossein Gholizadeh, T. Prabhakar Clement, Christopher Green, Geoffrey R. Tick, Alain Plattner, Yong Zhang
Gaps in water quality modeling of hydrologic systems Gaps in water quality modeling of hydrologic systems
This review assesses gaps in water quality modeling, emphasizing opportunities to improve next-generation models that are essential for managing water quality and are integral to meeting goals of scientific and management agencies. In particular, this paper identifies gaps in water quality modeling capabilities that, if addressed, could support assessments, projections, and evaluations...
Authors
Lisa Lucas, Craig J. Brown, Dale M. Robertson, Nancy T. Baker, Zachary Johnson, Christopher Green, Jong Cho, Melinda L. Erickson, Allen C. Gellis, Jeramy Roland Jasmann, Noah Knowles, Andreas Prein, Paul E. Stackelberg
Ranking river basins for stream temperature research and monitoring in the contiguous United States Ranking river basins for stream temperature research and monitoring in the contiguous United States
There is a need to prioritize research and data collection in river basins by integrating information from environmental, ecological, and socioeconomic datasets to maintain acceptable water quality for human uses and ecosystem health. Multiple anthropogenic and natural stressors are responsible for driving changes in stream temperatures that can alter ecosystems and degrade water quality...
Authors
Ramon C. Naranjo, Zachary Johnson, Lisa Lucas, Nancy T. Baker, Christopher Green
Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model Analyzing multi-year nitrate concentration evolution in Alabama aquatic systems using a machine learning model
Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the...
Authors
Bahareh KarimiDermani, Christopher Green, Geoffrey Tick, Hossein Gholizadeh, Wei Wei, Yong Zhang
Prioritizing US Geological Survey science on salinization and salinity in candidate and selected priority river basins Prioritizing US Geological Survey science on salinization and salinity in candidate and selected priority river basins
The US Geological Survey (USGS) is selecting and prioritizing basins, known as Integrated Water Science basins, for monitoring and intensive study. Previous efforts to aid in this selection process include a scientifically defensible and quantitative assessment of basins facing human-caused water resource challenges (Van Metre et al. in Environmental Monitoring and Assessment, 192(7)...
Authors
Christopher H. Conaway, Nancy T. Baker, Craig J. Brown, Christopher T. Green, Douglas B. Kent
GW-NDST software v 1.1.2 GW-NDST software v 1.1.2
This version 1.1.2 release of the Groundwater Nitrate Decision Support Tool (GW-NDST) for Wisconsin supersedes the version 1.1.1 release. This version 1.1.2 release includes updated instructions in the readme file and fixes minor bugs.
GW-NDST software v 1.1.0 GW-NDST software v 1.1.0
Official version 1.1.0 software release of the Groundwater-Nitrate Decision Support Tool (GW-NDST) for Wisconsin. This release (v1.1.0) is matched with and described by Juckem, P.F., Corson-Dosch, N.T., Schachter, L.A., Green, C.T., Ferin, K.M., Booth, E.G., Kucharik, C.J., Austin, B.P., and Kauffman, L.J., 2024, Design and calibration of a Nitrate Decision Support Tool for Groundwater...
NO3GWT version 1.0.0 NO3GWT version 1.0.0
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). The GW-NDST was reviewed and approved by the journal via this official...