Jeremy White
Dr. Jeremy White is a Supervisory Hydrologic Technician at the Virginia and West Virginia Water Science Center.
He completed his B.S. in computer science at West Texas A&M, and M.S. and Ph.D. in geology at the University of South Florida. Dr. White has extensive experience with parameter estimation and uncertainty quantification in environmental and geophysical models, as well as simulation of surface water and groundwater interactions and density-dependent groundwater flow. Dr. White also has experience using high-performance computing systems to solve massively and embarrassingly parallel problems.
Science and Products
Filter Total Items: 50
Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses
Computer models of environmental systems routinely inform decision making for water resource management. In this context, quantifying uncertainty in the important simulated outputs, and reducing uncertainty through assimilating historic system-state observations, is as important as the numerical model. However, implementing high-dimensional and stochastic workflows are challenging, often...
Authors
Jeremy White, Brioch Hemmings, Michael N. Fienen, Matthew Knowling
Extending the capture map concept to estimate discrete and risk-based streamflow depletion potential Extending the capture map concept to estimate discrete and risk-based streamflow depletion potential
A popular and contemporary use of numerical groundwater models is to estimate the discrete relation between groundwater extraction and surface-water/groundwater exchange. Previously, the concept of a “capture map” has been put forward as a means to effectively summarize this relation for decision-making consumption. While capture maps have enjoyed success in the environmental simulation...
Authors
Jeremy T. White, Linzy K. Foster, Michael N. Fienen
Approaches to highly parameterized inversion: PEST++ Version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis Approaches to highly parameterized inversion: PEST++ Version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis
PEST++ Version 5 extends and enhances the functionality of the PEST++ Version 3 software suite, providing environmental modeling practitioners access to updated Version 3 tools as well as new tools to support decision making with environmental models. Version 5 of PEST++ includes tools for global sensitivity analysis (PESTPP-SEN); least-squares parameter estimation with integrated first...
Authors
Jeremy T. White, Randall J. Hunt, Michael N. Fienen, John E. Doherty
Brightness of the night sky affects loggerhead (Caretta caretta) sea turtle hatchling misorientation but not nest site selection Brightness of the night sky affects loggerhead (Caretta caretta) sea turtle hatchling misorientation but not nest site selection
Sea turtles in the Gulf of Mexico, which are listed as either threatened or endangered under the US Endangered Species Act, face numerous threats from many sources but are particularly susceptible to the effects of light pollution on nesting beaches. Light pollution affects the distribution, density, and placement of nests on beaches, and disrupts seafinding in hatchlings emerging from...
Authors
Thomas Stanley, Jeremy White, Susan Teel, Mark Nicholas
Towards reproducible environmental modeling for decision support: A worked example Towards reproducible environmental modeling for decision support: A worked example
A fully worked example of decision-support-scale uncertainty quantification (UQ) and parameter estimation (PE) is presented. The analyses are implemented for an existing groundwater flow model of the Edwards aquifer, Texas, USA, and are completed in a script-based workflow that strives to be transparent and reproducible. High-dimensional PE is used to history-match simulated outputs to
Authors
Jeremy T. White, Linzy K. Foster, Michael N. Fienen, Matthew J. Knowling, Brioch Hemmings, James R. Winterle
A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support
Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water...
Authors
Jeremy T. White, Matthew Knowling, Michael N. Fienen, Daniel T. Feinstein, Garry W. McDonald, Catherine R. Moore
Non-USGS Publications**
White, J.T., Fienen, M.N, and Doherty,J.E. 2016. A python framework for environmental model uncertainty analysis, Environmental Modelling & Software, Volume 85, Pages 217-228, ISSN 1364-8152
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Filter Total Items: 50
Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses
Computer models of environmental systems routinely inform decision making for water resource management. In this context, quantifying uncertainty in the important simulated outputs, and reducing uncertainty through assimilating historic system-state observations, is as important as the numerical model. However, implementing high-dimensional and stochastic workflows are challenging, often...
Authors
Jeremy White, Brioch Hemmings, Michael N. Fienen, Matthew Knowling
Extending the capture map concept to estimate discrete and risk-based streamflow depletion potential Extending the capture map concept to estimate discrete and risk-based streamflow depletion potential
A popular and contemporary use of numerical groundwater models is to estimate the discrete relation between groundwater extraction and surface-water/groundwater exchange. Previously, the concept of a “capture map” has been put forward as a means to effectively summarize this relation for decision-making consumption. While capture maps have enjoyed success in the environmental simulation...
Authors
Jeremy T. White, Linzy K. Foster, Michael N. Fienen
Approaches to highly parameterized inversion: PEST++ Version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis Approaches to highly parameterized inversion: PEST++ Version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis
PEST++ Version 5 extends and enhances the functionality of the PEST++ Version 3 software suite, providing environmental modeling practitioners access to updated Version 3 tools as well as new tools to support decision making with environmental models. Version 5 of PEST++ includes tools for global sensitivity analysis (PESTPP-SEN); least-squares parameter estimation with integrated first...
Authors
Jeremy T. White, Randall J. Hunt, Michael N. Fienen, John E. Doherty
Brightness of the night sky affects loggerhead (Caretta caretta) sea turtle hatchling misorientation but not nest site selection Brightness of the night sky affects loggerhead (Caretta caretta) sea turtle hatchling misorientation but not nest site selection
Sea turtles in the Gulf of Mexico, which are listed as either threatened or endangered under the US Endangered Species Act, face numerous threats from many sources but are particularly susceptible to the effects of light pollution on nesting beaches. Light pollution affects the distribution, density, and placement of nests on beaches, and disrupts seafinding in hatchlings emerging from...
Authors
Thomas Stanley, Jeremy White, Susan Teel, Mark Nicholas
Towards reproducible environmental modeling for decision support: A worked example Towards reproducible environmental modeling for decision support: A worked example
A fully worked example of decision-support-scale uncertainty quantification (UQ) and parameter estimation (PE) is presented. The analyses are implemented for an existing groundwater flow model of the Edwards aquifer, Texas, USA, and are completed in a script-based workflow that strives to be transparent and reproducible. High-dimensional PE is used to history-match simulated outputs to
Authors
Jeremy T. White, Linzy K. Foster, Michael N. Fienen, Matthew J. Knowling, Brioch Hemmings, James R. Winterle
A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support
Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water...
Authors
Jeremy T. White, Matthew Knowling, Michael N. Fienen, Daniel T. Feinstein, Garry W. McDonald, Catherine R. Moore
Non-USGS Publications**
White, J.T., Fienen, M.N, and Doherty,J.E. 2016. A python framework for environmental model uncertainty analysis, Environmental Modelling & Software, Volume 85, Pages 217-228, ISSN 1364-8152
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.