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Elaheh White

(She/her/hers)

Elaheh (Ellie) White is a senior data scientist in the Data Science Branch of the USGS Water Resources Mission Area.

Professional Experience

  • USGS, [Aug 2021 – Present], Data Scientist

  • EPA ORISE, [Dec 2020 – Aug 2021], Postdoctoral Research Fellow

  • CA State Water Resources Control Board, [Sep 2020 -  Dec 2020], ML Modeler

  • UC Davis, [Jul 2015 – Sep 2020], Graduate Student Researcher

Education and Certifications

  • Ph.D., Civil and Environmental Engineering, University of California - Davis

  • M.Sc., Civil and Environmental Engineering, University of California - Davis

  • B.Sc., Civil Engineering, University of Kentucky

Science and Products

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations
Authors
Jacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) White
By
Science Synthesis, Analysis and Research Program, Advanced Research Computing (ARC)

Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models

Process-based, large-scale (e.g., conterminous United States [CONUS]) hydrologic models have struggled to achieve reliable streamflow drought performance in arid regions and for low-flow periods. Deep learning has recently seen broad implementation in streamflow prediction and forecasting research projects throughout the world with performance often equaling or exceeding that of process-based mode
Authors
Scott Douglas Hamshaw, Phillip J. Goodling, Konrad Hafen, John C. Hammond, Ryan R. McShane, Roy Sando, Apoorva Ramesh Shastry, Caelan E. Simeone, David Watkins, Elaheh (Ellie) White, Michael Wieczorek
By
Ecosystems Mission Area, Water Resources Mission Area, Climate Research and Development Program, Idaho Water Science Center, Oregon Water Science Center, Wyoming-Montana Water Science Center

Dynamics of streamflow permanence in a headwater network: Insights from catchment-scale model simulations

The hillslope and channel dynamics that govern streamflow permanence in headwater systems have important implications for ecosystem functioning and downstream water quality. Recent advancements in process-based, semi-distributed hydrologic models that build upon empirical studies of streamflow permanence in well-monitored headwater catchments show promise for characterizing the dynamics of streamf
Authors
D. Tyler Mahoney, J.R. Christensen, H.E. Golden, C.R. Lane, G.R. Evenson, Elaheh (Ellie) White, K.M. Fritz, E D'Amico, Chris D. Barton, Tanja N. Williamson, Kenton Sena, C.T. Agouridis
By
Ohio-Kentucky-Indiana Water Science Center

Data-Driven Drought Prediction Project Model Outputs for Select Spatial Units within the Conterminous United States

This metadata record describes model outputs and supporting model code for the Data-Driven Drought Prediction project of the Water Resources Mission Area Drought Program. The data listed here include outputs of multiple machine learning model types for predicting hydrological drought at select locations within the conterminous United States. The child items referenced below correspond to different
By
Water Resources Mission Area

Data to support water quality modeling efforts in the Delaware River Basin

This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this data release
By
Water Resources Mission Area
Circular calendar charts showing the projected effects of climate change on the onset and end of spawning
Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)
Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)
Circular calendar charts showing the projected effects of climate change on the onset and end of spawning

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)

Circular calendar charts showing the projected effects of climate change on the onset and end of spawning for the American Shad and the Striped Bass in the Hudson River Estuary, during two modeling periods: 1950 to 2012 and 2012 to 2099.

By
Water Resources Mission Area
Circular calendar charts showing the projected effects of climate change on the onset and end of spawning

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)

Circular calendar charts showing the projected effects of climate change on the onset and end of spawning for the American Shad and the Striped Bass in the Hudson River Estuary, during two modeling periods: 1950 to 2012 and 2012 to 2099.

By
Water Resources Mission Area
Circular calendar charts show the projected start, duration, and end of spawning for each species in each year from 1950-2099
Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)
Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)
Circular calendar charts show the projected start, duration, and end of spawning for each species in each year from 1950-2099

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)

Circular calendar charts showing the projected effects of climate change on the onset and end of spawning for the American Shad and the Striped Bass in the Hudson River Estuary, during two modeling periods: 1950 to 2012 and 2012 to 2099.

By
Water Resources Mission Area
Circular calendar charts show the projected start, duration, and end of spawning for each species in each year from 1950-2099

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)

Circular calendar charts showing the projected effects of climate change on the onset and end of spawning for the American Shad and the Striped Bass in the Hudson River Estuary, during two modeling periods: 1950 to 2012 and 2012 to 2099.

By
Water Resources Mission Area

Science and Products

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations
Authors
Jacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) White
By
Science Synthesis, Analysis and Research Program, Advanced Research Computing (ARC)

Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models

Process-based, large-scale (e.g., conterminous United States [CONUS]) hydrologic models have struggled to achieve reliable streamflow drought performance in arid regions and for low-flow periods. Deep learning has recently seen broad implementation in streamflow prediction and forecasting research projects throughout the world with performance often equaling or exceeding that of process-based mode
Authors
Scott Douglas Hamshaw, Phillip J. Goodling, Konrad Hafen, John C. Hammond, Ryan R. McShane, Roy Sando, Apoorva Ramesh Shastry, Caelan E. Simeone, David Watkins, Elaheh (Ellie) White, Michael Wieczorek
By
Ecosystems Mission Area, Water Resources Mission Area, Climate Research and Development Program, Idaho Water Science Center, Oregon Water Science Center, Wyoming-Montana Water Science Center

Dynamics of streamflow permanence in a headwater network: Insights from catchment-scale model simulations

The hillslope and channel dynamics that govern streamflow permanence in headwater systems have important implications for ecosystem functioning and downstream water quality. Recent advancements in process-based, semi-distributed hydrologic models that build upon empirical studies of streamflow permanence in well-monitored headwater catchments show promise for characterizing the dynamics of streamf
Authors
D. Tyler Mahoney, J.R. Christensen, H.E. Golden, C.R. Lane, G.R. Evenson, Elaheh (Ellie) White, K.M. Fritz, E D'Amico, Chris D. Barton, Tanja N. Williamson, Kenton Sena, C.T. Agouridis
By
Ohio-Kentucky-Indiana Water Science Center

Data-Driven Drought Prediction Project Model Outputs for Select Spatial Units within the Conterminous United States

This metadata record describes model outputs and supporting model code for the Data-Driven Drought Prediction project of the Water Resources Mission Area Drought Program. The data listed here include outputs of multiple machine learning model types for predicting hydrological drought at select locations within the conterminous United States. The child items referenced below correspond to different
By
Water Resources Mission Area

Data to support water quality modeling efforts in the Delaware River Basin

This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this data release
By
Water Resources Mission Area
Circular calendar charts showing the projected effects of climate change on the onset and end of spawning
Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)
Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)
Circular calendar charts showing the projected effects of climate change on the onset and end of spawning

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)

Circular calendar charts showing the projected effects of climate change on the onset and end of spawning for the American Shad and the Striped Bass in the Hudson River Estuary, during two modeling periods: 1950 to 2012 and 2012 to 2099.

By
Water Resources Mission Area
Circular calendar charts showing the projected effects of climate change on the onset and end of spawning

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 1)

Circular calendar charts showing the projected effects of climate change on the onset and end of spawning for the American Shad and the Striped Bass in the Hudson River Estuary, during two modeling periods: 1950 to 2012 and 2012 to 2099.

By
Water Resources Mission Area
Circular calendar charts show the projected start, duration, and end of spawning for each species in each year from 1950-2099
Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)
Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)
Circular calendar charts show the projected start, duration, and end of spawning for each species in each year from 1950-2099

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)

Circular calendar charts showing the projected effects of climate change on the onset and end of spawning for the American Shad and the Striped Bass in the Hudson River Estuary, during two modeling periods: 1950 to 2012 and 2012 to 2099.

By
Water Resources Mission Area
Circular calendar charts show the projected start, duration, and end of spawning for each species in each year from 1950-2099

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)

Uncertainties: local change - How will climate change affect the timing of fish spawning? (image 2)

Circular calendar charts showing the projected effects of climate change on the onset and end of spawning for the American Shad and the Striped Bass in the Hudson River Estuary, during two modeling periods: 1950 to 2012 and 2012 to 2099.

By
Water Resources Mission Area