Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Phillip Goodling
Phillip Goodling is a hydrologist in the Water Budget Branch (Earth System Processes Division) of the US Geological Survey Water Resources Mission Area. He joined the US Geological Survey in 2018 and joined the Water Resources Mission Area in 2023.
Phillip's research interests include:
- Machine learning and artificial intelligence applications in the geosciences
- Characterizing and forecasting drought
- Karst groundwater systems
- Building tools and pipelines for reproducible data analysis
- Low-cost and non-contact sensor systems
Education and Certifications
University of Maryland, College Park, MD
M.S. in Geology (December 2018).
Thesis: Seismic Observations of Fluvial Energy DissipationThe College of William and Mary, Williamsburg, VA
B.S. in Geology (May 2014).
Thesis: Tidal River-Aquifer Salinity Communication and its Implications for Drinking Water Quality in Jamestown, Virginia
Science and Products
Northeast Region Artificial Intelligence/Machine Learning Capability Team
Outputs for Image-based Estimation Models Outputs for Image-based Estimation Models
Monotonic Trends in Modelled Water Supply for Select Outputs from NHM-PRMS models for Hawaii, Puerto Rico, and the US Virgin Islands Monotonic Trends in Modelled Water Supply for Select Outputs from NHM-PRMS models for Hawaii, Puerto Rico, and the US Virgin Islands
Monotonic Trends in Modelled Water Supply for Select Outputs from NHM-PRMS, WRF-Hydro, and CONUS404-BA, 1983-2021 and 2000-2020 Monotonic Trends in Modelled Water Supply for Select Outputs from NHM-PRMS, WRF-Hydro, and CONUS404-BA, 1983-2021 and 2000-2020
Model output and code for a long short-term memory model forecasting streamflow drought across the Conterminous United States by focusing on percentiles below 50 percent Model output and code for a long short-term memory model forecasting streamflow drought across the Conterminous United States by focusing on percentiles below 50 percent
Model outputs and model code for machine learning models forecasting streamflow drought across the Conterminous United States Model outputs and model code for machine learning models forecasting streamflow drought across the Conterminous United States
Model Predictions, Observations, and Annotation Data for Deep Learning Models Developed to Estimate Relative Flow at 11 Massachusetts Streamflow Sites, 2017-2024 Model Predictions, Observations, and Annotation Data for Deep Learning Models Developed to Estimate Relative Flow at 11 Massachusetts Streamflow Sites, 2017-2024
Video thumbnail. Image of man wading in stream with a line across the stream. Text reads: Eyes on Streams. The Short Story.
Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Text reads: Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer
Imagery as Streamflow Data: Introducing the USGS Flow Photo ExplorerFlow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer
Imagery as Streamflow Data: Introducing the USGS Flow Photo ExplorerFlow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Thumbnail reads: Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer (AD).
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer (AD)
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer (AD)Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer (AD)
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer (AD)Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Long-term monotonic trends in water budget components in the contiguous United States: Insights from two hydrologic models Long-term monotonic trends in water budget components in the contiguous United States: Insights from two hydrologic models
Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations
Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model
Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations
A low-cost approach to monitoring streamflow dynamics in small, headwater streams using timelapse imagery and a deep learning model A low-cost approach to monitoring streamflow dynamics in small, headwater streams using timelapse imagery and a deep learning model
A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA
Streamflow Rank Estimation (SRE) Model Streamflow Rank Estimation (SRE) Model
Science and Products
Northeast Region Artificial Intelligence/Machine Learning Capability Team
Outputs for Image-based Estimation Models Outputs for Image-based Estimation Models
Monotonic Trends in Modelled Water Supply for Select Outputs from NHM-PRMS models for Hawaii, Puerto Rico, and the US Virgin Islands Monotonic Trends in Modelled Water Supply for Select Outputs from NHM-PRMS models for Hawaii, Puerto Rico, and the US Virgin Islands
Monotonic Trends in Modelled Water Supply for Select Outputs from NHM-PRMS, WRF-Hydro, and CONUS404-BA, 1983-2021 and 2000-2020 Monotonic Trends in Modelled Water Supply for Select Outputs from NHM-PRMS, WRF-Hydro, and CONUS404-BA, 1983-2021 and 2000-2020
Model output and code for a long short-term memory model forecasting streamflow drought across the Conterminous United States by focusing on percentiles below 50 percent Model output and code for a long short-term memory model forecasting streamflow drought across the Conterminous United States by focusing on percentiles below 50 percent
Model outputs and model code for machine learning models forecasting streamflow drought across the Conterminous United States Model outputs and model code for machine learning models forecasting streamflow drought across the Conterminous United States
Model Predictions, Observations, and Annotation Data for Deep Learning Models Developed to Estimate Relative Flow at 11 Massachusetts Streamflow Sites, 2017-2024 Model Predictions, Observations, and Annotation Data for Deep Learning Models Developed to Estimate Relative Flow at 11 Massachusetts Streamflow Sites, 2017-2024
Video thumbnail. Image of man wading in stream with a line across the stream. Text reads: Eyes on Streams. The Short Story.
Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Text reads: Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer
Imagery as Streamflow Data: Introducing the USGS Flow Photo ExplorerFlow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer
Imagery as Streamflow Data: Introducing the USGS Flow Photo ExplorerFlow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Thumbnail reads: Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer (AD).
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer (AD)
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer (AD)Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer (AD)
Imagery as Streamflow Data: Introducing the USGS Flow Photo Explorer (AD)Flow is a critical variable in streams since it affects aquatic and riparian biological communities and human uses of water (i.e., recreation, public water supply, etc.). Flow regimes are changing due to anthropogenic (e.g., water withdrawals) and natural impacts (e.g., extreme weather events).