Charles Stillwell, Ph.D.
Charlie Stillwell is a Hydrologist for the South Atlantic Water Science Center’s Watershed and Statistical Hydrology Team.
I study hydrology in altered watersheds, particularly those impacted by urbanization. How does development affect flow and water quality? Can green infrastructure counteract hydrologic alteration? I use statistical, data-driven methods to understand these patterns. My research supports water resource decision makers.
Professional Experience
Hydrologist, U.S. Geological Survey, South Atlantic Water Science Center, 2018-present
Education and Certifications
Ph.D. Biological & Agricultural Engineering, North Carolina State University, 2019
M.Eng. Biological & Agricultural Engineering, North Carolina State University, 2016
B.S. Civil Engineering, Drexel University, 2014
Science and Products
Filter Total Items: 14
Data and Code for Predicting Flood Damage Probability Across the Conterminous United States Data and Code for Predicting Flood Damage Probability Across the Conterminous United States
This data release contains the associated data described in the related primary publication, "Predicting Flood Damage Probability Across the Conterminous United States" (Collins et al. [2022], see Related External Resources section). Publicly available geospatial datasets and random forest algorithms were used to analyze the spatial distribution and underlying drivers of flood damage...
Application of the North Carolina Stochastic Empirical Loading and Dilution Model (SELDM) to Assess Potential Impacts of Highway Runoff Application of the North Carolina Stochastic Empirical Loading and Dilution Model (SELDM) to Assess Potential Impacts of Highway Runoff
In 2013, the U.S. Geological Survey (USGS) in partnership with the U.S. Federal Highway Administration (FHWA) published a new national stormwater quality model called the Stochastic Empirical Loading Dilution Model (SELDM; Granato, 2013). The model is optimized for roadway projects but in theory can be applied to a broad range of development types. SELDM is a statistically-based...
Science and Products
Filter Total Items: 14
Data and Code for Predicting Flood Damage Probability Across the Conterminous United States Data and Code for Predicting Flood Damage Probability Across the Conterminous United States
This data release contains the associated data described in the related primary publication, "Predicting Flood Damage Probability Across the Conterminous United States" (Collins et al. [2022], see Related External Resources section). Publicly available geospatial datasets and random forest algorithms were used to analyze the spatial distribution and underlying drivers of flood damage...
Application of the North Carolina Stochastic Empirical Loading and Dilution Model (SELDM) to Assess Potential Impacts of Highway Runoff Application of the North Carolina Stochastic Empirical Loading and Dilution Model (SELDM) to Assess Potential Impacts of Highway Runoff
In 2013, the U.S. Geological Survey (USGS) in partnership with the U.S. Federal Highway Administration (FHWA) published a new national stormwater quality model called the Stochastic Empirical Loading Dilution Model (SELDM; Granato, 2013). The model is optimized for roadway projects but in theory can be applied to a broad range of development types. SELDM is a statistically-based...