Nonlinear behavior of urban flood peaks in the U.S. Mid-Atlantic region

ABSTRACT Urbanization, i.e., increasing urban development areas in a watershed, is well known as a major cause of increasing flood magnitudes. This study analyzes the observed flood peaks at 262 watersheds in the U.S. Mid-Atlantic region with varying levels of urban development and free from reservoir impacts. Our analysis reveals an interesting, V-shaped nonlinear behavior: flood peaks first decrease and then increase with increasing percentage of urban development area at the watershed scale (PDAW), with the shift occurring at a PDAW threshold of around 10%. Regression analyses suggest that the V-shaped pattern primarily results from complex interactions among climate conditions (e.g., storm-event rainfall) and landscape properties (e.g., elevation, distance to the coast). A neural network model was then developed to capture such interactions, satisfactorily reproducing the V-shaped pattern with an R-squared value of 0.58, RMSE of 6.72 mm/day, and NSE of 0.55. These findings highlight the need to account for nonlinear dynamics in flood prediction and management in the coastal environment.

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Publication Details

Journal
Journal of Hydroinformatics
Published
2025-10-01
DOI
https://doi.org/10.2166/hydro.2025.005
Primary Topic
Flood Risk Assessment and Management
Type
article
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article

Nonlinear behavior of urban flood peaks in the U.S. Mid-Atlantic region

Hua Ge, L. Ruby Leung, Hong‐Yi Li
Journal of Hydroinformatics
Flood Risk Assessment and Management
article

Nonlinear behavior of urban flood peaks in the U.S. Mid-Atlantic region

Hua Ge, L. Ruby Leung, Hong‐Yi Li
article en

Abstract

ABSTRACT Urbanization, i.e., increasing urban development areas in a watershed, is well known as a major cause of increasing flood magnitudes. This study analyzes the observed flood peaks at 262 watersheds in the U.S. Mid-Atlantic region with varying levels of urban development and free from reservoir impacts. Our analysis reveals an interesting, V-shaped nonlinear behavior: flood peaks first decrease and then increase with increasing percentage of urban development area at the watershed scale (PDAW), with the shift occurring at a PDAW threshold of around 10%. Regression analyses suggest that the V-shaped pattern primarily results from complex interactions among climate conditions (e.g., storm-event rainfall) and landscape properties (e.g., elevation, distance to the coast). A neural network model was then developed to capture such interactions, satisfactorily reproducing the V-shaped pattern with an R-squared value of 0.58, RMSE of 6.72 mm/day, and NSE of 0.55. These findings highlight the need to account for nonlinear dynamics in flood prediction and management in the coastal environment.

Journal of HydroinformaticsVol. 27(10)
Pacific Northwest National Laboratory (US), University of Houston (US)
Openalex Percentile: Top 74%
Flood Risk Assessment and Management
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