A Physically Based Model for Non‐Point Source Pollutant Wash‐Off Process Over Impervious Surfaces
Abstract Physically based urban wash‐off modeling presents a promising approach for investigating the dynamics of road‐deposited sediments (RDS) and the associated pollutants during rainfall events. This paper proposes a novel physically based model to predict urban wash‐off process over impervious...
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Wiley
2025-06-01
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Series: | Water Resources Research |
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Online Access: | https://doi.org/10.1029/2024WR038791 |
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author | Xue Tong Qiuhua Liang Graham Sander Gang Wang Xijun Lai |
author_facet | Xue Tong Qiuhua Liang Graham Sander Gang Wang Xijun Lai |
author_sort | Xue Tong |
collection | DOAJ |
description | Abstract Physically based urban wash‐off modeling presents a promising approach for investigating the dynamics of road‐deposited sediments (RDS) and the associated pollutants during rainfall events. This paper proposes a novel physically based model to predict urban wash‐off process over impervious surfaces, where raindrop‐induced detachment, flow‐driven detachment and deposition are computed separately. This is achieved by modifying the Hairsine‐Rose (HR) model to account for sediment trapping due to road roughness, and incorporating multi‐sized particles to capture shielding effects during detachment and particle‐size selection during deposition across varying particle size distributions (PSDs). The model is validated through laboratory experiments, including single‐size particle simulations under varying rainfall conditions and particle sizes, and multi‐size particle simulations with different PSDs. Numerical experiments are conducted to systematically examine the relationships between model parameters and influencing factors (i.e., rainfall intensity and particle size). The results enhance parameter interpretation and simplify the model. Model predictions show strong agreement with experimental measurements, demonstrating that model parameters correlate with respective influencing factors and remain physically interpretable. By incorporating multi‐sized particles to reflect the PSD, the model effectively captures the particle‐size selection phenomenon during detachment and deposition. This study provides new insights into RDS wash‐off process modeling. |
format | Article |
id | doaj-art-a955da0980a64a02bd1f0f83d2c50d96 |
institution | Matheson Library |
issn | 0043-1397 1944-7973 |
language | English |
publishDate | 2025-06-01 |
publisher | Wiley |
record_format | Article |
series | Water Resources Research |
spelling | doaj-art-a955da0980a64a02bd1f0f83d2c50d962025-07-04T11:51:25ZengWileyWater Resources Research0043-13971944-79732025-06-01616n/an/a10.1029/2024WR038791A Physically Based Model for Non‐Point Source Pollutant Wash‐Off Process Over Impervious SurfacesXue Tong0Qiuhua Liang1Graham Sander2Gang Wang3Xijun Lai4State Key Laboratory of Hydrology‐Water Resources and Hydraulic Engineering Hohai University Nanjing PR ChinaSchool of Architecture, Building and Civil Engineering Loughborough University England UKSchool of Architecture, Building and Civil Engineering Loughborough University England UKState Key Laboratory of Hydrology‐Water Resources and Hydraulic Engineering Hohai University Nanjing PR ChinaState Key Laboratory of Lake Science and Environment Nanjing Institute of Geography & Limnology, CAS Nanjing PR ChinaAbstract Physically based urban wash‐off modeling presents a promising approach for investigating the dynamics of road‐deposited sediments (RDS) and the associated pollutants during rainfall events. This paper proposes a novel physically based model to predict urban wash‐off process over impervious surfaces, where raindrop‐induced detachment, flow‐driven detachment and deposition are computed separately. This is achieved by modifying the Hairsine‐Rose (HR) model to account for sediment trapping due to road roughness, and incorporating multi‐sized particles to capture shielding effects during detachment and particle‐size selection during deposition across varying particle size distributions (PSDs). The model is validated through laboratory experiments, including single‐size particle simulations under varying rainfall conditions and particle sizes, and multi‐size particle simulations with different PSDs. Numerical experiments are conducted to systematically examine the relationships between model parameters and influencing factors (i.e., rainfall intensity and particle size). The results enhance parameter interpretation and simplify the model. Model predictions show strong agreement with experimental measurements, demonstrating that model parameters correlate with respective influencing factors and remain physically interpretable. By incorporating multi‐sized particles to reflect the PSD, the model effectively captures the particle‐size selection phenomenon during detachment and deposition. This study provides new insights into RDS wash‐off process modeling.https://doi.org/10.1029/2024WR038791Non‐point source pollutanturban wash‐offHairsine‐Rose modelstormwater quality modeling |
spellingShingle | Xue Tong Qiuhua Liang Graham Sander Gang Wang Xijun Lai A Physically Based Model for Non‐Point Source Pollutant Wash‐Off Process Over Impervious Surfaces Water Resources Research Non‐point source pollutant urban wash‐off Hairsine‐Rose model stormwater quality modeling |
title | A Physically Based Model for Non‐Point Source Pollutant Wash‐Off Process Over Impervious Surfaces |
title_full | A Physically Based Model for Non‐Point Source Pollutant Wash‐Off Process Over Impervious Surfaces |
title_fullStr | A Physically Based Model for Non‐Point Source Pollutant Wash‐Off Process Over Impervious Surfaces |
title_full_unstemmed | A Physically Based Model for Non‐Point Source Pollutant Wash‐Off Process Over Impervious Surfaces |
title_short | A Physically Based Model for Non‐Point Source Pollutant Wash‐Off Process Over Impervious Surfaces |
title_sort | physically based model for non point source pollutant wash off process over impervious surfaces |
topic | Non‐point source pollutant urban wash‐off Hairsine‐Rose model stormwater quality modeling |
url | https://doi.org/10.1029/2024WR038791 |
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