ENHANCING RAINFALL-RUNOFF POLLUTION MODELING BY INCORPORATION OF NEGLECTED PHYSICAL PROCESSES

Highlights

● An improved wash-off model integrated with rainfall pollution and SCS-CN is presented.

● Nash-Sutcliffe efficiency coefficients of the enhanced model increased by 2%, 8%, 3% for chemical oxygen demand, total N, total P and 100% for NH₄⁺-N.

● Two pollution modes dominated by land and rainfall pollutant were identified.

● Refined modeling indicated 12% runoff within 15 min includes 80% to 90% the pollutant load.

Abstract

The growing need to mitigate rainfall-runoff pollution, especially first flush, calls for accurate quantification of pollution load and the refined understanding of its spatial-temporal variation. The wash-off model has advantages in modeling rainfall-runoff pollution due to the inclusion of two key physical processes, build-up and wash-off. However, this disregards pollution load from wet precipitation and the relationship between rainfall and runoff, leading to uncertainties in model outputs. This study integrated the Soil Conservation Service curve number (SCS-CN) into the wash-off model and added pollutant load from wet precipitation to enhance the rainfall-runoff pollution modeling. The enhanced wash-off model was validated in a typical rural-residential area. The results showed that the model performed better than the established wash-off model and the commonly-used event mean concentrations method, and identified two different modes of pollution characteristics dominated by land pollution and rainfall pollution, respectively. In addition, the model simulated more accurate pollutant concentrations at high-temporal-resolution. From this, it was found that 12% of the total runoff contained 80% to 95% of the total load for chemical oxygen demand, total N, and total P, whereas it contained only 15% of the total load for NH₄⁺-N. The enhanced model can provide deeper insights into non-point pollution mitigation.

Graphical abstract

Keywords

Erhai Lake / field experiment / non-point source / pollution load / rainfall runoff / wash-off model

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Acknowledgements

This work was financially supported by the Key Science and Technology Program of Yunnan Province (202202AE090034), the Key Research and Development Program of Yunnan Province (202203AC100002), and the Erhai Academy of Green Development (EAGD).

Compliance with ethics guidelines

Mingjin Cheng, Xin Liu, Han Xiao, Fang Wang, Minghao Pan, Zengwei Yuan, and Hu Sheng declare that they have no conflicts of interest or financial conflicts to disclose. This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2023. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)