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

doi:10.15302/J-FASE-2023519

RESEARCH ARTICLE

Mingjin CHENG¹, Xin LIU¹, Han XIAO^1,², Fang WANG², Minghao PAN^1,², Zengwei YUAN^1,², Hu SHENG²(

)

¹. 1State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
². Lishui Institute of Ecology and Environment, Nanjing University, Nanjing 211200, China

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Abstract

● 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.

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.

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

Corresponding Author(s): Hu SHENG

Online First Date: 29 November 2023

Cite this article:

Mingjin CHENG,Xin LIU,Han XIAO, et al. ENHANCING RAINFALL-RUNOFF POLLUTION MODELING BY INCORPORATION OF NEGLECTED PHYSICAL PROCESSES[J]. Front. Agr. Sci. Eng. , 29 November 2023. [Epub ahead of print] doi: 10.15302/J-FASE-2023519.

URL:

https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2023519
https://academic.hep.com.cn/fase/EN/Y/V/I/0

Fig.1 Conceptual model for estimating rainfall-runoff pollution load.

Fig.2 Experimental site (审图号: GS 京 (2023) 2266 号). (a) Geolocation of Gusheng village. (b) Experimental vegetable field. (c) Unconstructed field. (d–f) Construction steps including building drainage paths, covering the waterproof membrane and leveling.

Tab.1 Summary of observed water quality of runoff (mg·L⁻¹)

Fig.3 Correlation analysis of the logarithm of water quality indicators. Gray shading represents the 95% confidence bands.

Fig.4 Fitting model with observation water quality data. The red dots represent monitoring data. The blue and orange curves represent simulated results from the enhanced and established wash-off models, respectively. NSE_M and NSE_T mean Nash-Sutcliffe efficiency coefficients of the enhanced and established wash-off models, respectively.

Fig.5 Modeling the cumulative rainfall pollutant load F(t). F₁(t) and F₂(t) represent pollution load from surface and wet precipitation, respectively.

Fig.6 Comparison of simulated pollutant concentration in runoff between the enhanced wash-off model and EMC method.

Fig.7 Comparing the contributions of runoff q(t) and cumulative pollution load washed out F(t) at various time ranges.

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