Robustness analysis of storm water quality modelling with LID infrastructures from natural event-based field monitoring

Sijie Tang; Jiping Jiang; Yi Zheng; Yi Hong; Eun-Sung Chung; Asaad Y Shamseldin; Yan Wei; Xiuheng Wang

doi:10.1016/j.scitotenv.2020.142007

Robustness analysis of storm water quality modelling with LID infrastructures from natural event-based field monitoring

Sci Total Environ. 2021 Jan 20:753:142007. doi: 10.1016/j.scitotenv.2020.142007. Epub 2020 Aug 26.

Authors

Sijie Tang¹, Jiping Jiang², Yi Zheng³, Yi Hong⁴, Eun-Sung Chung⁵, Asaad Y Shamseldin⁶, Yan Wei⁷, Xiuheng Wang⁸

Affiliations

¹ Shenzhen Municipal Engineering Lab of Environmental IoT Technologies, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
² Shenzhen Municipal Engineering Lab of Environmental IoT Technologies, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China. Electronic address: jiangjp@sustech.edu.cn.
³ Shenzhen Municipal Engineering Lab of Environmental IoT Technologies, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
⁴ School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 49109-1041, USA.
⁵ Department of Civil Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
⁶ Department of Civil and Environmental Engineering, The University of Auckland, Auckland 1010, New Zealand.
⁷ Shenzhen Howay Technology CO., LTD, Shenzhen 518000, China.
⁸ State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.

PMID: 32911170
DOI: 10.1016/j.scitotenv.2020.142007

Abstract

Sponge city construction (SCC) in China, as a new concept and a practical application of low-impact development (LID), is gaining wide popularity. Modelling tools are widely used to evaluate the ecological benefits of SCC in stormwater pollution mitigation. However, the understanding of the robustness of water quality modelling with different LID design options is still limited due to the paucity of water quality data as well as the high cost of water quality data collection and model calibration. This study develops a new concept of 'robustness' measured by model calibration performances. It combines an automatic calibration technique with intensive field monitoring data to perform the robustness analysis of storm water quality modelling using the SWMM (Storm Water Management Model). One of the national pilot areas of SCC, Fenghuang Cheng, in Shenzhen, China, is selected as the study area. Five water quality variables (COD, NH3-N, TN, TP, and SS) and 13 types of LID/non-LID infrastructures are simulated using 37 rainfall events. The results show that the model performance is satisfactory for different water quality variables and LID types. Water quality modelling of greenbelts and rain gardens has the best performance, while the models of barrels and green roofs are not as robust as those of the other LID types. In urban runoff, three water quality parameters, namely, SS, TN and COD, are better captured by the SWMM models than NH₃-N and TP. The modelling performance tends to be better under heavy rain and significant pollutant concentrations, denoting a potentially more stable and reliable design of infrastructures. This study helps to improve the current understanding of the feasibility and robustness of using the SWMM model in sponge city design.

Keywords: Nash-Sutcliffe efficiency coefficient (NSE); Robustness analysis; SWMM; Sponge city; Stormwater quality.