Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability

Kun Xie, Jong-Suk Kim, Linjuan Hu (), Hua Chen (), Chong-Yu Xu, Jung Hwan Lee, Jie Chen, Sun-Kwon Yoon, Di Zhu, Shaobo Zhang and Yang Liu
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Kun Xie: Wuhan University
Jong-Suk Kim: Wuhan University
Linjuan Hu: Water Resources Bureau of Chenzhou
Hua Chen: Wuhan University
Chong-Yu Xu: University of Oslo
Jung Hwan Lee: K-Water Research Institute
Jie Chen: Wuhan University
Sun-Kwon Yoon: Seoul Institute of Technology
Di Zhu: Wuhan University
Shaobo Zhang: Wuhan University
Yang Liu: Wuhan University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 1, No 6, 111 pages

Abstract: Abstract Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couples the Storm Water Management Model (SWMM) and a multiobjective particle swarm optimization algorithm is proposed for a simulation–optimization framework. The objectives of the ISM are to minimize the flooding volume, front-pool water level fluctuation, and operational cost. Synthetic rainfall events with different durations and return periods based on the Gumbel distribution and observed rainfall events are utilized to comprehensively assess the designed model's performance in the Dealim3 catchment, South Korea. The selected ISM-based scheduling strategies are assessed in accordance with climate change mitigation (i.e., reducing greenhouse gas emissions) and local adaptation strategies (i.e., improving drainage systems). The results indicate that these strategies generated by ISM lead to reductions in flooding, water level fluctuation, and operational costs. The maximum daily rainfall with a 100-year return period increased by 2.1% and 6.8% during 2025–2064 under SSP1-2.6 and SSP5-8.5, respectively, compared with the historical period (1975–2014), thereby increasing the magnitude of urban flooding. The ISM may also significantly lower the flooding process at specific nodes. The ISM-based strategy outperforms climate change mitigation and other adaptation strategies. This study shows that the ISM-based strategy are very useful to deal with the impact of climate change on urban flooding.

Keywords: Urban drainage system; Urban flooding; Simulation optimization; Adaptation strategy; Mitigation strategy (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-022-03357-0

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