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

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.

Automated summary

Intelligent scheduling of urban drainage systems is seen as a sustainable flood management strategy. This study proposes a new intelligent scheduling model (ISM) that combines the SWMM and a multiobjective particle swarm optimization algorithm to minimize flooding volume, water level fluctuation, and operational cost. The results show that the ISM-based strategies effectively reduce flooding, water level fluctuation, and operational costs, outperforming climate change mitigation and other adaptation strategies.