Urbanization is increasing the proportion of surface imperviousness, resulting in frequent urban waterlogging and in turn, triggers water pollution and ecological degradation. The establishment of low impact development (LID) facilities can effectively mitigate the impacts while generating multiple ecosystem service benefits, which is important in the context of climate change and dual carbon. This study proposes a many-objective optimization framework to optimize the layout of LID facilities. Firstly, the costs and ecosystem service benefits of different LID facilities are monetized to obtain the net cost. Secondly, runoff and water quality pollution are simulated to obtain the corresponding reduction and removal rates via the SWMM model. The total runoff control rate is combined with the net cost to form a dual-objective function. The optimal paving ratio layout of LID is acquired using the elite non-dominated sorted genetic algorithm (NSGA-II). Deterministic scenario simulation and optimization are carried out in Beijing Zijing Yayuan neighborhood as an example. The results show that the excess carbon released during construction of the permeable paving scenario increases net cost pressure, but its runoff reduction rates and water quality pollution removal rates perform well. The final optimization scenario can realize conflicting trade-offs between objectives, which can reduce direct cost inputs by up to 3.2*105 RMB and ecological compensation inputs by 1.5*105 RMB when the minimum total runoff control rate and pollutant removal rate are achieved. This study contributes to supporting decision-making stormwater management scenarios and highlights the effectiveness of permeability for flooding mitigation.
Keywords SWMM model, NSGA-II algorithm, Ecosystem service value, Low impact development, Multi-objective optimization