Optimization of climate adaptability for low-impact development stormwater drainage systems based on CMIP6

Abstract

[Objective] With the intensification of global climate change, the frequency and intensity of extreme rainfall events have significantly increased, often overwhelming traditional urban stormwater drainage systems. To mitigate the risks of urban flooding under changing climate conditions, a climate adaptation optimization method for low-impact development(LID) stormwater drainage systems was proposed, based on projections from the CMIP6(Coupled Model Intercomparison Project Phase 6) models. [Methods] Taking a district in Jiujiang City as a case study, the optimal CMIP6 rainfall projection scenario was selected by integrating Taylor diagram scores and interannual variability scores from various models. These rainfall scenarios were then used for multi-objective optimization of the SWMM(Storm Water Management Model) with NSGA-II(Non-dominated Sorting Genetic Algorithm II). The scale of the LID facilities was optimized based on Pareto optimal result. [Results] The findings indicated that all three models predict increased rainfall under various future climate scenarios, with FGOALS-g3 simulating the highest total annual rainfall and daily rainfall, along with greater uncertainty. As investment in LID facilities increased, both runoff volume and the number of overflow nodes were effectively controlled. The moderate investment scenario, costing 2.602 7 million yuan, reduced runoff by 21.55%, while the maximum investment scenario, costing 5.195 8 million yuan, reduced runoff by 25.00%. The number of overflow nodes decreased by 14.12% and 18.82%, respectively. Compared to the baseline conditions, the peak runoff under the moderate and maximum investment scenarios was reduced to 0.64 m³/s and 0.62 m³/s, accounting for approximately 86.49% and 83.71% of the original values, respectively. [Conclusion] The result demonstrate that future climate change will place greater pressure on urban flooding due to extreme rainfall events. Analysis of the simulation result indicates that both the moderate and maximum investment LID facility plans effectively control runoff volume and the number of overflow nodes. This suggests that the optimization strategy is effective in addressing future extreme rainfall impacts. The method provides a valuable reference for flood risk management and adaptive urban planning in similar built-up areas.

Keywords

climate change / future rainfall projections / LID facilities / multi-objective optimization / climate adaptation / impact factors