Multi-Objective Optimization of Nature-Based Solution Layouts for Enhanced Ecosystem Services

The increasing prevalence of impervious surfaces coupled with intense rainfall has exacerbated urban waterlogging, nonpoint source pollution, and ecosystem degradation. Nature-based solutions (NbS) have emerged as effective strategies for urban stormwater management. This study proposes a four-objective simulation-optimization framework, integrating the Stormwater Management Model (SWMM) with the NSGA-II algorithm, to optimize NBS layouts while accounting for ecosystem service value (ESV). Six NbS scenarios were evaluated in a case study in Beijing, China. Results indicated that rain garden scenarios outperformed others in maximizing ESV, particularly through enhanced net carbon sequestration. Sensitivity analysis revealed that pollution control rate exhibited greater variability than runoff reduction rate, and achieving simultaneous improvements in these metrics often incurred higher costs and reduced ESV. The optimal solution achieved a 51.95% runoff reduction rate, 87.35% pollution control rate, an ESV of 2.78 × 10⁵ CNY, and a cost of 40.14 × 10⁶ CNY. This framework provides a robust reference for harmonizing cost-efficiency, water quality and quantity control, and ecosystem service enhancement in urban stormwater management.