Optimization model of storage detention tanks based on nonlinear time-varying process of surface runoff under low impact development mode

Stormwater detention tanks (SDTs), a key component of gray infrastructure in low-impact development (LID), are crucial for managing excessive rainfall and mitigating overflow pollution. However, in data-limited areas, SDTs often lack precision in achieving multiple objectives, including removing runoff pollution, regulating runoff volume, and reducing peak flow. In this study, we propose an optimized SDT model based on the nonlinear time-varying process of surface runoff generation (NTVP-SDT). This model comprises modules for rainfall characteristics (intensity and pattern), nonlinear time-varying surface runoff generation, two-stage pollutant load calculation, and SDT volume determination. They can more accurately calculate peak flow reduction and total pollutant load removal in SDT with limited data, while also adapting to complex urban surfaces and time-varying rainfall processes. When applied to a sponge city case in a commercial area in Northwestern China, this model reduces the required SDT volume by 23.5% compared to traditional empirical calculations, while achieving the same runoff reduction. By analyzing the response relationship between different design objectives, different rainfall recurrence periods, and key parameters of SDT, the multi-peak morphological changes in the inflow process and pollution load process of the system have been clarified, as well as the non-linear decreasing function relationship between the change of SDT volume, unit effective volume runoff reduction, and recurrence interval. The findings contribute to improving the design accuracy of multi-objective SDT and upgrading urban stormwater management models.