A calibration method for SWMM to mitigate the impact of the structure defect without considering runoff on building walls

In this study, we propose a hypothesis that an automatic calibration framework can address modeling uncertainties in the Storm Water Management Model (SWMM) due to structural defects result inability of model account for runoff generated on building walls from wind-driven rain. To test hypothesis, introduce rainfall error into indirectly consider effects inclined rain walls. We couple optimization algorithm Differential Evolution Adaptive Metropolis (DREAM) with SWMM using newly developed API functions. demonstrate effectiveness framework, conduct case study Guangzhou, China and assess impacts uncertainty parameter estimations simulated boundaries. The results show improve average Nash–Sutcliffe index selected events by more than 5%. It also captures peak flow accurately. This contributes theory accounting considering uncertainty.