A calibration method for SWMM to mitigate the impact of the structure defect without considering runoff on building walls - Xu, Zhi; Ma, Chong; Gao, Xichao; Ma, Yiming; Zhou, Jinjun (Frontiers in Ecology and Evolution, 2023)
In this study, we propose a hypothesis that an automatic calibration framework can address modelling uncertainties in SWMM due to structural defects that result in the inability of the model to account for runoff generated on building walls from wind-driven rain. To test this hypothesis, we introduce a rainfall error model into the calibration framework to indirectly consider the effects of inclined wind-driven rain on building walls. We couple the optimization algorithm DREAM with SWMM using newly developed API functions. To demonstrate the effectiveness of the framework, we conduct a case study in Guangzhou, China, and assess the impacts of rainfall uncertainty on model parameter estimations and simulated runoff boundaries. Our results show that the framework is capable of improving the calibration accuracy of SWMM by considering both parameter uncertainty and rainfall uncertainty, as it captures peak flow more accurately and yields a more robust Nash Sutcliffe index compared to calibration that only considers parameter uncertainty. This framework contributes to the theory of SWMM calibration by accounting for structural defects and considering rainfall uncertainty.