Simulation Performance Evaluation and Uncertainty Analysis on a Coupled Inundation Model Combining SWMM and WCA2D

Urban floods are becoming increasingly more frequent, which has led to tremendous economic losses. The application of inundation modeling to predict and simulate urban flooding is an effective approach for disaster prevention and risk reduction, while also addressing the uncertainty problem in the model is always a challenging task. In this study, a cellular automaton (CA)-based model combining a storm water management model (SWMM) and a weighted cellular automata 2D inundation model was applied and a physical-based model (LISFLOOD-FP) was also coupled with SWMM for comparison. The simulation performance and the uncertainty factors of the coupled model were systematically discussed. The results show that the CA-based model can achieve sufficient accuracy and higher computational efficiency than can a physical-based model. The resolution of terrain and rainstorm data had a strong influence on the performance of the CA-based model, and the simulations would be less creditable when using the input data with a terrain resolution lower than 15 m and a recorded interval of rainfall greater than 30 min. The roughness value and model type showed limited impacts on the change of inundation depth and occurrence of the peak inundation area. Generally, the CA-based coupled model demonstrated laudable applicability and can be recommended for fast simulation of urban flood episodes. This study also can provide references and implications for reducing uncertainty when constructing a CA-based coupled model.

Automated summary

Urban floods are causing significant economic losses and the application of inundation modeling to predict and simulate urban flooding is effective for disaster prevention. This study compares a cellular automaton (CA)-based model with a physical-based model and finds that the CA-based model has higher computational efficiency and sufficient accuracy. The resolution of terrain and rainstorm data significantly influences the model performance.