The flood process simulation of the Xiushui River Basin based on STFS-Urban

Abstract

[Objective] Natural catchments typically have complex tributary systems. Under the influence of short-duration, intense rainfall, the process of runoff generation and concentration is intensified, leading to a surge in river discharge and rising water levels, which increases the likelihood of severe flooding. To improve the timeliness and accuracy of basin flood simulations, [Methods] the spatiotemporal flood simulation model STFS-Urban is used to analyze the spatiotemporal interaction mechanisms and characteristic parameter expressions under the integrated model. A standard dataset is constructed, and the coupling integration of the basin flood physical model with the improved Transformer deep learning algorithm is achieved. The Xiushui River Basin in Shenyang is selected as the study area, where flood process scenario simulations are conducted using monitoring data from two typical heavy rainfall events on “2022-07-06” and “2022-07-28.” The simulation result are validated through comparison with observed data from the Gongzhutun hydrological station. [Results] The results show that the difference between the predicted and observed water levels is less than 0.5 m, with an error within 1.5%. The error in the predicted peak time relative to the observed time is less than 1.85 hours. The constructed integrated basin flood model accurately predicts the inundation extent and flood evolution trends, which are consistent with the actual situation. The simulation efficiency is approximately 31 to 34 times higher than that of the physical model. [Conclusion] The results indicate that the established STFS-Urban basin flood integrated model is capable of effectively simulating the flood evolution process. While ensuring accuracy, the model significantly enhances computational efficiency and can provide a scientific basis for the prevention and control of river basin flood disasters and the formulation of countermeasures.

Keywords

STFS-Urban Model / basin flood simulation / deep learning / Transformer / Xiushui River / rainfall / runoff / flood routing