Study of Flood Simulation in Small and Medium-Sized Basins Based on the Liuxihe Model

Jingyu Li, Yangbo Chen (), Yanzheng Zhu and Jun Liu
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Jingyu Li: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Yangbo Chen: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Yanzheng Zhu: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Jun Liu: School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China

Sustainability, 2023, vol. 15, issue 14, 1-16

Abstract: The uneven distribution of meteorological stations in small and medium-sized watersheds in China and the lack of measured hydrological data have led to difficulty in flood simulation and low accuracy in flood forecasting. Traditional hydrological models no longer achieve the forecasting accuracy needed for flood prevention. To improve the simulation accuracy of floods and maximize the use of hydrological information from small and medium-sized watersheds, high-precision hydrological models are needed as a support mechanism. This paper explores the applicability of the Liuxihe model for flood simulation in the Caojiang river basin and we compare flood simulation results of the Liuxihe model with a traditional hydrological model (Xinanjiang model). The results show that the Liuxihe model provides excellent simulation of field floods in Caojiang river basin. The average Nash–Sutcliffe coefficient is 0.73, the average correlation coefficient is 0.9, the average flood peak present error is 0.33, and the average peak simulation accuracy is 93.9%. Compared with the traditional flood hydrological model, the Liuxihe model simulates floods better with less measured hydrological information. In addition, we found that the particle swarm optimization (PSO) algorithm can improve the simulation of the model, and its practical application only needs one representative flood for parameter optimization, which is suitable for areas with little hydrological information. The study can support flood forecasting in the Caojiang river basin and provide a reference for the preparation of flood forecasting schemes in other small and medium-sized watersheds.

Keywords: flood simulation; distributed hydrological model; Liuxihe model; parameter optimization; runoff (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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