Application and Research of Liuxihe Model in the Simulation of Inflow Flood at Zaoshi Reservoir

Abstract

Floods occur frequently in China, and watershed floods are caused mainly by intensive rainfall, but the spatial distribution of this rainfall is often very uneven. Thus, a watershed hydrological model that enables a consideration of a heterogeneous spatial distribution of rainfall is needed. In this study, a flood forecasting scheme based on the Liuxihe model is established for the Zaoshi Reservoir. The particle swarm optimization (PSO) algorithm is used to optimize the model parameters for flood simulation, and the model’s performance is assessed by a comparison with measured flood data. The spatial distributions of rainfall selected for this study are non-uniform, with much greater rainfall in some areas than in others in some cases. Rainfall may be concentrated in the middle of the basin, in the reservoir area, or in the upstream portion of the basin. The Liuxihe-model-based flood inflow forecasting scheme for the Zaoshi Reservoir demonstrates an excellent simulation effect, with an average peak simulation accuracy of 96.3%, an average peak time of 1.042 h early, and an average Nash–Sutcliffe coefficient of 0.799. Under the condition of an uneven spatial distribution of rainfall, the Liuxihe model simulates floods well. The PSO algorithm significantly improves the model’s simulation accuracy, and its practical application requires only the selection of a typical flood for parameter optimization. Thus, the flood simulation effect of the Liuxihe model is ideal for the watershed above the Zaoshi Reservoir, and the scheme developed in this study can be applied for operational flood forecasting.