数字水系分级对流溪河模型中小河流洪水预报的影响

为了探讨流溪河模型在中小河流洪水预报中的适用性,以及基于DEM的数字水系提取和分级对流域洪水预报的影响,构建了江西省龙华江流域洪水预报流溪河模型,采用PSO算法优选模型参数,最后验证模型并讨论了数字水系分级对流域洪水预报的影响。结果表明:随着数字水系分级变多,流域洪水峰值增大、峰现时间提前、径流系数越大、模拟过程越接近实测值;采用流溪河模型进行中小河流洪水预报时,不能采用1级数字水系构建模型,适宜采用3级水系构建模型;流溪河模型采用PSO算法的自动优选模型参数,实际应用中只需要一场具有代表性的实测洪水过程就可以优选模型参数,有效提高了模型的性能;基于3级水系构建龙华江流域洪水预报流溪河模型,采用PSO算法优选模型参数,对50场洪水过程的模拟结果与实测值吻合很好,模型可用于龙华江流域实时洪水预报。

Impact of Digital River Channel Classification on Flood Forecasting of Small and Medium Sized Watershed Based on Liuxihe Model

The aim of this study is to explore the applicability of Liuxihe model in forecasting flood of small and medium sized watershed and the impact of DEM-based digital river channel delineation and classification on flood forecasting result. A Liuxihe model for flood forecasting in the Longhua river watershed is established, with model parameters optimized by using Particle Swarm Optimization (PSO) algorithm. Moreover, the effect of digital river channel classification on flood forecasting is discussed. Results show that higher order of digital river channel would result in higher peak flow of flood, earlier peak flow, larger runoff coefficient, and simulation result closer to factual values. Three-order of river system, rather than one order, is more appropriate when adopting the Liuxihe model for the flood forecast of small and medium sized watershed. As parameters in the Liuxihe model are optimized by PSO algorithm, only one observed flood event is required for parameter optimization in practical application, hence improving the performance of the model effectively. Validated by 50 flood events, the Liuxihe model with three orders of river system for flood forecasting of Longhua river watershed is proved feasible in real-time flood forecasting for the Longhua river watershed