分布式降雨输入与汇流模拟对洪峰计算的影响

3000 km2以下中小流域洪水灾害是当前洪涝灾害防治的薄弱环节，提高洪峰预报精度、降低不确定性是当务之急。论文以伊河河源地区东湾流域为例，基于新安江模型和蒙特卡洛抽样方法，通过集总式、降雨集总–汇流分布式、降雨分布–汇流集总式和降雨分布–汇流分布式4种方式，研究降雨分布式输入与汇流分布式模拟对洪峰精度和不确定性的影响。结果表明：（1）降雨分布式输入总体上能够提高洪峰精度，但对于某些降雨空间分布极端不均的洪水，则会扩大洪峰误差；（2）降雨分布式输入使新安江模型汇流参数相互独立，扩大了洪峰计算的不确定性；（3）汇流分布式模拟总体上能够减小洪峰计算的不确定性。研究结果为中小河流洪水预报提供了有益的参考。

Influence of distributed rainfall input and routing simulation on flood peak flow calculation

Flood forecasting for small and medium-sized catchments with an area smaller than 3000 km2 is a weak spot in the national flood forecasting system. For such catchments, it is imperative to improve accuracy and reduce uncertainty in flood peak forecasting. This paper examines the effects of distributed rainfall input and distributed routing simulation on accuracy and uncertainty in peak flow estimation through a case study of the Dongwan catchment of the upper Yiluo River. Based on the Xinanjiang model and Monte Carlo sampling method, we adopt four methods: lumped simulation, lumped rainfall-distributed routing simulation, distributed rainfall-lumped routing simulation, and distributed rainfall-distributed routing simulation. The results show that a distributed rainfall input improves overall accuracy in peak flow estimation, but it increases estimation errors greatly for rainfall events with extremely uneven spatial distribution is inputted. A distributed rainfall input contains parameters independent of each other and will raise uncertainty in flood peak flow calculations. Distributed routing simulations reduce overall uncertainty in flood peak calculation. The results are useful to flood forecasting for small and medium-sized catchments.