基于雷达测雨的实时洪水预报模型

实时洪水预报系统通常会伴随系统误差，即模型误差和观测误差．为了减小系统误差，本次研究尝试将雷达测雨技术、BP神经网络技术引入流域洪水预报中，并建立基于分布式水文模型的洪水预报模型．将该实时预报模型应用于史灌河流域．从预报的结果来看，该实时预报模型很好地解决了雷达遥感数据与水文模型的耦合，为在流域洪水预报中采用雷达测雨提供了先行的研究基础．

Weather radar rainfall data-based flood forecasting model

The system error always exists in the real-time flood forecasting system, which includes the model error and the error of observation. In order to diminish the system error, this study tried to introduce the weather radar rainfall technique and BP neural network technique into River Basin flood forecasting, and a flood forecasting model was developed based on the distributed hydrological model. The real-time forecasting model was applied to the Shiguanhe River Basin, and the result showed that the model could well realize the coupling of radar remote sensing data with the hydrological model. The model developed provided a basis for research on the application of weather radar rainfall technique to river basin flood forecasting.