基于水动力学模型的实时糙率反推在洪水预报中的应用

Application of Real-time Roughness Updating Based on Hydrodynamic Model in Flood Forecasting

针对洪水预报中糙率的实时修正问题,采用卡尔曼滤波技术实现动量方程实时校正糙率,以提高水动力学模型的洪水预报精度。同时考虑河道下游糙率对上游水深的影响,使反演的糙率更显客观。选择长江干流寸滩至万县河段进行试验,应用2004年该河段洪水资料作验证。结果表明,实时反演的糙率对提高洪水预报精度有效。

To detect the effect of roughness parameters in real-time updating for flood forecasting,and to improve the accuracy of hydraulic forecasting model, the Kalman filter was applied to realize the real-time updating of the roughness parameters by the momentum equation. In order to reflect the roughness parameters impersonality, the influence of water depth,caused by the downstream river segment can he accounted for. The calculation of flood forecasting for river section from Cuntan to Wanxian of Yangtze River verifies that the inverse analysis of channel parameters can be resultful in promoting the accuracy of forecasting by adopting the flood information of 2004.