在洪水预报中的应用与改进

为了实现概率洪水预报,采用贝叶斯预报系统(BFS)中的水文不确定性处理器(HUP),对水文预报的不确定性进行分析.采用新安江模型作为确定性水文模型,以贝叶斯理论为工具,在先验分布和似然函数确定的基础上,最终得到后验分布,从而实现了概率预报.针对预报结果的特点,提出了BFS的改进方案,最后将模型应用于密赛流域.应用结果表明,BFS能够有效地提高预报精度,而改进的BFS能够进一步提高预报精度.

Application and improvement of BFS in flood forecasting

The hydrologic uncertainty processor(HUP) within the Bayesian forecasting system(BFS) was employed to investigate the hydrologic forecasting uncertainties,in order to realize probabilistic flood forecasting.The Xin’anjiang model was used to yield initial discharge forecasting series,and the posterior distribution of discharge was obtained by the selected prior distribution and the likelihood function based on the Bayesian theory,and thus the probabilistic flood forecasting could be realized.According to the characteristics of the forecasting results,an improved BFS method was proposed and applied to probabilistic flood forecasting in the Misai Basin.The results show that BFS can effectively improve the forecasting accuracy and the improved BFS method can further improve the forecasting accuracy.