陆水水库洪水集合概率预报方法与应用研究

Ensemble Probability Flood Forecasting Method and Application in Lushui Reservoir

为探讨预见期内模型结构的不确定性对洪水预报精度的影响,分别选用陆水水库流域资料和欧洲中期天气预报中心(ECMWF)的降水预报数据,驱动API、新安江、GR4J三个水文模型。通过对期望值预报精度和概率预报整体性能的分析,比较贝叶斯模型平均(BMA)和改进的BMA (M-BMA)两种统计后处理方法的有效性。结果表明:两种方法的期望值预报在一定程度上提高了原始预报精度;两种方法均能提供可靠的预报区间;通过连续概率排位分数(CRPS)等多个指标分析,M-BMA方法的概率预报性能优于BMA方法。

To explore the uncertainty of hydrologic model structure in the leading time flood forecasting accuracy, we selected the dataset of Lushui Reservoir watershed and the precipitation forecast data of the European Center for Medium-Term Weather Forecast (ECMWF) to drive the three hydrological models (API, Xinanjiang and GR4J). Through the analysis of the expected value forecast accuracy and the overall per-formance of the probability forecast, the effectiveness of the two statistical post-processing methods, the Bayesian model averaging (BMA) and the improved BMA (M-BMA) were compared. The results show that the expected value forecast of the two methods improves the original forecast accuracy to a certain extent;both methods can provide reliable forecast intervals;through the analysis of multiple indicators such as Continuous ranked probability score (CRPS), the performance of the M-BMA method is better than that of the BMA method.