丹江口水库秋汛期长期径流预报

Long-term runoff forecasting for autumn flooding seasons in Danjiangkou reservoir based on analyzing the physical causes

针对目前长期径流预报中物理成因考虑较少的问题,以丹江口水库为例,在分析影响径流物理背景的基础上,研究前期气象因子与水库秋汛期入库径流过程的相关关系,识别影响径流的大气环流与海温等物理因子,利用主成分分析法提取主要预报信息,建立了包含大气环流因子、海温因子等气象物理信息以及前期降雨、径流等水文信息作为预报因子集的三层BP神经网络预报模型.利用1956～2008年秋汛期9、10月入库径流量进行模拟与试报,并与仅采用前期降雨径流的预测模型进行了比较,结果显示基于物理成因分析的预测模型稳定性良好,模拟及试报精度较高,9、10月试报精度平均提高约30%,分别达到87.5%和75%,并对预报年份中的丰枯特征有较好的体现.

Aiming at the lack of consideration of using the physical cause approach to forecasting long-term runoff, a BP (Back Propagation) neural network model is developed for autumn season runoff forecasts. The model considers those physical factors affecting the runoff process. The factors include the preceding atmospheric circulation, ocean and historical runoff. Using the principal component analysis, the autumn runoff can be quantitatively related to a set of physical predictors including atmospheric circulation, sea surface temperature, and antecedent precipitation and runoff. The model is applied to the Danjiangkou Reservoir. Observed September and October runoff during the period 1956-2000 are used for the model calibration, while observation of 2001-2008 are used for the model validation. The results show that the stability of the model utilizing the physical predictors is generally favorable. On average, a 30% improvement can be achieved in September and October runoff forecasting comparing to the method of using only antecedent precipitation especially during the drought and flooding years.