基于Vine Copula的短期径流预报不确定性分析

定量分析短期径流预报序列的不确定性特征，对于提高水库短期运行计划的可靠性具有重要意义。针对常用多元椭圆Copula或阿基米德Copula难以有效刻画短期径流预报不确定性特征的问题，本文引入了Vine Copula对不同径流量级及不同预见期下预报不确定性进行定量评估，进而分析了先验信息对于后续时段预报不确定性的影响。以雅砻江流域锦西水库为例进行验证，结果表明：相较于传统多元Copula函数，Vine Copula构建的相对预报误差联合分布均能通过假设检验且拟合效果最好，模拟结果的统计量与实测数据相差较小；通过利用调度期内已经发生的相对预报误差信息，可以有效减小后续时段相对预报误差期望值及90%置信水平分位距，降低预报的不确定性。

Vine Copula-based analysis on uncertainty of short-term streamflow forecasting

It is important to evaluate the effect of forecast uncertainty on the decision of reservoir operation. Previous studies usually use the traditional Copula to describe uncertainty information in the short-term forecasting of streamflow sequences, but such methods are ineffective and unsatisfactory. We adopt Vine Copula to characterize the uncertainty of streamflow forecasting, and realize a quantitative evaluation of the uncertainty in the conditions of different streamflow levels and different lead times. And the effect of prior information on the subsequent forecasting uncertainty can also be analyzed. Application of this new method to the Jinxi Reservoir shows that Vine Copula can pass the hypothesis test and achieve the best fitting effect compared to the traditional Copula model, and statistically its simulation error is the lowest against the measured data. When the prior information in the same planning period is used, the expectation of its relative forecasting errors and its 90% confidence level interquartile range can be significantly reduced, thus lowering forecasting uncertainty.