水库月平均流量滚动预报及其不确定性研究

可靠的入库流量预报是支撑水库科学长期调度决策的基础。针对中长期流量预报模型预见期有限及流量预报存在不确定性的问题，采用人工神经网络滚动预报不同预见期流量，在此基础上，利用Copula函数建立预报误差序列的联合分布函数，实现对水文预报误差序列的随机模拟，从而定量描述流量预报不确定性。三峡水库非汛期后期月平均流量预报及其不确定性研究结果表明：所构建的非汛期月平均流量预报模型预报效果较好，可用于滚动作业预报；Copula函数能较好描述预报相对误差序列间相关性，模拟序列相关系数、统计特征值和经验分布与实测序列相差较小，模拟效果较好。研究成果可为水库开展长期优化调度提供有效入库流量信息支撑和决策支持。

Rolling forecast for reservoir monthly average flows and its uncertainty

Reliable reservoir inflow forecast is the basis of long-term reservoir scheduling decision. This paper describes a new method of rolling flow forecast that gradually increases forecast horizon using the artificial neural network to overcome the limitation of traditional medium- and long-term flow forecast models on forecast horizon and their uncertainty in flow forecasting. A Copula function is adopted to construct joint distribution functions for the sequences of prediction errors and realize random simulation of hydrological prediction errors, and thus this method can describe flow forecast uncertainties quantitatively. Calculations of the monthly average flow and the uncertainties for the Three Gorges reservoir in late non-flood season show that our new method is satisfactory and applicable to practical operation of rolling forecast. Copula functions well describe the correlation between prediction relative error sequences, and often produce small deviation of the correlation coefficients, statistics, and empirical distributions of the simulated sequences from those of the observed sequences.