4.7 Article

Post-processing of reservoir releases to support real-time reservoir operation and its effects on downstream hydrological alterations

期刊

JOURNAL OF HYDROLOGY
卷 596, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.jhydrol.2021.126073

关键词

Release data; Real-time reservoir operation; Bayesian joint probability model; Ecodeficit; Ecosurplus; Uncertainty

资金

  1. Fund for Innovative Research Group of the National Natural Science Foundation of China [51721093]
  2. National Key R&D Program of China [2017YFC0404505]
  3. National Natural Science Foundation of China [71861137001]

向作者/读者索取更多资源

Research has shown that the accuracy of release data is crucial for supporting reservoir operation, while most previous studies have focused only on inflow data. By post-processing release data, significant improvements in reservoir operation accuracy can be achieved.
Reservoir operation relies on two sets of flow data to conduct error analysis: inflow data and release data. Previous studies applied only the inflow data to reduce errors and improve the reservoir's operational effectiveness. Little attention has been paid to the accuracy of the release data. However, release data provides more accurate information than inflow data for the protection of downstream river ecosystems. In this paper, we aim to illustrate the necessity of fully using the release data to improve a reservoir's operational performance in a real-time reservoir operation system. Firstly, we designed a hypothetical reservoir system, performed five numerical experiments with different ranges of model parameters, and found that the error variances of reservoir releases were always larger than those of inflows. This indicates that the post-processing of reservoir releases is required to improve the accuracy of release data and support real-time reservoir operation. Then, we used a Bayesian joint probability (BJP) model for post-processing release data to reduce errors and quantify the uncertainty. We also applied the ecodeficit and ecosurplus based on flow duration curves to evaluate downstream hydrological alterations and further demonstrate the necessity of post-processing of release data. Results showed that the BJP model led to considerable improvement in the root-mean-square error and the continuous ranked-probability score. This demonstrates that the BJP model was effective in reducing errors. Our results highlight the importance of applying release data and improving their accuracy to support reservoir operation. If the release data is neglected, operational performance will degrade.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据