基于分层贝叶斯模型的长江上游主汛期径流概率预报
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1.长江水利委员会水文局;2.长江水利委员会水文局长江三峡水文水资源勘测局;3.水利部交通运输部国家能源局南京水利科学研究院;4.河海大学水文水资源与水利工程科学国家重点实验室;5.南京信息工程大学水文与水资源工程学院

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xx

基金项目:

湖北省自然科学基金(2021CFB006),“一带一路”水与可持续发展科技基金资助项目(2020490811,2020491211)


Probability prediction of main flood runoff in the upper reaches of the Yangtze River based on hierarchical Bayesian Model
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1.Bureau of Hydrology,Changjiang Water Resources Commission;2.Hydrological Bureau of the Yangtze River Water Conservancy Commission,Yangtze River Three Gorges Hydrological and Water Resources Survey Bureau;3.Nanjing Hydraulic Research Institute;4.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering;5.Nanjing University of Information Science &6.Technology

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    摘要:

    长期径流预报是水文预报的重要领域,对流域水资源的综合管理意义重大。基于气候遥相关影响区域径流量的理论基础,本文以长江上游干流5个水文站1954~2020年主汛期(6~8月)径流为研究对象,筛选出10个大尺度气候因子作为预报因子,通过分层贝叶斯模型开展长期径流概率预报。研究表明:长江上游主汛期径流量明显受到多种大尺度气候因子综合作用,筛选出北美副高脊线位置指数等10项大尺度气候因子作为模型预测因子;以站点主汛期径流服从的对数正态分布为预测目标的先验分布,建立5条马尔科夫链,通过MCMC算法在概率空间内随机采样推求参数后验分布,概率预测结果的不确定性区间对实测值覆盖率高;通过预测结果相关性分析、受试者工作特征曲线(ROC)和连续分级概率技巧评分(CRPSS)等方法对模型模拟性能评价表明模型有效地捕捉了大尺度气候因子信息,适用于长江上游径流长期预报。

    Abstract:

    Long term runoff forecasting is an important field of hydrological forecasting, which is of great significance to the comprehensive management of watershed water resources. The runoff of five hydrological stations in the upper reaches of the Yangtze River in the main flood season (June to August) from 1954 to 2020 are taken as the reseach object. This research is based on the theory of climate teleconnection affecting regional runoff. 10 climate factors are employed as prediction factors for runoff probability prediction based on hierarchical Bayesian model. The results show that the runoff in the upper reaches of the Yangtze River in the main flood season is obviously affected by a variety of large-scale climate factors. North American subtropical high ridge position index et. al. are selected as the model prediction factors. Taking the log normal distribution as the prior distribution of the prediction target, five Markov chains are established, and the parameter posterior distribution is deduced by random sampling in the probability space through MCMC algorithm. The uncertainty interval of the probability prediction results has a high coverage of the measured values. The correlation analysis of prediction results, ROC curve and CRPSS shows that the model effectively captures the information of large-scale climate factors and is suitable for the long-term prediction of runoff in the upper reaches of the Yangtze River.

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  • 收稿日期:2022-06-18
  • 最后修改日期:2022-06-18
  • 录用日期:2022-11-07
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