摘要
基于黄河流域1979-2018年的ERA-Interim再分析气候与水文数据,以及CMIP5中10个气候模式下3种典型排放情景(RCP2.6、RCP4.5、RCP8.5)的全球气候变化数据,采用离散数据的处理方法,建立黄河流域贝叶斯网络模型,推断黄河流域近40余年来气候要素对径流的影响概率,预测黄河流域未来径流量。结果表明:1979-2018年黄河天然径流量呈减小趋势,基于贝叶斯网络分区间概率预测预报的径流量也呈减小趋势;黄河流域的不同区间(低、中、高)径流量对气候的敏感程度不同,但径流始终与降水相关性最高;在RCP2.6情景下,黄河流域未来20年、60年的径流量为585.50亿、588.57亿m^(3);在RCP4.5情景下,其值为585.42亿、587.53亿m^(3);在RCP8.5情景下,其值为593.50亿、585.11亿m^(3)。
With the development of social economy,the demand for water resources of the Yellow River basin is increasing.And most areas of the Yellow River basin are in arid and semi-arid areas and the ecological environment is fragile,aggravating the sensitivity of the water resources system to climate change.Although many researches have been conducted on the impact of climate factors on hydrological phenomena,the causality and the probability of interaction between climate factors and runoff are still very vague.Besides,in the study of hydrological forecast,the water balance model,the wavelet analysis,the neural network,and the fuzzy inference method can merely provide a deterministic forecast of hydrological process,while they can not quantitatively describe the uncertainty of forecast results.Based on the correlation and uncertainty of climate and hydrological systems,Bayesian network(BN)is then used to quantify the impact of climate factors on runoff and forecast the future runoff in the Yellow River basin.Based on expert knowledge bases and other scholars′research results on the relationship between climate and runoff in the Yellow River basin,six climate factors including temperature,pressure,wind speed,specific humidity,evaporation,and precipitation were determined to form the variable node of BN with runoff,and the BN model of climate runoff was constructed by the Netica.The ChiMerge method was used to discretize the ERA-Interim reanalysis of climate and hydrological data from 1979 to 2018 into three sections.After the determination of network structure and training data set,the conditional probability table of each node can be obtained by the maximum likelihood estimation,and the Bayesian influence probability between variables can be calculated by the variable elimination method.In the BN model for predicting runoff,all the data in the prediction model is divided into twelve intervals to improve the prediction accuracy.The ERA reanalysis data in years of 1979-2018 is used as the training set.The history climat
作者
赵菲菲
张青青
张宇
石旭芳
钟德钰
ZHAO Feifei;ZHANG Qingqing;ZHANG Yu;SHI Xufang;ZHONG Deyu(School of Water Resources and Electric Power,Qinghai University,Xining 810016,China;State Key Laboratory of Plateau Ecology and Agriculture,Qinghai University,Xining 810016,China;State Key Laboratory of Hydroscience and Engineering,Department of Hydraulic Engineering,Tsinghua University,Beijing 100084,China)
出处
《南水北调与水利科技（中英文）》
CAS
北大核心
2021年第3期511-519,共9页
South-to-North Water Transfers and Water Science & Technology
基金
国家重点研发计划项目(2017YFC0404303)。
关键词
贝叶斯网络
气候因子
径流
不确定性问题
黄河流域
Bayesian network
climate factor
runoff
uncertainty problem
Yellow River basin
