资料匮乏地区径流降尺度模型构建及预测

发布时间：2018-03-03 16:30

  本文选题：径流降尺度　切入点：贝叶斯神经网络　出处：《中国农村水利水电》2016年01期 　论文类型：期刊论文

【摘要】：基于贝叶斯神经网络,构建了资料匮乏地区的径流降尺度模型,模拟了叶尔羌河卡群站月平均径流,与BP神经网络的结果进行了对比,验证了BNN的优越性,并结合CMIP5三种气候模式GFDL_ESM2G,GFDL_ESM2M及MIROC5的RCP 4.5,RCP 6.0,RCP 8.5三种情景,对未来3个时段(2020年代,2050年代,2080年代)卡群站月平均径流进行了预测,并定量计算了预测的不确定性区间,研究表明:贝叶斯神经网络降尺度模型可以较好地捕捉叶尔羌河的径流特征,即相关系数达到0.9以上,效率系数达到0.8,且模拟效果比ANN较优;未来情景下,叶尔羌河流域受气温升高影响,3个时段年径流均呈现增加的趋势,增加幅度分别为75%~92%,83%~110%,88%~127%,其中RCP8.5情景下的径流增加幅度比其他情景较明显;不同月份径流存在不同程度的增加趋势,其中5-8月份变化趋势相对较明显。
[Abstract]:Based on Bayesian neural network, the downscaling model of runoff in the area of lack of data is constructed, and the average monthly runoff of Karn station in Yerqiang River is simulated. The results are compared with the results of BP neural network, and the superiority of BNN is verified. Combined with the three climate models of CMIP5, GFDL, ESM2G / GFDL2M and MIROC5's RCP 4.5RCP6.0 / RCP8.5.The monthly mean runoff of Cage stations in the next three periods is forecasted from 2020s to 2080's, and the uncertainty interval of prediction is calculated quantitatively. The results show that the downscaling model of Bayesian neural network can better capture the runoff characteristics of the Yerqiang River, that is, the correlation coefficient is more than 0.9, the efficiency coefficient is 0.8, and the simulation effect is better than that of ANN. The annual runoff of the Yerqiang River Basin was affected by the increase of temperature, and the annual runoff showed an increasing trend in the three periods. The increase range was 75 / 92and 110810810810108127respectively, in which the increase of runoff in RCP8.5 scenario was more obvious than that in other scenarios, and the increase trend of runoff in different months was different. Among them, the trend of change in May and August is relatively obvious.
【作者单位】： 河海大学水文水资源与水利工程科学国家重点实验室;
【基金】：国家自然科学基金面上项目(41371051)
【分类号】：TV121
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本文编号：1561807