摘要
提出了一种基于集合经验模态分解的多尺度信息熵方法来分析长江流域138个气象站1961~2016年的月降水在不同时间尺度下的随机性,然后,采用模糊C均值聚类(FCM)算法对流域月降水进行空间区划,最后,探讨各区平均月降水序列与厄尔尼诺1+2区的平均海表温度(NINO1+2)之间的时滞相关性。结果表明:(1)长江流域月降水存在明显的季节、年际和年代际变化特征;(2)流域月降水在不同时间尺度下的随机性均呈现出东部高-西部低的空间分布模式,并且流域月降水的IMF分量的随机性随时间尺度的增大而降低,且不同地区间其随机性的差异越来越大;(3)流域月降水在各IMF分量上的随机性沿纬向自西向东逐渐增大,而沿经向呈现出拟均匀性;(4)流域月降水在空间上可划分为6个一致性子区域:西部高原区、西南部横断山区、北部低山盆地区、南部低山丘陵区、东南部鄱阳湖平原区和东部长江三角洲区;(5)流域各区平均月降水对NINO1+2的最佳响应时滞自东南沿海向内陆地区依次由2个月延长至4个月。
A multi-scale information entropy(EEMD-ME)method based on ensemble empirical mode decomposition(EEMD)was proposed to quantify the stochastic characteristics of monthly precipitation across the different time scales during 1961-2016 at 138 meteorological stations in the Yangtze River Basin.Then,the spatial categorization of meteorological stations was performed using the fuzzy C-means clustering(FCM)algorithm in the basin.Finally,the lag-time correlation between average monthly precipitation series in each sub-region and NINO1+2 index was discussed.The results are as follows:(1)The monthly precipitation in the Yangtze River Basin exhibits remarkable seasonal,interannual and interdecadal variation characteristics.(2)The randomness of monthly precipitation in the east of the basin is higher than that in the west under the different time scales.The randomness of IMF component of monthly precipitation decreases with the increase of time scale,and the difference of randomness among different regions is more and more obvious.(3)The stochastic characteristics of each IMF component of monthly precipitation increases gradually from west to east along the latitudinal direction,and shows quasi uniformity along the longitudinal direction.(4)The monthly precipitation in the basin can be divided into six homogeneous sub-regions:the western plateau region,the Hengduan mountain region,the northern low mountains and basin region,the southern low mountains and hills region,the southeast Poyang lake plain region and the eastern Yangtze River Delta region.(5)The optimal time lags of average areal monthly precipitation responding to NINO1+2 are diverse across the different sub-regions,and the lag period increases gradually from 2 months to 4 months from the southeast coast to the inland area.
作者
李佳佳
贺新光
胡思
LI Jia-jia;HE Xin-guang;HU Si(College of Resources and Environmental Science,Hunan Normal University,Changsha 410081,China;Key Laboratory of Geospatial Big Data Mining and Application,Hunan Province,Changsha 410081,China)
出处
《长江流域资源与环境》
CAS
CSSCI
CSCD
北大核心
2021年第1期111-121,共11页
Resources and Environment in the Yangtze Basin
基金
国家自然科学基金(41472238)
湖南省教育厅创新平台开放基金(18K018)。
关键词
长江流域
月降水
多尺度信息熵
随机性
模糊C均值聚类
Yangtze River Basin
monthly precipitation
multiscale information entropy
stochastic characteristics
fuzzy C-mean clustering
