院报 ›› 2015, Vol. 32 ›› Issue (12): 18-23.DOI: 10.11988/ckyyb.20140550

• 水资源与环境 • 上一篇    下一篇

参考作物蒸腾量的多重分形特性分析

张 杰1,刘国栋2, 3,孙怀卫2,3,吴 静3   

  1. 1.重庆水利电力职业技术学院 水利工程系,重庆 402160;2.中国科学院南京土壤研究所 土壤与农业可持续发展国家重点实验室,南京 210008;3.华中科技大学 水电与数字化工程学院,武汉 430074
  • 收稿日期:2014-07-04 出版日期:2015-12-20 发布日期:2015-12-11
  • 通讯作者: 孙怀卫(1985-),男,湖北天门人,讲师,博士,主要研究方向为水文学及水资源,(电话)13419600578(电子信箱)huaiweisun@whu.edu.cn。
  • 作者简介:张 杰(1983-),男,重庆人,讲师,主要研究方向为水资源评价,(电话)13996192362
  • 基金资助:

    国家自然科学基金项目(51309106,51239004);华中科技大学自主创新基金项目(2014QN235)

Multifractal Analysis of Time Series of Reference Crop Evapotranspiration7

ZHANG Jie1,LIU Guo-dong2,3,SUN Huai-wei2,3,WU Jing3   

  1. 1.School of Hydraulic Engineering,Chongqing Water Resources and Electric Engineering College,Chongqing402160,China;2.State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science of Chinese Academy of Sciences,Nanjing 210008,China;3.School of Hydropower and InformationEngineering,Huazhong University of Science and Technology,Wuhan 430074,China
  • Received:2014-07-04 Online:2015-12-20 Published:2015-12-11

摘要:

分析ET(Evapotranspiration,参考作物蒸腾量)的动力学特性,有助于进行中长期需水量的分析与预测。将多重分形特性分析方法应用于1978—2007年间30 a汉江流域3个典型站点(钟祥、天门、武汉)的参考作物ET时间序列。结果表明,逐日参考作物ET序列不仅具有不规则的高频振荡的特征,而且还具有明显的分形行为;在不同时间宽度(日、旬、月)下,逐日参考作物ET序列的多重分形特征最强。进一步的分析结果表明,序列中脉动引起的波动相关性和极端事件引起的胖尾概率等都是引起多重分形特征形成的原因。结合趋势转折分析方法发现:不同时间阶段内的多重分形特征显著;但在不同的时间段内,多重分形谱和局部分维宽度等都受到了极端事件的影响,且影响幅度与所处流域内的位置有关。

关键词: 水资源, 参考作物蒸腾量, 年代转际, 多重分形分析, 趋势转折分析方法

Abstract:

In order to predict water requirement in the medium-to-long period,we studied dynamics features of reference crop evapotranspiration.Multifractal analysis was applied to the time series of reference crop evapotranspiration of three stations (Zhongxiang,Tianmen and Wuhan),located in Hanjiang basin from 1978 to 2007.Results show that,the daily reference crop evapotranspiration contains characteristics of irregular high-frequency fluctuation and exhibits the strongest multifractal characteristics among different time intervals such as one day,ten days and one month.Further analysis by partition function shows that,most part of multifractality in the time series’ data was due to correlations caused by fluctuations and the fat-tailed probability distributions caused by extreme events.Moreover,the multifractal features vary within the different parts of the time series and the changes can be explained by the methods of piecewise linear fitting and trend changing points (PLFIM).Finally,both of multifractal spetrum and local piecewise width are influenced by extreme climate events in different time intervals,and the effects of extreme climate events are relevant to different locations in basin.

Key words: water resources, reference crop evapotranspiration, inter-decadal variation, multifractal analysis PLFIM

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