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中国沙漠 ›› 2014, Vol. 34 ›› Issue (3): 835-840.DOI: 10.7522/j.issn.1000-694X.2013.00383

• 天气与气候 • 上一篇    下一篇

基于EEMD的明代北京地区干旱灾害特征分析

李艳萍, 陈昌春, 张余庆, 毕硕本   

  1. 南京信息工程大学 遥感学院, 江苏 南京 210044
  • 收稿日期:2013-11-18 修回日期:2013-12-16 出版日期:2014-05-20 发布日期:2014-05-20
  • 作者简介:李艳萍(1989-),女,江苏苏州人,硕士研究生,主要从事水文水资源研究。Email:15151800150@163.com
  • 基金资助:
    国家自然科学基金面上项目(41271410);江苏省高校优势学科建设工程资助项目(PAPD)资助

The Characteristics of Drought Disasters in Beijing during the Ming Dynasty (1368-1644) Based on Ensemble Empirical Mode Decomposition Method

Li Yanping, Chen Changchun, Zhang Yuqing, Bi Shuoben   

  1. School of Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Received:2013-11-18 Revised:2013-12-16 Online:2014-05-20 Published:2014-05-20
  • Contact: 陈昌春(Email:changchunc@nuist.edu.cn)

摘要: 集合经验模态分解(EEMD)是一种适用于非线性、非平稳序列的信号分析方法,将EEMD应用于气候要素时间序列,可提取真实可靠的气候变化信号。根据北京地区历史时期干旱灾害资料,采用EEMD分解方法对明代(1368—1644年)北京地区干旱灾害等级序列进行多时间尺度的分析,获得简洁且平稳性较好的固有模态函数分量,并与所统计的明代北京地区干旱灾害频次多项式拟合曲线进行对比。结果表明:将EEMD应用于干旱灾害等级序列,可以提取干旱灾害中各个尺度的变化,对明代北京地区干旱灾害进行多尺度分析。明代北京地区干旱灾害存在着2.8年、6.3年的年际周期,11.5年、26.6年、53.6年的年代际周期和118.7年、299.5年的世纪周期。北京地区干旱灾害在明代整个时间跨度上呈现着先增加后微减的变化趋势,总体而言明代中期以后旱灾明显增多。

关键词: 干旱, EEMD, 多时间尺度, 趋势分析

Abstract: Ensemble empirical mode decomposition (EEMD) method is a useful tool for nonlinear, non-stationary signal analysis, and it can be applied effectively to analysis of the climate time series. Using the historical data of drought disasters in Beijing during the Ming Dynasty, temporal scales and their trends of the drought disasters were researched with EEMD method, some simple, stable and valid patterns of intrinsic mode function (IMF) were obtained, and we compared IMF of drought disasters series with the drought frequency polynomial fitting curve. Results showed that during the period of Ming Dynasty, there exists inter-annual periods of 2.8 a, 6.3 a, decadal periods of 11.5 a, 26.6 a, 53.6 a and centinnial-scale periods of 118.7 a, 299.5 a. Overall, the drought disasters in Beijing during the Ming Dynasty increased at first and then decreased slightly, and frequency of droughts increased significantly since mid-Ming Dynasty.

Key words: drought, ensemble empirical mode decomposition (EEMD), multiple time scales, trend analysis

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