| 引用本文: | Isabella BORDI,Klaus FRAEDRICH,江剑民,Alfonso SUTERA.中国东部诸流域的干旱和湿润期:模式和预测.湖泊科学,2003,15(Z1):56-67. DOI:10.18307/2003.sup07 |
| Isabella BORDI,Klaus FRAEDRICH,JIANG Jianmin,Alfonso SUTERA.Dry and Wet Periods in Eastern China Watersheds:Patterns and Predictability. J. Lake Sci.2003,15(Z1):56-67. DOI:10.18307/2003.sup07 |
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| 中国东部诸流域的干旱和湿润期:模式和预测 |
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Isabella BORDI,Klaus FRAEDRICH,江剑民,Alfonso SUTERA
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1.Department of Physics, University of Rome La Sapienza, Rome Italy;2.Institute for Meteorology, University of Hamburg, Germany;3.中国气象局培训中心, 北京 10008
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| 摘要: |
| 降水观测记录是近50a来中国九大流域干旱和湿润期时空变化分析的基础.以两年为时间尺度引入标准降水指数(SPI)来分析评价气候变化情景,结果表明黄河流域、长江流域和淮河(或珠江)流域等三个主要的区域表现为低频率变化指数:另外分析显示北方地区自20世纪70年代以来干旱更频繁的出现,在SPI时间序列中呈现负趋势变化.这也许与表征SPI信号的长周期有关(24a和48a周期).与这些长周期一起,也有一些其它方面的因素有助于频谱变化,范围从3a到9a不等.在指数时间序列中这些周期成分的存在为长期预测干旱和湿润期提供了很好的条件. |
| 关键词: 干旱和湿润期 中国东部 预测 |
| DOI：10.18307/2003.sup07 |
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| Dry and Wet Periods in Eastern China Watersheds:Patterns and Predictability |
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Isabella BORDI1, Klaus FRAEDRICH2, JIANG Jianmin3, Alfonso SUTERA4
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1.Department of Physics, University of Rome La Sapienza, Rome Italy;2.Institute for Meteorology, University of Hamburg, Hamburg Germany;3.Training Centre of China Meteorological Administration, Beijing 100081, P. R. China;4.Department of Physics, University of Rome "La Sapienza", Rome Italy
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| Abstract: |
| Rain gauge observations are the basis for an analysis of the time-space variability of dry and wet periods during the last fifty years in nine Chinese watersheds. The Standardized Precipitation Index (SPI) is introduced to assess the climatic conditions on a biennial time-scale. To capture the spatial pattern of co-variability principal component analysis (PCA) is applied to the watersheds SPI time series. Results suggest that the index low-frequency variability for watersheds is well described by that of three main areas:regions near the Yellow river, regions nearby the Yangtze River and Huaihe or Zhujiang Basin. Furthermore, the analysis shows that the northern basins from the seventies is experiencing dry conditions more frequently, which is due to the presence of a negative trend in the SPI time series. It is perhaps related to long-term periodicities that characterise the SPI signal (say 24 and 48 year). Together with these long periods there are other ones contributing to the power spectrum variance ranging from 3 up to 9 year. These periodic components in the index time series provide good chances for the long-term predictability of dryness and wetness. |
| Key words: dry and wet period eastern of china predictability |
