Detection of trends in precipitation during 1960-2008 in Jiangxi province, southeast China
Detection of trends in precipitation during 1960-2008 in Jiangxi province, southeast China
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DOI：
10.1007/s00704-013-0831-2
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发表时间：
2013-10-01
影响因子：
3.4
通讯作者：
Zhang, Jinchi
中科院分区：
文献类型：
--
作者：
Huang, Jin;Sun, Shanlei;Zhang, Jinchi
降水量的变化对人类生活产生巨大影响，研究其变化规律具有重要意义。为探讨江西省1960 - 2008年降水量、降水极值和降水集中度的时空变化规律，采用Mann-Kendall检验方法对江西省1960 - 2008年降水量、降水极值和降水集中度的时空变化特征进行了分析。结果表明，年、季、月尺度上降水正、负趋势站间差异显著，1月、8月、冬季和夏季降水呈显著增加趋势，10月和秋季降水呈显著减少趋势。对于极端降水，大多数降水指数表明，极端降水的强度和天数都在增加，平均降水量，特别是在一个潮湿的日子，表现出显着的正趋势。在降水集中度方面，月降水异质性呈不显著的下降趋势，而最大降水日数的贡献呈不显著的上升趋势。利用重标极差分析方法对降水量、极端降水量和降水集中度的变化过程进行了长期持续性检验。
Changes in precipitation exert an enormous impact on human life, and it is of vital importance to study regular patterns of meteorological and hydrological events. In order to explore the changing spatial and temporal patterns of precipitation amounts, precipitation extremes and precipitation concentration in Jiangxi province in southeast China between 1960 and 2008, several precipitation indices series were analysed using the Mann-Kendall test in this study. Our results indicate remarkable differences among the stations with negative and positive precipitation trends at the annual, seasonal and monthly scales, significant increasing trends are mainly found during January, August, winter and summer, while significant decreasing trends mostly are observed during October and autumn. For precipitation extremes, most precipitation indices suggest that both the intensity and the days of extreme precipitation are increasing; the mean precipitation amount, especially, on a wet day shows a significant positive trend. When it comes to precipitation concentration, the monthly rainfall heterogeneity shows an insignificant downward trend, while the contribution of the days with greatest rainfall displays an insignificant upward trend. Furthermore, the long-range persistence is detected for changing process of precipitation amount, extreme precipitation and precipitation concentration using the Rescaled Range Analysis.