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  • CN 62-1070/P
  • ISSN 1000-694X
  • 双月刊 创刊于1981年
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天气与气候

1961—2012年甘肃省干湿变化特征及其影响因子

  • 王素萍 ,
  • 李耀辉 ,
  • 冯建英 ,
  • 王劲松 ,
  • 王静
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  • 中国气象局兰州干旱气象研究所 甘肃省干旱气候变化与减灾重点实验室/中国气象局干旱气候变化与减灾重点开放实验室, 甘肃 兰州 730020
王素萍(1979- ),女,甘肃合作人,助理研究员,主要从事干旱气候变化及其影响研究.Email:sp_wang 07ami@sina.com

收稿日期: 2014-01-06

  修回日期: 2014-02-02

  网络出版日期: 2014-11-20

基金资助

甘肃省青年科技基金项目(1107RJYA015);国家公益性行业(气象)科研专项项目(GYHY201006023)资助

Changes and Driving Factors of Surface Wetness Indexin Gansu, China form 1961 to 2012

  • Wang Suping ,
  • Li Yaohui ,
  • Feng Jianying ,
  • Wang Jinsong ,
  • Wang Jing
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  • Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province & Open Key Laboratory of Arid Climate Change and Reducing Disaster of China Meteorological Administration, Lanzhou Institute of Arid Meteorology, China Meteorological Administration, Lanzhou 730020, China

Received date: 2014-01-06

  Revised date: 2014-02-02

  Online published: 2014-11-20

摘要

基于1961—2012年甘肃省和青海省祁连山区54个气象站逐日气象数据,分析了甘肃省地表湿润指数的时空变化特征及各气象要素对不同区域干湿变化的影响程度.结果表明:近52年,以黄河为界,甘肃东、西部呈现出不同的干湿变化趋势,河东地区趋于干旱,尤其是陇中和陇东部分地区;河西部分区域趋于湿润,尤其是祁连山中部地区.河东变干主要是秋季显著变干,其中9月最突出;河西变湿主要是冬季变湿,尤其是在12月和1月.敏感性和贡献度分析表明,除降水量外,地表湿润指数对空气相对湿度的敏感性最高,其次是日照时数,再次是最高温度和平均风速,对最低温度的敏感性最低.近52年地表湿润指数-30%~30%的相对变化量中,降水量的贡献最大,贡献量在-30%~25%;其次是风速,贡献量在-4%~12%,最高温度、最低温度、日照时数以及相对湿度的贡献较小,在-4%~4%.

本文引用格式

王素萍 , 李耀辉 , 冯建英 , 王劲松 , 王静 . 1961—2012年甘肃省干湿变化特征及其影响因子[J]. 中国沙漠, 2014 , 34(6) : 1624 -1632 . DOI: 10.7522/j.issn.1000-694X.2014.00014

Abstract

In this study, the change of the surface wetness index (SWI) was investigated in Gansu province using the observed meteorological data from 54 stations during 1961 to 2012. The results showed that bounded by the Yellow River, SWI were different from east to west, in the east of the Yellow River it tended to dry, especially in Longdong area, and in western part it tended to wet, especially in the central of Qilian Mountain. The dry trends in eastern part mainly happened in autumn, especially in September, and wet in western area occurred in winter, especially in December and January. A differentiation equation method was used to attribute the change in surface wetness index to climate variables. The results indicate that except for precipitation, the surface wetness index was most sensitive to the change in relative humidity, followed by sunshine duration, maximum temperature, and wind speed respectively, the minimum temperature has the lowest sensitivity. The actual relative change of SWI was between -30% to 30% in the past 52 years, and precipitation was the dominant factor which contributed -30% to 25% of the change. followed by wind speed with -4% to 12%,the contribution of maximum temperature, sunshine duration and relative humidity were small than others ,which caused -4% to 4% changes of SWI.

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