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1961—2014年中国干燥度指数的时空变化研究
引用本文:王利平,文明,宋进喜,窦馨逸.1961—2014年中国干燥度指数的时空变化研究[J].自然资源学报,2016,31(9):1488-1498.
作者姓名:王利平  文明  宋进喜  窦馨逸
作者单位:西北大学城市与环境学院,西安 710127
基金项目:陕西省重点科技创新团队(2014KCT-27)
摘    要:论文采用中国1961—2014年530个气象站数据,运用FAO-56 Penman-Monteith公式计算潜在蒸散发量,并结合降水量计算中国1961—2014年干燥度指数(Aridity index,AI),然后采用Mann-Kendall趋势检验、突变检验和小波分析对其进行时空变化分析。结果表明:1)1961—2014年中国整体、西北地区、青藏地区的干燥度指数均呈显著减小趋势,而南方地区干燥度指数呈微弱减小趋势,北方地区干燥度指数呈不显著增加趋势。中国干燥度指数的突变发生于1986年,而各个分区的突变时间有所差异。2)1961—2014年中国整体、北方地区和西北地区干燥度指数变化主周期均为28 a,南方地区稍晚1 a,青藏地区提前2 a,所有地区均在主周期上经历了变大—变小—变大的过程。同时所有地区也存在不同时间尺度的次周期变化。3)整个中国、西北地区和南方地区潜在蒸散发量的减少和降水量的增加,共同引起了干燥度指数的减小。在北方地区,年降水量的显著减小和潜在蒸散发减小引起干燥度指数呈现微弱增加趋势。在青藏地区,潜在蒸散发的微弱增加和降水量显著增加引起干燥度指数呈现显著减小趋势。4)中国干燥度指数在空间格局上和降水的分布相反,呈现出西北大、东南小的特征。北方地区整体干燥度指数偏小,但中部区域降水相对减少,蒸发能力增强,导致干燥度指数相对偏大。南方地区气温较高,蒸发能力强,但雨量充沛,是我国干燥度指数最小的区域。西北地区较为干燥,降水少,蒸发强,是我国干燥度指数最大的区域。青藏地区由于青藏高原的阻挡作用以及东部地区较为丰富的降水量,使得干燥度指数由东向西逐渐增加,呈现西干东湿的格局。

关 键 词:Mann-Kendall检验  Penman-Monteith  干燥度指数  小波分析  自然地理学  
收稿时间:2016-01-28
修稿时间:2016-04-28

Spatial-temporal Variation of Aridity Index during 1961-2014 in China
WANG Li-ping,WEN Ming,SONG Jin-xi,DOU Xin-yi.Spatial-temporal Variation of Aridity Index during 1961-2014 in China[J].Journal of Natural Resources,2016,31(9):1488-1498.
Authors:WANG Li-ping  WEN Ming  SONG Jin-xi  DOU Xin-yi
Institution:College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
Abstract:Aridity index (AI) is a typically reliable indicator of the wet and dry degree in a region, which is widely used in geography and ecology research, and is often involved in global change research. In this study, based on the data from 530 meteorological stations during 1961-2014 in China, potential evapotranspiration was derived using the FAO-56 Penman-Monteith formula, then AI was calculated using potential evapotranspiration and precipitation and analyzed with the Mann-Kendall (MK) trend test, abrupt change test and wavelet analysis. The conclusions were obtained as follows: 1) The AI in China generally shows a significant decreasing trend during 1961-2014 with significant spatial differences. This holds true for the northwestern region and Tibetan region. A weak decreasing trend of AI is shown in the southern region, while a non-significant increasing trend of AI is shown in the northern region. An abrupt change of AI in China occurred in 1986 according to the MK abrupt change test. 2) According to wavelet analysis, there is a primary 28-year period of AI in China and in the northern and northwestern regions, 29-year period in the southern region and 26-year period in the Tibetan region; meanwhile, there are different secondary periods in different regions. 3) Potential evapotranspiration and precipitation are two major factors influencing the spatial and temporal variation of AI. The decreasing trends of AI in China and in the northwestern and southern region were caused by the decrease of potential evapotranspiration and the increase of precipitation, while the decrease of potential evapotranspiration and precipitation resulted in the non-significant increasing trend of AI in the northern region, and the weak increase of potential evapotranspiration and the significant increase of precipitation resulted in the significant decreasing trend of AI in the Tibetan region. 4) The spatial distribution of AI in China shows obviously opposite pattern with precipitation, with larger AI in the northwestern region and smaller AI in the southeastern region. AI in the northern region is relatively smaller, except in its middle area where a relative larger AI is presented due to the relative smaller precipitation and larger evaporation. AI in the southern region is the lowest in China due to the great amount of precipitation, while AI in the northwestern region is the largest in China due to the small amount of precipitation and strong evaporation. Due to the highest elevation in the Tibetan region and a relatively high quantity of precipitation in its eastern area, AI in the Tibetan region gradually increases from the east to west.
Keywords:physical geography  aridity index (AI)  Penman-Monteith formula  Mann-Kendall test  wavelet analysis
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