自然资源学报 ›› 2016, Vol. 31 ›› Issue (4): 658-671.doi: 10.11849/zrzyxb.20150345

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基于AI指数的新疆干湿时空变化及其影响因素分析

张彦龙, 刘普幸*   

  1. 西北师范大学地理与环境科学学院,兰州 730070
  • 收稿日期:2015-04-03 修回日期:2015-09-24 出版日期:2016-04-28 发布日期:2016-04-28
  • 通讯作者: 刘普幸(1964- ),女,山西平遥人,博士,教授,硕士生导师,中国自然资源学会会员(S300001163M),主要研究方向为干旱区域环境与绿洲建设、树轮水文学等。 E-mail:fmlpx@nwnu.edu.cn
  • 作者简介:张彦龙(1989- ),男,甘肃通渭人,硕士研究生,主要研究干旱区域环境与绿洲建设。E-mail:zhangylsunshine@163.com
  • 基金资助:

    国家自然科学基金项目(40961035); 甘肃省科技计划基金项目(0803RJZA094); 甘肃省级重点学科自然地理学项目

Study on Temporal and Spatial Variation of the Dry-wet and Its Influence Factors in Xinjiang Based on Aridity Index

ZHANG Yan-long, LIU Pu-xing   

  1. College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China
  • Received:2015-04-03 Revised:2015-09-24 Online:2016-04-28 Published:2016-04-28
  • Supported by:

    National Natural Science Foundation of China, No.40961035; Science and Technology Project of Gansu Province, No.0803RJZA094; Provincial Key Disciplines of Natural Geography Project of Gansu

摘要:

论文基于新疆53个气象站点1961—2013年逐日气温、降水、风速、日照时数、相对湿度、大气环流指数和太阳黑子数据,应用Penman-Monteith模型、ArcGIS反距离加权插值、Mann-Kendall(M-K)检验、Morlet小波和主成分分析方法,分析其降水量、潜在蒸散量和AI指数时空变化及影响因素。结果表明:近53 a来,新疆降水量呈上升趋势,潜在蒸散量在波动中呈下降趋势,倾向率分别为8.81 mm/10 a和-28.73 mm/10 a,AI指数在波动中呈下降趋势,倾向率为 -0.05/10 a,多年平均值为0.5,表明新疆气候有变湿趋势。从年内分布看,降水量和潜在蒸散量呈单峰型分布,峰值分别出现在7月和8月,分别为24.58和137.12 mm;AI指数最大值滞后于降水量(7月)和潜在蒸散量(8月),出现在9月,为0.9,最小值出现在1月,为0.46。新疆潜在蒸散量空间分布为南疆大于北疆,东部大于西部;降水量北疆大于南疆;AI指数的空间分布与降水量相反,总体表现为南疆大于北疆,盆地大于山区,M-K趋势介于0~-0.02/a之间,且北疆AI指数减小趋势较南疆更显著,与北疆比南疆更湿润的事实相符。新疆降水量和潜在蒸散量分别在1987和1981年发生突变,AI指数在1981和1984年存在两个明显的突变点。Morlet小波及其功率谱分析表明,降水量存在6.49、5.71和4.35 a(p≤0.05)的周期,蒸散量存在21.37 a(p≤0.2)的周期,AI指数存在6.62、3.45 a(p≤0.1)的周期,表明AI指数可能受大气环流和厄尔尼诺的影响。主成分分析表明,AI指数与气温呈正相关,与降水量呈负相关,且南疆比北疆对降水更敏感。此外,AI指数与维尔霍扬斯克-奥伊米亚康(WYMI)、ENSO关系密切,相关系数分别为0.46(p≤0.05)和-0.34(p≤0.05)。

关键词: AI指数, Mann-Kendall检验, 小波分析, 新疆, 主成分分析

Abstract:

Based on the collected climate data regarding daily temperature, precipitation, wind speed, sunshine hour as well as related humidity from 53 meteorological stations, and atmospheric circulation index and sunspot in the study region during 1961-2013, evapotrans-piration (ET0) was estimated by applying Penman-Monteith model. Additionally, Inverse Dis-tance Weighted was applied to comprehensively investigate the temporal-spatial variations of ET0, precipitation and aridity index (AI). The abrupt change and period of ET0, precipitation and AI were characterized using comprehensive time series analysis conducted with moving M-K test and Morlet wavelet. Principal component analysis was employed to analyze the factors that influenced the AI. The results showed that: In recent 53 years, precipitation displayed an increasing trend (8.81 mm/10 a), ET0 and AI presented decreasing trend on the whole at the rates of -28.73 mm/10 a and -0.05/10 a, suggesting that the regional climate trended to be wetter in Xinjiang. As for annual distribution, ET0 and precipitation both exhibited unimodal distributions with peaks appeared in August (137.12 mm) and July (24.58 mm), the maximum in September (0.9), and the minimum in January (0.46). Spatially, the ET0 in southern Xinjiang was greater than that in northern Xinjiang, and that of east was greater than that of west; the precipitation in northern was greater than that in southern. The spatial patterns of AI and precipitation were opposite. Overall, the AI in south was greater than that in north, and that in basin was greater than that in the mountains. The M-K trend of AI was between 0 - -0.02/a, and the decreasing trend of AI in north was more extraordinary than that in south, consistent with the facts that the north of Xinjiang was wetter than the south. The abrupt changes for ET0 and precipitation occurred in 1987 and 1981, respectively. There were two distinct point mutations of AI in 1981 and 1984. Morlet wavelet and its power spectrum analysis showed: Precipitation exhibited periods of 6.49 a, 5.71 a and 4.35 a (p≤0.05), ET0 showed the period of 21.37 a (p≤0.2), and AI had periods of 6.62 a and 3.45 a (p≤0.1), indicating that it was related to atmospheric circumfluence and El Niño events to some extent. Principal component analysis demonstrated: AI was positively correlated with temperature and negatively correlated with precipitation, and the southern part of Xinjiang was more sensitive to precipitation than the northern part. Correlation coefficient between AI and WYMI, and ENSO were 0.46 (p≤0.05) and -0.34 (p≤0.05), respectively.

Key words: aridity index, Mann-Kendall trend, Morlet wavelet analysis, principal component analysis, Xinjiang

中图分类号: 

  • P426.616