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应用生态学报 ›› 2021, Vol. 32 ›› Issue (11): 4050-4058.doi: 10.13287/j.1001-9332.202111.014

• 研究论文 • 上一篇    下一篇

1989—2019年西北地区干燥度指数时空变化及其对气候因子的响应

柳利利1,2,3,韩磊1,2,3*,韩永贵1,2,3,高阳1,2,3,彭苓4   

  1. 1宁夏大学地理科学与规划学院, 银川 750021;
    2中阿旱区特色资源与环境治理国际合作联合实验室, 银川 750021;
    3宁夏旱区资源评价与环境调控重点实验室, 银川 750021;
    4宁夏大学农学院, 银川 750021
  • 出版日期:2021-11-15 发布日期:2022-05-15
  • 通讯作者: *E-mail: layhan@163.com
  • 作者简介:柳利利, 女, 1997年生, 硕士研究生。主要从事干旱半干旱地区生态水文研究。E-mail:Lili242073@163.com
  • 基金资助:
    本文由国家自然科学基金项目(31760236,31460220)资助

Spatio-temporal variations of aridity index and its response to climate factors in Northwest China during 1989-2019

LIU Li-li1,2,3, HAN Lei1,2,3*, HAN Yong-gui1,2,3, GAO Yang1,2,3, PENG Ling4   

  1. 1School of Geography and Planning, Ningxia University, Yinchuan 750021, China;
    2China-Arab Joint International Research Laboratory for Featured Resources and Environmental Go-vernance in Arid Regions, Yinchuan 750021, China;
    3Ningxia Key Laboratory of Resource Evaluation and Environmental Regulation in Arid Region, Yinchuan 750021, China;
    4College of Agriculture, Ningxia University, Yinchuan 750021, China
  • Online:2021-11-15 Published:2022-05-15
  • Supported by:
    This work was supported by the National Natural Science of China (31760236, 31460220).

摘要: 基于西北地区143个气象站点的气象数据,采用FAO-56 Penman-Monteith公式计算潜在蒸发量,并结合降水量计算西北地区1989—2019年干燥度指数(AI),采用Mann-Kendall趋势分析、小波分析、偏微分方程等方法来揭示其变化趋势、变化周期和气候因子对AI的贡献率。结果表明: 1989—2019年,西北地区AI整体呈不显著的减小趋势,其中,青海呈显著减小趋势,新疆呈不显著的上升趋势;研究区AI在2010年发生了突变,AI变化存在1个17年的主周期。西北地区AI呈现出由东南部向中部、西北部向中部增加的空间格局。西北地区AI变化的倾向率为-1.267·(10 a)-1,其中,甘肃、宁夏、陕西、青海和新疆AI变化的倾向率分别为-1.17、-0.41、-0.49、-1.77和-2.73·(10 a)-1。青海小灶火、新疆库尔勒、阿克苏和吐鲁番地区干旱风险发生的可能性较高。降水量和实际水汽压是影响甘肃、宁夏、青海、陕西AI变化的主要气侯因子,影响新疆AI变化的主要气侯因子为潜在蒸散、太阳辐射和平均气温。

关键词: 干燥度指数, Penman-Monteith, 贡献率, 西北地区

Abstract: Based on the meteorological data of 143 meteorological site, we calculated aridity index (AI) with the potential evaporation formulated by FAO-56 Penman-Monteith and precipitation in Northwest China during 1989-2019. Mann-Kendall trend analysis, wavelet analysis and partial differential equation were used to examine the AI change trend, variation cycle, and contribution rate of main climate impact factors to AI. The results showed that there was a non-significant decreasing trend of AI in Northwest China on the whole, a significant decreasing trend of AI in Qinghai, and a non-significant increasing trend of AI in Xinjiang during 1989-2019. There was an abrupt change of AI in the study area in 2010. There was a primary 17-year periodicity in the change of AI in Northwest China. The spatial distribution of AI was shown as a larger AI in the middle of Northwest China and a smaller AI in the Southeast and Northwest in Northwest China. The tendency rates of AI were -1.27, -1.17·(10 a)-1, -0.41, -0.49, -1.77 and -2.73·(10 a)-1 in Northwest China, Gansu, Ningxia, Shanxi, Qinghai, and Xinjiang, respectively. The possibility of drought risk was higher in Xiaozaohuo, Korla, Aksu, and Turpan region. Precipitation and actual water vapor pressure were the dominant factors of AI changes in Gansu, Ningxia, Qinghai, and Shaanxi. But the potential evapotranspiration, solar radiation, and average temperature were the main climate factors for AI changes in Xinjiang.

Key words: aridity index, Penman-Monteith, contribution rate, Northwest China.