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

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

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。

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.