选用塔克拉玛干沙漠周边40个气象站1961-2009年日降水资料和4个极端弱降水指标，分析该地区极端弱降水的时空变化特征。采用M-K法和F检验对各站点降水指标的变化趋势及变化率进行检验和计算，并利用Monte Carlo模拟进行区域显著性检验。由 Copula函数得到两降水指标的联合分布，计算两降水指标的联合重现期。结果表明：（1）年最长连续无降水日数（CDD）多为80～100 d，呈显著减少趋势；出现频率最高的每年日降水量小于降水日序列25%分位数的日数（D25）为0～10 d，呈显著增加趋势；每年日降水量小于降水日序列25%分位数的总降水量（P25）的值集中在0～1.5 mm，2～3 mm的 P25从2000年才开始出现；出现频率最高的每年日降水量小于降水日序列25%分位数的日平均降水量（I25）为0.1～0.3 mm，I25超过0.4 mm的情况极少出现。（2）CDD与D25和P25各自五年一遇值的空间分布相反。除CDD和D25均大于各自五年一遇值的联合重现期较长外，其余各类型联合重现期较短。沙漠周边地区发生不同类型极端弱降水事件的概率不同。
With the daily precipitation data of 40 meteorological stations surrounding the Taklimakan Desert from 1961 to 2009 and four precipitation indices, the spatio-temporal variations of extremely weak precipitation in this area were analyzed in this paper. The Mann-Kendall method and F test were used to verify and calculate the trends and the changing rates of these indices in each station. Monte Carlo simulation was used to verify the regional significance of precipitation events and Copula function was used to determine the joint distribution and return period for every two of these indices. The results showed that: (1) CDD frequently ranged 80-100 days and exhibited a decreasing trend. The most frequent D25 values ranged 0-10 days, the number of days with extremely precipitation increased over time. Most P25 values ranged 0-1.5 mm, but values of 2-3 mm began to appear after 2000. I25 with the highest frequency ranged 0.1-0.3 mm, I25>0.4 mm was extremely rare; and (2) the spatial distribution of CDD in each 5-year period showed patterns contrasting those of D25 and P25. The joint return period for most values was short, and the occurrence probability of diverse types of extreme precipitation events is different.
[1] 普宗朝,张山清,李景林,等.近47年塔克拉玛干沙漠周边地区气候变化[J].中国沙漠,2010,30(2):413-421.
[2] 杨振常,李玉霖,崔夺,等.半干旱典型沙区1951-2005年主要气象要素及潜在蒸散量的变化趋势研究[J].中国沙漠,2012,32(5):1384-1392.
[3] 刘晓云,李栋梁,王劲松.1961-2009年中国区域干旱状况的时空变化特征[J].中国沙漠,2012,32(2):473-483.
[4] 段春锋,缪启龙,曹雯.塔克拉玛干沙漠周边地区潜在蒸散时空演变特征及其主要影响因素[J].中国沙漠,2012,32(6):1723-1730.
[5] 王永东,李生宇,徐新文,等.Hargreaves公式在塔克拉玛干沙漠腹地的适用性[J].中国沙漠,2013,33(2):367-372.
[6] 刘晓阳,毛节泰,张帆,等.塔克拉玛干沙漠地区水汽分布特征[J].地球科学,2012,42(2):267-276.
[7] 魏娜,巩远发,孙娴,等.西北地区近50 a降水变化及水汽输送特征[J].中国沙漠,2010,30(6):1450-1457.
[8] 杨青,魏文寿,李军.塔克拉玛干沙漠及周边地区大气水汽量的时空变化[J].科学通报,2008,53:62-68.
[9] 赵洋,齐欣林,陈永乐,等.极端降雨事件对不同类型生物土壤结皮覆盖土壤碳释放的影响[J].中国沙漠,2013,33(2):543-548.
[10] Hirsch R M,Slack J R.Non-parametric trend test for seasonal data with serial dependence[J].Water Resource Research,1984,20(6):727-732.
[11] Livesey R E,Chen W Y.Statistical field significance and its determination by Monte-Carlo techniques[J].Monthly Weather Review,1983,111:46-59.
[12] Genest C,Rivest L P.Statistical inference procedures for bivariate Archimedean Copula[J].Journal of American Statistical Association,1993,88:1034-1043.
[13] Zhang L,Singh V P.Bivariate flood frequency analysis using the Copula Method[J].Journal of Hydrologic Engineering,2006,11(2):150-164.
[14] 张建华,曾建潮.经验分布函数概率模型的分布估算法[J].计算机工程与应用,2011,47(8):33-35.
[15] 谢中华.MATLAB统计分析与应用:40个案例分析[M].北京:北京航空航天大学出版社,2010:179-186.
[16] Akaike H.A new look at the statistical model identification[J].IEEE Transactions on Automatic Control,1974,19(6):716-723.
[17] Salvador G,Michele C.Frequency analysis via Copulas:theoretical aspects and applications to hydrological events[J].Water Resource Research,2004,40(12):1015-1027.
[18] Rasoul M,Ahmad F F,Yagob D.Bivariate drought frequency analysis using the copula method[J].Theoretical Applied Climatology,2012,108:191-206.
[19] Yue S,Rasmussen P.Bivariate frequency analysis:discussion of some useful concepts for hydrological application[J].Hydrological Processes,2002,16(14):811-819.
[20] 黄俊利,魏文寿,杨青,等.塔克拉玛干沙漠南缘极端降水变化趋势与突变[J].沙漠与绿洲气象,2012,6(2):30-34.
[21] 张强,李剑峰,陈晓宏,等.基于Copula函数的新疆极端降水概率时空变化特征[J].地理学报,2011,66(1):3-12.
[22] 李剑峰,张强,陈晓宏,等.新疆极端降水概率分布特征的时空演变规律[J].灾害学,2011,26(2):11-17.
甘公网安备 62010202000688号