刘向培, 佟晓辉, 贾庆宇, 辛卓航, 杨剑刃. 1960—2017年中国降水集中程度特征分析[J]. 水科学进展, 2021, 32(1): 10-19. DOI: 10.14042/j.cnki.32.1309.2021.01.002
引用本文: 刘向培, 佟晓辉, 贾庆宇, 辛卓航, 杨剑刃. 1960—2017年中国降水集中程度特征分析[J]. 水科学进展, 2021, 32(1): 10-19. DOI: 10.14042/j.cnki.32.1309.2021.01.002
LIU Xiangpei, TONG Xiaohui, JIA Qingyu, XIN Zhuohang, YANG Jianren. Precipitation concentration characteristics in China during 1960—2017[J]. Advances in Water Science, 2021, 32(1): 10-19. DOI: 10.14042/j.cnki.32.1309.2021.01.002
Citation: LIU Xiangpei, TONG Xiaohui, JIA Qingyu, XIN Zhuohang, YANG Jianren. Precipitation concentration characteristics in China during 1960—2017[J]. Advances in Water Science, 2021, 32(1): 10-19. DOI: 10.14042/j.cnki.32.1309.2021.01.002

1960—2017年中国降水集中程度特征分析

Precipitation concentration characteristics in China during 1960—2017

  • 摘要: 降水集中程度是反映降水结构的重要指标。基于1960—2017年中国773个气象站日降水资料,运用降水集中程度(Q),研究中国降水集中程度的时空特征,分析其与降水量、海洋状态之间的关系。结果表明:①中国年平均Q值为0.38,南北较低中间较高;冬季和秋季的降水集中程度相对较高,平均Q值分别为0.53和0.51,夏季和春季相对较低,分别为0.39和0.48。②年降水集中程度变化趋势较小,总体上略有上升,东南升高西北降低;在年和季尺度,Q和降水量均表现出较强的负相关性,年尺度相关系数为-0.71,秋季的相关性最强,相关系数为-0.89,春季的相关性最弱,相关系数为-0.70,降水集中程度和降水量共同影响水旱灾害受灾面积。③Q与NINO3.4指数间的相关性随着滞后时间的延长先增大后减小,当滞后时间为2个月时相关系数最大,平均为0.13,由北向南总体呈"-+-"分布;与PDO指数间的相关系数随着滞后时间的延长先减小后增大再减小,当滞后时间为4个月时相关系数最大,平均为0.12,以负相关为主。

     

    Abstract: Precipitation concentration is an important component of precipitation structure. The present study uses daily precipitation data from 1960—2017 to calculate the precipitation concentration index Q and investigate in detail the spatial and temporal characteristics of precipitation concentration in China. In particular, this analysis considers how precipitation concentration is related to both precipitation amount and maritime conditions. The results indicate that China's yearly mean Q value is 0.38, with Q values highest in central China and lower in northern and southern China. Precipitation concentration is higher in winter and autumn, with mean Q values of 0.53 and 0.51, respectively; in contrast, the mean Q values for summer and spring are 0.39 and 0.48, respectively. For China as a whole, the yearly mean Q value is increasing slowly with time; however, yearly mean Q trends exhibit regional variation, with positive and negative temporal trends in southeastern and northwestern China, respectively. The results indicate a negative correlation between precipitation concentration and precipitation amount at both annual and seasonal scales (correlation coefficient of-0.71 at the annual scale). This correlation is strongest in autumn and weakest in winter (correlation coefficients of -0.89 and-0.70, respectively). Moreover, both precipitation concentration and precipitation amount control the area affected by flood and drought. The correlation coefficient between yearly Q and the NINO3.4 index increases and then decreases with increasing lag time, with the strongest correlation (coefficient:0.13) found for a lag time of 2 months. This correlation also exhibits a distinct spatial pattern, with a "-+-" distribution from north to south. Similarly, the correlation coefficient between yearly Q and the Pacific Decadal Oscillation increases and then decreases with increasing lag time; this correlation is strongest (coefficient:0.12) for a lag time of 4 months. This relationship exhibits a negative correlation across most of China.

     

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