| 降水集中度的变化特征及影响因素分析——以山西为例 |
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| 引用本文： | 袁瑞强,王亚楠,王鹏,王仕琴,陈宇宏.降水集中度的变化特征及影响因素分析——以山西为例[J].气候变化研究进展,2018,14(1):11-20. |
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| 作者姓名： | 袁瑞强 王亚楠 王鹏 王仕琴 陈宇宏 |
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| 作者单位： | 1.山西大学环境与资源学院,太原 0300062 江西师范大学鄱阳湖湿地与流域研究教育部重点实验室,南昌 3300223 中国科学院遗传所农业资源研究中心,石家庄 0500214 中国人民解放军军械工程学院,石家庄 050021 |
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| 基金项目： | 国家自然科学基金项目（41301033） |
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| 摘 要： | 基于山西14个站点1957—2014年逐日降水资料,计算年尺度与多年尺度下的降水集中指数（CI）和极端降水指数（R95p和R99p),利用统计方法,研究了山西降水集中度的时空变化和影响因素。结果表明,CI值与R99p显著正相关,CI值增大则发生极端强降水几率增大。多年尺度CI值体现出明显的纬度地带性和较大的局地空间差异。受温带大陆性季风气候控制,山西省降水CI值介于0.59~0.64,相对亚热带地区较小,且变化范围较小。总体上年尺度CI值呈下降趋势,五台山、右玉、五寨、运城等站点下降趋势显著。盆地区域较高山高原区的CI值更大且下降趋势不显著,更易于发生极端降水事件。较高的高程和较大的变幅可增强高程对CI值的影响。太平洋年代际振荡（PDO）与年尺度CI值显著负相关。PDO冷位相时期,西太平洋副高西进、偏强,CI值偏高,发生极端强降水的可能性增大。本研究揭示了自然地理条件和海-气相互作用对区域尺度降水集中度存在复杂的影响。
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| 关 键 词： | 降水集中指数（CI) 极端降水 时空分布 太平洋年代际振荡（PDO) 山西 |
| 收稿时间： | 2017-02-22 |
| 修稿时间： | 2017-04-13 |
An analysis of precipitation concentration variation characteristics and influential factors in Shanxi province,China |
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| Rui-Qiang YUAN,Ya-Nan WANG,Peng WANG,Shi-Qin WANG,Yu-Hong CHEN.An analysis of precipitation concentration variation characteristics and influential factors in Shanxi province,China[J].Advances in Climate Change,2018,14(1):11-20. |
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| Authors: | Rui-Qiang YUAN Ya-Nan WANG Peng WANG Shi-Qin WANG Yu-Hong CHEN |
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| Institution: | 1.School of Environment and Resource, Shanxi University, Taiyuan 030006, China2 Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China3 Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy Sciences, Shijiazhuang 050021, China4 Ordnance Engineering College of the Chinese People’s Liberation Army, Shijiazhuang 050021, China; |
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| Abstract: | Shanxi province is located on the border of monsoon in the north of China, which is across multiple latitudes. The mountains and basins in the study area are staggered. Therefore, the influential factors of precipitation in the study area are complicated. Based on the daily precipitation data of 14 rain gauging stations in Shanxi from 1957 to 2014, the precipitation concentration index (CI) and extreme precipitation index (R95p, R99p) were calculated at annual scale and multi-year scale. Mann-Kendall method was utilized to analyze the spatio-temporal variation of precipitation concentration, and its influential factors. The results showed that the positive correlation between R99p and CI at annual scale is significant, which means high precipitation concentration will increase the possibility of extreme precipitation. Precipitation concentration shows obvious differences in latitudinal zonality and local space at multi-year scale. Due to a temperate continental monsoon type, the precipitation concentration degree is relatively low and the change range is relatively narrow (0.59-0.64). In general, the concentration of precipitation is decreasing at inter annual scale, and the descending trend of Wutaishan, Youyu, Wuzhai and Yuncheng is significant at the 0.05 significance level. The possibility of extreme precipitation in the central basin area is greater than that in the plateau high-mountain area due to the higher CI value and insignificant declined trends detected. The correlation between multi-year scale precipitation CI and elevation, latitude, longitude, mean annual precipitation and mean annual rainy days is not significant. The higher elevation and larger amplitude make the influence of elevation on the concentration of precipitation at annual scale increase. The correlation between precipitation CI and NAO, ENSO air-sea interaction and EASMI is not significant. However, there is a significant negative correlation between annual CI and PDO. The possibility of extreme precipitation is greater when PDO is negative with the Western Pacific Subtropical High westward and stronger. The results demonstrated geographic conditions and air-sea interactions exert a complex influence on precipitation concentration at a regional scale. |
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| Keywords: | Precipitation Concentration Index (CI) Extreme precipitation Temporal and spatial distribution Pacific Decadal Oscillation (PDO) Shanxi province |
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