郑炎辉（1990-），男，广东揭阳人，博士研究生，主要从事水文与水资源方面的研究。 E-mail: zheng_yanhui@foxmail.com
P426.6
国家自然科学基金项目(51210013,51479216,51509127)；国家科技支撑计划(2012BAC21B0103)；水利部公益项目(201201094,201301002-02)；
ZHENG Yanhui
1. Center for Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China; 2 Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong Higher Education Institutes, SunYat-sen University, Guangzhou 510275, China; 3 School of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, ChinaCHEN Xiaohong
1. Center for Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China; 2 Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong Higher Education Institutes, SunYat-sen University, Guangzhou 510275, China; 3 School of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, ChinaHE Yanhu
1. Center for Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China; 2 Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong Higher Education Institutes, SunYat-sen University, Guangzhou 510275, China; 3 School of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, ChinaLAI Chengguang
1. Center for Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China; 2 Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong Higher Education Institutes, SunYat-sen University, Guangzhou 510275, China; 3 School of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, ChinaWANG Zhaoli
1. Center for Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China; 2 Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong Higher Education Institutes, SunYat-sen University, Guangzhou 510275, China; 3 School of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, China基于珠江流域内43个常规气象站点19602012年的逐日降水资料,计算了流域内各站点长期降水集中度（LCI）和逐年降水集中度（ACI）;采用Mann-Kendall趋势检验法和Sen’s坡度检验法检测ACI时间上的变化趋势;同时采用反距离权重插值法（IDW）对LCI的区域特征和ACI的变化趋势进行空间插值以分析降水集中度的时空分布规律;采用随机森林（RF）算法对降水集中度的影响因子进行重要度分析。结果表明:（1）珠江流域逐年降水集中度ACI的年际变化不明显,东南部呈上升趋势,西北部呈下降趋势;（2）珠江流域西北部长期降水集中度LCI值偏小,即降水分布较平均;东南部长期降水集中度LCI值偏大,即降水较集中,表明该地区降水极值情况发生的几率相对较高,该空间分布趋势可能是受距离海洋的远近及海拔的影响;（3）众多气候影响因子中,东亚夏季季风（EASMI）对珠江流域的降水集中度影响最明显。
Under the influence of the well-evidenced global warming, extreme precipitation events are likely to occur more and more frequently. As a result, the urban flood control and waterlogging prevention are under serious threat. The extreme precipitation events can be effectively identified by precipitation concentration index (CI), which is mainly used to describe the inhomogeneity of precipitation. This paper attempts to explore the spatiotemporal variations and the causes of precipitation concentration. Mann-Kendall statistical test and Sen’s slope method were used to detect the temporal patterns of ACI (annual precipitation concentration index), and inverse distance weighted (IDW) interpolation method was used to analyze the spatial change of LCI (long period precipitation concentration index) as well as the temporal variation trend of ACI based on daily precipitation at the 43 stations during 1960-2012 in the Pearl River Basin. Meanwhile, the random forest algorithm (RF) was applied to identify the contributions of the influencing factors. The results show that: (1) The northwest of Pearl River Basin indicates lower LCI while the southeast part indicates higher LCI, which shows that the extreme precipitation events will occur more frequently in the southeast. (2) The interannual variation of ACI in Pearl River Basin is not obvious, the northwest part shows decreasing trend while the southeast part shows increasing trend, the spatial distribution trend is likely to be affected by distance from ocean and altitude. (3) The importance analysis based on RF shows that the east Asian summer monsoon (EASMI) is the most significant factor of precipitation concentration among the 7 factors.
