| 我国不同历时年最大降雨量的随机性及空间差异性 |
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| 引用本文： | 李鑫鑫,桑燕芳,谢平,刘昌明.我国不同历时年最大降雨量的随机性及空间差异性[J].地球信息科学,2018,20(8):1094-1101. |
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| 作者姓名： | 李鑫鑫 桑燕芳 谢平 刘昌明 |
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| 作者单位： | 1. 中国科学院地理科学与资源研究所 陆地水循环与地表过程重点实验室,北京 1001012. 中国科学院大学,北京 1014073. 武汉大学水资源与水电工程科学国家重点实验室,武汉 430072 |
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| 基金项目： | 国家重点研发计划（2017YFA0603702）;国家自然科学基金项目（91647110、41330529、91547205）;中国科学院地理科学与资源研究所“秉维”优秀青年人才计划项目;中国科学院青年创新促进会项目（2017074）;中国水利水电科学研究院全国山洪灾害调查评价项目(SHZH-IWHR-57) |
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| 摘 要： | 极端降水极易引发山洪和城市内涝等水灾害,给生态环境安全、社会经济发展、人民生命财产安全等带来极大损失,认识其（尤其是短历时）空间分布差异对洪涝灾害防治等具有重要意义。本文利用60 min、6 h和24 h共3种历时的年最大降雨量的统计特征参数,生成服从皮尔逊-Ⅲ型分布的长序列样本,并选用信息熵指标研究其随机性及空间分布差异。结果显示：各历时年最大降雨量的随机性均呈现由东南向西北逐渐减小的空间格局,但不同历时降水随机性的空间分布存在差异,主要体现在青藏高原东部、海河流域和淮河流域3个区域。此外,所求年最大降雨量信息熵值主要考虑了其取值的相对离散情况,故该信息熵值与整个序列绝对离散程度（即标准差）的关系不明显,而主要由序列均值处峰值高低的峰度系数决定,二者呈现明显的负相关关系;且由于峰度系数和变差系数的良好相关性也导致了变差系数与信息熵值之间呈现出良好关系。季风、台风、局地天气系统和人类活动等因素综合影响,决定了不同历时极端降水的空间分布格局及其差异。信息熵指标可以很好地反映中国各历时年最大降雨量随机性的空间分布格局,因而结果可为洪涝灾害防治、农业规划布局、生态环境规划保护等提供科学依据。
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| 关 键 词： | 短历时 极端降水 信息熵 随机性 空间格局 |
| 收稿时间： | 2017-07-10 |
Stochastic Characteristics of Annual Extreme Rainfall with Different Durations and Their Spatial Difference in China |
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| LI Xinxin,SANG Yanfang,XIE Ping,LIU Changming.Stochastic Characteristics of Annual Extreme Rainfall with Different Durations and Their Spatial Difference in China[J].Geo-information Science,2018,20(8):1094-1101. |
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| Authors: | LI Xinxin SANG Yanfang XIE Ping LIU Changming |
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| Institution: | 1. Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;2. University of Chinese Academy of Sciences, Beijing 101407, China3. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China |
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| Abstract: | Extreme rainfall events caused terrible mountain torrents, urban floods and other disasters, and brought great losses to the ecological environment, social and economic development, people's lives and properties. Investigating the spatial pattern and difference of the occurrence of extreme rainfalls, especially for short time-duration, is important for the floods prevention and control. Based on the annual maximum rainfall data at the time resolutions of 60 min, 6 h and 24 h, we generated the series data with 2000 samples following the Pearson-III probabilistic distribution, and then calculated the information entropy values by discrete information entropy theory. Investigation was focused on the spatial distribution and difference of stochastic characteristics of extreme rainfall with different time-durations. Results show that the entropy value of annual extreme rainfall in the southeastern region is greater than that in the northwestern region, indicating more obviously stochastic characteristics of extreme rainfall in the latter. However, the spatial distribution of stochastic characteristics of extreme rainfall, which is comprehensively determined by monsoon, typhoon, local weather system and human activities, vary with time resolutions, especially the entropy value in the Eastern Tibet, Haihe River Basin and the Huaihe River Basin express obvious difference. In addition, the entropy value mainly accounts for the relatively discrete degree of extreme rainfall value, rather than absolutely discrete degree, so the entropy value is determined by the kurtosis value of extreme rainfall and the two variables show a significant negative correlation. Moreover, the paper indicates that the evident relationship between the entropy value and coefficient of variation does not mean causality, which is resulted from the relationship of kurtosis and entropy value. It is concluded that the spatial distribution of entropy values of extreme rainfall in different time-durations can reflect the spatial distribution of its stochastic characteristics, thus the results can be a helpful scientific basis for the flood prevention and control, agriculture and economic-social developments. |
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| Keywords: | short time-duration extreme rainfall information entropy stochastic characteristics spatial pattern |
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