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基于多重现期的京津冀小时极端降雨特征分析及致灾因子危险性评估
引用本文:王晓雅,蒋卫国,邓越,蒋梓杰.基于多重现期的京津冀小时极端降雨特征分析及致灾因子危险性评估[J].地理研究,2020,39(11):2581-2592.
作者姓名:王晓雅  蒋卫国  邓越  蒋梓杰
作者单位:1.北京师范大学地理科学学部 环境遥感与数字城市北京市重点实验室,北京 1008752.北京师范大学地理科学学部 环境演变与自然灾害教育部重点实验室,北京 100875
基金项目:国家重点研发计划专项项目(2017YFB0504102);国家重点研发计划专项项目(2017YFB0504105);国家重点研发计划专项项目(2016YFC0503002);国家自然科学基金(41571077);水资源安全北京实验室项目
摘    要:为了探究京津冀小时极端降雨特征,本文基于地区线性矩法计算多种重现期下的小时极端降雨值,并分析小时极端降雨的特征与变化趋势,同时探究小时极端降雨致灾因子危险性并得到以下结果。① 研究得到适合计算京津冀小时极端降雨频率估计值的分布线型及不同重现期的频率估计值;② 京津冀大于50年一遇的小时极端降雨的强度虽高(>70 mm/h)但其出现的次数(1~2次)相对较少,且多数站点的频次与强度无显著变化趋势;③ 小时极端降雨致灾因子危险性较高的区域位于北京、保定、廊坊交界地带及唐山、衡水、邢台部分地区。本研究在考虑空间差异的基础上探究小时极端降雨并分析其特征和致灾因子危险性,对于京津冀极端降雨灾害的管理与风险规避有一定的意义。

关 键 词:地区线性矩  小时极端降雨  强度与频次  致灾因子危险性  
收稿时间:2019-09-10
修稿时间:2020-03-05

Characteristic analysis and fatalness of disaster-inducing factors assessment of hourly extreme rainfall in different return periods of Beijing-Tianjin-Hebei region
WANG Xiaoya,JIANG Weiguo,DENG Yue,JIANG Zijie.Characteristic analysis and fatalness of disaster-inducing factors assessment of hourly extreme rainfall in different return periods of Beijing-Tianjin-Hebei region[J].Geographical Research,2020,39(11):2581-2592.
Authors:WANG Xiaoya  JIANG Weiguo  DENG Yue  JIANG Zijie
Institution:1. Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China2. Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Abstract:The Beijing-Tianjin-Hebei region is one of the densely populated and economically developed areas in China. In the past 30 years, it has encountered several severe extreme rainfall events, especially for the short-term precipitation, which caused serious economic losses and casualties. In addition, regional differences have a great influence on the calculation of the return period. In order to explore the characteristics of hourly extreme rainfall in Beijing-Tianjin-Hebei region, this paper calculates the hourly extreme rainfall values under various return periods based on the regional L-moment method. The advantage of regional L-moment method is that it considers the spatial difference of hourly extreme rainfall, and select the most suitable frequency distribution for each district. The method contains four steps: data selection, identification of hydrological and meteorological consistent areas, selection of frequency distribution in consistent areas, and calculation of hourly rainfall frequency estimates. What's more, the characteristics and trends of hourly extreme rainfall were analyzed. At the same time, the fatalness of disaster-inducing factors of hourly extreme rainfall was investigated and the following results were obtained. (1) The study obtained the frequency value and the distribution suitable for calculating the hourly extreme rainfall frequency value in Beijing-Tianjin-Hebei region for different return periods. (2) The frequency of hourly extreme rainfall in the study region is concentrated in 20-40 times, and the intensity is concentrated in 40-50 mm/h. There is no significant change in most stations in terms of the frequency and intensity. Although the intensity of hourly extreme rainfall in the region for more than a 50-year return period is high (>70 mm/h), the frequency is relatively low. (3) The fatalness of disaster-inducing factors of hourly extreme rainfall is low in the northwest mountainous area and high in the southeast plain area. The junction of Beijing, Baoding and Langfang, and parts of Tangshan, Hengshui, and Xingtai are higher areas for fatalness of disaster-inducing factors leading to hourly extreme rainfall. On the basis of the spatial differences, this study explored hourly extreme rainfall values, analyzed their characteristics, and calculated the fatalness of disaster-inducing factors. It provided a certain basis for identifying high-risk areas of hourly extreme rainfall in the Beijing-Tianjin-Hebei region. It has certain significance for the management and risk prevention of extreme rainfall disasters in the study area.
Keywords:regional L-moment  hourly extreme rainfall  intensity and frequency  fatalness of disaster-inducing factor  
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