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中国电线积冰灾害危险性评估和区划 被引量:8

Assessment and zonation of wire icing disaster risk in China
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摘要 中国有关电线积冰方面的观测资料太少,难以直接用于灾害评估。为了解决这个问题,利用1960-2009年现有的电线积冰和常规气象资料,在分析观测冰重和常规气象指标相关性的基础上,逐步筛选出影响冰重的主要气象指标,构建了电线积冰灾害危险性评估的常规气象指标体系,再利用常规气象资料采用多元线性回归方法估算积冰量,对中国电线积冰灾害危险性进行了评估和区划。结果表明:(1)在综合考虑相关性和空间代表性的基础上,影响雨凇和混合凇冰重的主要气象指标为过程平均风速、积冰时间、过程累计降水量;影响雾凇的为过程平均风速、过程浓雾时间。(2)中国大部分地区电线积冰灾害危险性较低,中高危险区面积较小,分布特征表现出明显的南北差异。中高危险区呈带状分布于南方地区的长江中下游山地丘陵区,北方地区则呈点状分布,具体位置与山地丘陵、江河湖泊均有着密切的关系。 Wire icing data are too few to be directly used for the disaster assessment in China. To solve this prob- this study made use of the existing wire icing data and the conventional meteorological data during 1960-2008, analyzed the correlations between the observed ice weight and the conventional meteorological indices, and gradual- ly selected the main meteorological indices that influence ice weight. With these studies on the conventional meteor- ological data, a conventional meteorological index system of risk assessment for wire icing disaster was built, and the weight of wire icing was estimated by means of multiple linear regression analysis. Finally, wire icing risk was evaluated and zoned in China. Results show that the main meteorological indices influencing glaze and mixed-phase ice are average wind speed, icing time and accumulated precipitation during the ice-accretion process, while those influencing rime are average wind speed and fog time. The wire icing risk is low in the most parts of China. The proportions of the middle and high risk areas are small and their distribution varies greatly from north to south. The middle and high risk areas present a belt distribution in hilly ground of the middle and lower reaches of Yangtze River in south China, while in north China the areas present a spot distribution, and their specified locations are closely related to mountain and hill, rivers and lakes.
出处 《自然灾害学报》 CSCD 北大核心 2012年第3期110-118,共9页 Journal of Natural Disasters
基金 国家科技支撑计划课题(2008BAK50B02) 国家自然科学基金项目(40901297)
关键词 电线积冰 危险性 评估 中国 多元线性回归 wire icing risk estimate China multiple linear regression analysis
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