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基于格网的青海省雪灾综合风险评估
引用本文:韩炳宏,吴让,周秉荣,肖林,牛得草,傅华.基于格网的青海省雪灾综合风险评估[J].干旱区研究,2017,34(5).
作者姓名:韩炳宏  吴让  周秉荣  肖林  牛得草  傅华
作者单位:1. 青海省海南州兴海县气象局,青海兴海813399;青海省防灾减灾重点实验室,青海西宁810001;2. 青海省防灾减灾重点实验室,青海西宁,810001;3. 中国科学院西北生态环境资源研究院,甘肃兰州,730000;4. 草地农业生态系统国家重点实验室兰州大学草地农业科技学院,甘肃兰州,730020
基金项目:国家重点研发计划,行业专项重大项目“青藏高原影响及下游灾害天气的诊断与预报(GYHY201406001)”资助
摘    要:为了快速、准确地评估青海省区域雪灾的监测精度和雪灾风险等级,基于气象灾害风险度评估的理论和方法,选取了诱发青海省雪灾发生的社会经济、畜牧和气象等致灾因子,通过Matlab软件编程和Arc GIS空间分析相结合的方法,较为系统地分析了青海省雪灾风险度因子的空间分布及雪灾综合风险等级特征。结果表明:除人均GDP和牲畜数量外,积雪深度、可利用草场面积和牧草产量的空间分布格局与雪灾综合风险基本一致;且雪灾综合风险高发区主要集中于青南牧区和东北局部地区,其中主要以玉树、杂多、称多、囊谦、唐古拉、玛沁、达日、甘德、门源和祁连局地为主,西北部柴达木盆地和东部农业区处于低等风险水平,而其余地区处于中等水平。

关 键 词:雪灾综合风险  风险度模型  风险评估  青海省

Grid-based Estimation of Snow Disaster Risk in Qinghai Province
HAN Bing-hong,WU Rang,ZHOU Bing-rong,XIAO Lin,NIU De-cao,FU Hua.Grid-based Estimation of Snow Disaster Risk in Qinghai Province[J].Arid Zone Research,2017,34(5).
Authors:HAN Bing-hong  WU Rang  ZHOU Bing-rong  XIAO Lin  NIU De-cao  FU Hua
Abstract:In order to access the monitoring precision of snow disasters and their synthetical risk levels in Qinghai Province quickly and accurately,in this paper the theory and methods of meteorological disaster risk estimation were used to analyze the spatial distribution of risk factors in Qinghai Province,and a classification of snow disaster risk was carried out by selecting the social economy,animal husbandry and meteorological formative factors using the Matlab software programming and ArcGIS spatial analysis.The results showed that in addition to the per capita GDP and livestock number,the spatial distribution pattern of snow cover depth,available grassland area and grass yield were almost similar to that of snow disaster risk.The areas with frequent occurrence and high risk of snow disasters were mainly distributed in the south and some northeast regions of Qinghai Province,and the snow disaster risk level in the Qaidam Basin and the eastern agricultural regions was low,and it was moderate in other regions.
Keywords:snow disaster risk  risk assessing model  risk estimation  Qinghai Province
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