四川省干旱灾害风险分析与区划

Risk Analysis and Regionalization of Drought Disaster in Sichuan Province

摘要: 作为中国西南大省，四川省长期受到干旱灾害的影响。本文选用四川省37个气象站1977~2017年的逐日降水数据，四川省21市州的社会经济和地理信息数据，基于自然灾害风险理论，从致灾因子危险性、成灾环境敏感性、承灾体易损性和防灾减灾能力四个方面分别选取了10个评价指标，通过计算标准化降水指数SPI，利用层次分析和加权综合评价法，以ArcGIS 10.2为工具构建了四川省干旱灾害风险评估模型，并对四川省干旱灾害风险进行综合分析和区划。结果表明：四川中部及东部地区的成都、德阳、遂宁、资阳、内江、眉山、自贡、宜宾、广安、绵阳、乐山、南充市的旱灾风险偏高；四川西部地区、东南部的泸州和东北部的广元、巴中市旱灾风险偏低，多为低和较低风险区。构建的模型总体上可以反映四川省旱灾综合风险的分布情况，有利于制定和完善防灾减灾措施，从而主动规避旱灾风险，减少旱灾损失。

Abstract: As a major province in southwest China, Sichuan Province has been affected by drought for a long time. Based on the theory of natural disaster risk, this paper selected 10 evaluation indicators from four aspects: hazard of disaster-causing factors, environmental sensitivity, vulnerability of disaster-bearing bodies and disaster prevention and mitigation ability, by using the daily precipitation data of 37 meteorological stations in Sichuan Province from 1977 to 2017, socio-economic data and geographic information data of 21 cities and states in Sichuan Province, and by calculating the standardized precipitation index (SPI), applying the analytic hierarchy process and weighted comprehensive evaluation method, a risk assessment model for drought disaster in Sichuan Province was constructed based on ArcGIS software, and comprehensive analysis and regionalization were completed for drought disaster risk. The results show that the risk of drought disaster is relatively high in central and eastern Sichuan Province including Chengdu, Deyang, Suining, Ziyang, Neijiang, Meishan, Zigong, Yibin, Guang’an, Mianyang, Leshan and Nanchong city; the risk of drought disaster is relatively low in western Sichuan, Luzhou city in southeastern Sichuan and Guangyuan and Bazhong city in northeastern Sichuan. The model can generally reflect the distribution of comprehensive drought risk in Sichuan Province, and is conducive to formulating and improving disaster pre-vention and mitigation measures, so as to actively avoid the risk of drought and reduce drought losses