基于IOSM的苏州市自然灾害损失风险分析
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摘要
基于灾害损失历史统计数据,借助于内外集模型(IOSM)进行自然灾害风险分析。首先确定观测区间并检验观测样本的分布形式,基于此确定其可能性-概率分布矩阵PPD,然后计算灾害损失的风险期望值,并基于ArcGIS制作不同灾害情境下各灾害损失指标的风险分布图。针对苏州市主要自然灾害类型,选取受灾人口、倒塌房屋、农作物受灾面积和直接经济损失作为灾害损失指标进行灾害风险分析,得到以下主要结论:1)不同灾害类型下各灾害损失指标的风险水平和复现期不同,其中台风导致200万人口、12 500hm2农作物受灾和42.5万间房屋倒塌的复现期均为7.2~7.5a。2)不同类型灾害的损失特征和程度不同,台风对人口、农作物和房屋的影响最大;洪涝灾害的直接经济损失最大,说明洪涝灾害后果最严重;风雹对各项指标的影响都比较小,与其影响区域小、持续时间短等局部灾害特征有关。3)各类型灾害损失风险呈现显著的空间差异特征,对台风导致的灾害损失风险分析表明,吴中区、吴江市、常熟市和张家港市的灾害风险最大;常熟市和吴中区台风导致受灾人口和农作物减产的风险最大,市区周围台风导致受灾人口风险最小;常熟市台风导致房屋倒塌的风险最大。4)灾害发生概率及其可能性共同决定灾害损失风险。研究成果对民政部门制订、调整、规划救灾对策以及民生保险预算具有重要意义。
Based on statistical data of disaster losses,the natural disaster risk was analyzed with the help of inner and outer set model(i.e.IOSM)in this paper.Observation intervals of samples were firstly determined.The distribution form was then tested.On this basis,possibility probability distribution matrix(i.e.PPD)was calculated.And the disaster loss risk expectation was determined.Finally,the risk distribution map of each disaster loss index was produced by ArcGIS tools under different disaster situations.According to the main types of natural disasters in Suzhou City,affected population,collapse of housing,damaged crops area and direct economic losses were selected as the evaluating index of disaster loss risk.Following main conclusions were drawn:1)Each loss index caused from different types of disaster has its divergent risk level and repetition period.The repetition period of typhoon,which can affect two million population,damage 12 500 hectares of crops,or cause 425 000 houses collapsed,is almost as same as 7years;2)Loss characteristics and degree of different types of disasters is diverse.Population,crops and houses were most affected by typhoon than by other types of disaster.Flood disaster has the most serious consequence because of its largest direct economic loss among all kinds of disaster.Relatively low loss indicators of hail are related of its characteristics such as small influence area and short duration.3)Loss risk of each type shows significantly spatial difference.The disaster risk analysis showed that Wuzhong district,Wujiang district,Changshu City and Kunshan City have the highest risk of typhoon disaster.Population and the crop has the highest typhoon disaster risk in Changshu City and Wuzhong District,followed by Zhangjiagang and Wujiang City,Taicang City and Kunshan City.People and crops around urban area has the minimum risk.Houses suffer the highest collapsed risk from typhoon in Changshu City.4)The disaster occurrence probability and its possibility jointly decides the disaster loss risk.The research results have important meanings for disaster relief measures,planning adjustment and livelihood insurance budget of civil affairs.
Based on statistical data of disaster losses,the natural disaster risk was analyzed with the help of inner and outer set model(i.e.IOSM)in this paper.Observation intervals of samples were firstly determined.The distribution form was then tested.On this basis,possibility probability distribution matrix(i.e.PPD)was calculated.And the disaster loss risk expectation was determined.Finally,the risk distribution map of each disaster loss index was produced by ArcGIS tools under different disaster situations.According to the main types of natural disasters in Suzhou City,affected population,collapse of housing,damaged crops area and direct economic losses were selected as the evaluating index of disaster loss risk.Following main conclusions were drawn:1)Each loss index caused from different types of disaster has its divergent risk level and repetition period.The repetition period of typhoon,which can affect two million population,damage 12 500 hectares of crops,or cause 425 000 houses collapsed,is almost as same as 7years;2)Loss characteristics and degree of different types of disasters is diverse.Population,crops and houses were most affected by typhoon than by other types of disaster.Flood disaster has the most serious consequence because of its largest direct economic loss among all kinds of disaster.Relatively low loss indicators of hail are related of its characteristics such as small influence area and short duration.3)Loss risk of each type shows significantly spatial difference.The disaster risk analysis showed that Wuzhong district,Wujiang district,Changshu City and Kunshan City have the highest risk of typhoon disaster.Population and the crop has the highest typhoon disaster risk in Changshu City and Wuzhong District,followed by Zhangjiagang and Wujiang City,Taicang City and Kunshan City.People and crops around urban area has the minimum risk.Houses suffer the highest collapsed risk from typhoon in Changshu City.4)The disaster occurrence probability and its possibility jointly decides the disaster loss risk.The research results have important meanings for disaster relief measures,planning adjustment and livelihood insurance budget of civil affairs.
引文
[1]HENSTRA D.Evaluating local government emergency management programs:What framework should public managers adopt[J].Public Administration Review,2010(2):236-246.
[2]LICHTER M,FELSENSTEIN D.Assessing the costs of sealevel rise and extreme flooding at the local level:A GIS-based approach[J].Ocean&Coastal Management,2012,59:47-62.
[3]MONTOYAA L,MASSERB I.Management of natural hazard risk in Cartago,Costa Rica[J].Habitat International,2005,29:493-509.
[4]HOLAND I S,LUJALA P I,R D J K.Social vulnerability assessment for Norway:A quantitative approach[J].Norsk Geografisk Tidsskrift_Norwegian Journal of Geography,2011,65:1-17.
[5]赵思健,黄崇福,郭树军.情景驱动的区域自然灾害风险分析[J].自然灾害学报,2012,21(1):9-13.
[6]燕群,蒙吉军,康玉芳.基于防灾规划的城市自然灾害风险分析与评估研究进展[J].地理与地理信息科学,2011,27(6):78-82.
[7]王文圣,金菊良,李跃清.基于集对分析的自然灾害风险度综合评价研究[J].四川大学学报(工程科学版),2009,41(6):6-12.
[8]黄崇福.内集-外集模型的计算机仿真检验[J].自然灾害学报,2002,11(3):62-70.
[9]陈志芬,黄崇福,张俊香.基于扩散函数的内集-外集模型[J].模糊系统与数学,2006,20(1):42-48.
[10]ZOU Q,ZHOU J Z,ZHOU C,et al.Fuzzy risk analysis of flood disasters based on diffused-interior-outer-set model[J].Expert Systems with Applications,2012,39:6213-6220.
[2]LICHTER M,FELSENSTEIN D.Assessing the costs of sealevel rise and extreme flooding at the local level:A GIS-based approach[J].Ocean&Coastal Management,2012,59:47-62.
[3]MONTOYAA L,MASSERB I.Management of natural hazard risk in Cartago,Costa Rica[J].Habitat International,2005,29:493-509.
[4]HOLAND I S,LUJALA P I,R D J K.Social vulnerability assessment for Norway:A quantitative approach[J].Norsk Geografisk Tidsskrift_Norwegian Journal of Geography,2011,65:1-17.
[5]赵思健,黄崇福,郭树军.情景驱动的区域自然灾害风险分析[J].自然灾害学报,2012,21(1):9-13.
[6]燕群,蒙吉军,康玉芳.基于防灾规划的城市自然灾害风险分析与评估研究进展[J].地理与地理信息科学,2011,27(6):78-82.
[7]王文圣,金菊良,李跃清.基于集对分析的自然灾害风险度综合评价研究[J].四川大学学报(工程科学版),2009,41(6):6-12.
[8]黄崇福.内集-外集模型的计算机仿真检验[J].自然灾害学报,2002,11(3):62-70.
[9]陈志芬,黄崇福,张俊香.基于扩散函数的内集-外集模型[J].模糊系统与数学,2006,20(1):42-48.
[10]ZOU Q,ZHOU J Z,ZHOU C,et al.Fuzzy risk analysis of flood disasters based on diffused-interior-outer-set model[J].Expert Systems with Applications,2012,39:6213-6220.