Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period
Risk assessment plays an important role in disaster risk management. Existing multi-hazard risk assessment models are often qualitative or semi-quantitative in nature and used for comparative study of regional risk levels. They cannot estimate directly probability of disaster losses from the joint i...
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| Veröffentlicht in: | Stochastic environmental research and risk assessment 2015-01, Vol.29 (1), p.35-44 |
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| creator | Ming, Xiaodong Xu, Wei Li, Ying Du, Juan Liu, Baoyin Shi, Peijun |
| description | Risk assessment plays an important role in disaster risk management. Existing multi-hazard risk assessment models are often qualitative or semi-quantitative in nature and used for comparative study of regional risk levels. They cannot estimate directly probability of disaster losses from the joint impact of several hazards. In this paper, a quantitative approach of multi-hazard risk assessment based on vulnerability surface and joint return period of hazards is put forward to assess the risk of crop losses in the Yangtze River Delta region of China. The impact of strong wind and flood, the two most prominent agricultural hazards in the area, is analyzed. The multi-hazard risk assessment process consists of three steps. First, a vulnerability surface, which denotes the functional relationship between the intensity of the hazards and disaster losses, was built using the crop losses data for losses caused by strong wind and flood in the recent 30 years. Second, the joint probability distribution of strong wind and flood was established using the copula functions. Finally, risk curves that show the probability of crop losses in this multi-hazard context at four case study sites were calculated according to the joint return period of hazards and the vulnerability surface. The risk assessment result of crop losses provides a useful reference for governments and insurance companies to formulate agricultural development plans and analyze the market of agricultural insurance. The multi-hazard risk assessment method developed in this paper can also be used to quantitatively assess multi-hazard risk in other regions. |
| doi_str_mv | 10.1007/s00477-014-0935-y |
| format | Article |
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Existing multi-hazard risk assessment models are often qualitative or semi-quantitative in nature and used for comparative study of regional risk levels. They cannot estimate directly probability of disaster losses from the joint impact of several hazards. In this paper, a quantitative approach of multi-hazard risk assessment based on vulnerability surface and joint return period of hazards is put forward to assess the risk of crop losses in the Yangtze River Delta region of China. The impact of strong wind and flood, the two most prominent agricultural hazards in the area, is analyzed. The multi-hazard risk assessment process consists of three steps. First, a vulnerability surface, which denotes the functional relationship between the intensity of the hazards and disaster losses, was built using the crop losses data for losses caused by strong wind and flood in the recent 30 years. Second, the joint probability distribution of strong wind and flood was established using the copula functions. Finally, risk curves that show the probability of crop losses in this multi-hazard context at four case study sites were calculated according to the joint return period of hazards and the vulnerability surface. The risk assessment result of crop losses provides a useful reference for governments and insurance companies to formulate agricultural development plans and analyze the market of agricultural insurance. The multi-hazard risk assessment method developed in this paper can also be used to quantitatively assess multi-hazard risk in other regions.</description><identifier>ISSN: 1436-3240</identifier><identifier>EISSN: 1436-3259</identifier><identifier>DOI: 10.1007/s00477-014-0935-y</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>Agricultural development ; Aquatic Pollution ; business enterprises ; case studies ; Chemistry and Earth Sciences ; Comparative studies ; Computational Intelligence ; Computer Science ; crop losses ; Crops ; Disaster management ; Disaster risk ; Disasters ; Earth and Environmental Science ; Earth Sciences ; Emergency preparedness ; Environment ; Floods ; Hazards ; Insurance ; markets ; Math. Appl. in Environmental Science ; Mathematical models ; Original Paper ; Physics ; Probability distribution ; Probability Theory and Stochastic Processes ; Risk ; Risk assessment ; risk assessment process ; Risk management ; river deltas ; Statistics for Engineering ; Surface chemistry ; Waste Water Technology ; Water Management ; Water Pollution Control ; Wind ; wind speed</subject><ispartof>Stochastic environmental research and risk assessment, 2015-01, Vol.29 (1), p.35-44</ispartof><rights>Springer-Verlag Berlin Heidelberg 2014</rights><rights>Springer-Verlag Berlin Heidelberg 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-a7ab5a43c978a0b2989d6242b6ce7de8e8d941c2529bda97a0e15a8be0f1bff03</citedby><cites>FETCH-LOGICAL-c476t-a7ab5a43c978a0b2989d6242b6ce7de8e8d941c2529bda97a0e15a8be0f1bff03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00477-014-0935-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00477-014-0935-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Ming, Xiaodong</creatorcontrib><creatorcontrib>Xu, Wei</creatorcontrib><creatorcontrib>Li, Ying</creatorcontrib><creatorcontrib>Du, Juan</creatorcontrib><creatorcontrib>Liu, Baoyin</creatorcontrib><creatorcontrib>Shi, Peijun</creatorcontrib><title>Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period</title><title>Stochastic environmental research and risk assessment</title><addtitle>Stoch Environ Res Risk Assess</addtitle><description>Risk assessment plays an important role in disaster risk management. Existing multi-hazard risk assessment models are often qualitative or semi-quantitative in nature and used for comparative study of regional risk levels. They cannot estimate directly probability of disaster losses from the joint impact of several hazards. In this paper, a quantitative approach of multi-hazard risk assessment based on vulnerability surface and joint return period of hazards is put forward to assess the risk of crop losses in the Yangtze River Delta region of China. The impact of strong wind and flood, the two most prominent agricultural hazards in the area, is analyzed. The multi-hazard risk assessment process consists of three steps. First, a vulnerability surface, which denotes the functional relationship between the intensity of the hazards and disaster losses, was built using the crop losses data for losses caused by strong wind and flood in the recent 30 years. Second, the joint probability distribution of strong wind and flood was established using the copula functions. Finally, risk curves that show the probability of crop losses in this multi-hazard context at four case study sites were calculated according to the joint return period of hazards and the vulnerability surface. The risk assessment result of crop losses provides a useful reference for governments and insurance companies to formulate agricultural development plans and analyze the market of agricultural insurance. The multi-hazard risk assessment method developed in this paper can also be used to quantitatively assess multi-hazard risk in other regions.</description><subject>Agricultural development</subject><subject>Aquatic Pollution</subject><subject>business enterprises</subject><subject>case studies</subject><subject>Chemistry and Earth Sciences</subject><subject>Comparative studies</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>crop losses</subject><subject>Crops</subject><subject>Disaster management</subject><subject>Disaster risk</subject><subject>Disasters</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Emergency preparedness</subject><subject>Environment</subject><subject>Floods</subject><subject>Hazards</subject><subject>Insurance</subject><subject>markets</subject><subject>Math. Appl. in Environmental Science</subject><subject>Mathematical models</subject><subject>Original Paper</subject><subject>Physics</subject><subject>Probability distribution</subject><subject>Probability Theory and Stochastic Processes</subject><subject>Risk</subject><subject>Risk assessment</subject><subject>risk assessment process</subject><subject>Risk management</subject><subject>river deltas</subject><subject>Statistics for Engineering</subject><subject>Surface chemistry</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Wind</subject><subject>wind speed</subject><issn>1436-3240</issn><issn>1436-3259</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqNkU1r1kAQgINYsNT-gJ5c8OIlOrMf2exRilahINL2vEySSbs1b_K6u6m8_nq3pIh4EE8zh-cZGJ6qOkN4iwD2XQLQ1taAuganTH14Vh2jVk2tpHHPf-8aXlSnKYWuOEY5h3Bc9V9XmnPIlMMDi9065VDf0U-Kg4ghfROUEqe04zmLHyHfiYd1mjlSF6aQDyKtcaSeBc2DeLLul1DYyHmNs9hzDMvwsjoaaUp8-jRPqpuPH67PP9WXXy4-n7-_rHttm1yTpc6QVr2zLUEnXeuGRmrZNT3bgVtuB6exl0a6biBnCRgNtR3DiN04gjqp3mx393H5vnLKfhdSz9NEMy9r8tgYVA4lmP9AtWykUWgL-vov9H4pv5VHCiVNi6aFplC4UX1cUoo8-n0MO4oHj-AfI_ktki-R_GMkfyiO3JxU2PmW4x-X_yG92qSRFk-3JZK_uZKABgAdto1UvwBxE5-U</recordid><startdate>20150101</startdate><enddate>20150101</enddate><creator>Ming, Xiaodong</creator><creator>Xu, Wei</creator><creator>Li, Ying</creator><creator>Du, Juan</creator><creator>Liu, Baoyin</creator><creator>Shi, Peijun</creator><general>Springer-Verlag</general><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>FBQ</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7XB</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0W</scope><scope>SOI</scope><scope>7T2</scope><scope>7U1</scope><scope>7U2</scope><scope>7SR</scope><scope>7SU</scope><scope>7TA</scope><scope>JG9</scope></search><sort><creationdate>20150101</creationdate><title>Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period</title><author>Ming, Xiaodong ; Xu, Wei ; Li, Ying ; Du, Juan ; Liu, Baoyin ; Shi, Peijun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-a7ab5a43c978a0b2989d6242b6ce7de8e8d941c2529bda97a0e15a8be0f1bff03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Agricultural development</topic><topic>Aquatic Pollution</topic><topic>business enterprises</topic><topic>case studies</topic><topic>Chemistry and Earth Sciences</topic><topic>Comparative studies</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>crop losses</topic><topic>Crops</topic><topic>Disaster management</topic><topic>Disaster risk</topic><topic>Disasters</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Emergency preparedness</topic><topic>Environment</topic><topic>Floods</topic><topic>Hazards</topic><topic>Insurance</topic><topic>markets</topic><topic>Math. Appl. in Environmental Science</topic><topic>Mathematical models</topic><topic>Original Paper</topic><topic>Physics</topic><topic>Probability distribution</topic><topic>Probability Theory and Stochastic Processes</topic><topic>Risk</topic><topic>Risk assessment</topic><topic>risk assessment process</topic><topic>Risk management</topic><topic>river deltas</topic><topic>Statistics for Engineering</topic><topic>Surface chemistry</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><topic>Wind</topic><topic>wind speed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ming, Xiaodong</creatorcontrib><creatorcontrib>Xu, Wei</creatorcontrib><creatorcontrib>Li, Ying</creatorcontrib><creatorcontrib>Du, Juan</creatorcontrib><creatorcontrib>Liu, Baoyin</creatorcontrib><creatorcontrib>Shi, Peijun</creatorcontrib><collection>AGRIS</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Science Journals</collection><collection>ProQuest Engineering Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><collection>Environment Abstracts</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Engineered Materials Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Materials Business File</collection><collection>Materials Research Database</collection><jtitle>Stochastic environmental research and risk assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ming, Xiaodong</au><au>Xu, Wei</au><au>Li, Ying</au><au>Du, Juan</au><au>Liu, Baoyin</au><au>Shi, Peijun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period</atitle><jtitle>Stochastic environmental research and risk assessment</jtitle><stitle>Stoch Environ Res Risk Assess</stitle><date>2015-01-01</date><risdate>2015</risdate><volume>29</volume><issue>1</issue><spage>35</spage><epage>44</epage><pages>35-44</pages><issn>1436-3240</issn><eissn>1436-3259</eissn><abstract>Risk assessment plays an important role in disaster risk management. Existing multi-hazard risk assessment models are often qualitative or semi-quantitative in nature and used for comparative study of regional risk levels. They cannot estimate directly probability of disaster losses from the joint impact of several hazards. In this paper, a quantitative approach of multi-hazard risk assessment based on vulnerability surface and joint return period of hazards is put forward to assess the risk of crop losses in the Yangtze River Delta region of China. The impact of strong wind and flood, the two most prominent agricultural hazards in the area, is analyzed. The multi-hazard risk assessment process consists of three steps. First, a vulnerability surface, which denotes the functional relationship between the intensity of the hazards and disaster losses, was built using the crop losses data for losses caused by strong wind and flood in the recent 30 years. Second, the joint probability distribution of strong wind and flood was established using the copula functions. Finally, risk curves that show the probability of crop losses in this multi-hazard context at four case study sites were calculated according to the joint return period of hazards and the vulnerability surface. The risk assessment result of crop losses provides a useful reference for governments and insurance companies to formulate agricultural development plans and analyze the market of agricultural insurance. The multi-hazard risk assessment method developed in this paper can also be used to quantitatively assess multi-hazard risk in other regions.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><doi>10.1007/s00477-014-0935-y</doi><tpages>10</tpages></addata></record> |
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| subjects | Agricultural development Aquatic Pollution business enterprises case studies Chemistry and Earth Sciences Comparative studies Computational Intelligence Computer Science crop losses Crops Disaster management Disaster risk Disasters Earth and Environmental Science Earth Sciences Emergency preparedness Environment Floods Hazards Insurance markets Math. Appl. in Environmental Science Mathematical models Original Paper Physics Probability distribution Probability Theory and Stochastic Processes Risk Risk assessment risk assessment process Risk management river deltas Statistics for Engineering Surface chemistry Waste Water Technology Water Management Water Pollution Control Wind wind speed |
| title | Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period |
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