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GEPIC-V-R model: A GIS-based tool for regional crop drought risk assessment
•An extended GEPIC model (GEPIC-V-R) for crop drought risk assessment is developed.•Maize drought vulnerability and global maize drought risk are modeled by GEPIC-V-R.•High risk is present in South Africa, West and Central Europe, Southeast Asia, etc.•Risk pattern of China is low in southern regions...
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| Published in: | Agricultural water management 2014-10, Vol.144, p.107-119 |
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| Main Authors: | , , , , , |
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| cited_by | cdi_FETCH-LOGICAL-c399t-608941abcd75b91e58fb5ccee52440caf6e428ba903f6216ba23b3675719fdcf3 |
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| cites | cdi_FETCH-LOGICAL-c399t-608941abcd75b91e58fb5ccee52440caf6e428ba903f6216ba23b3675719fdcf3 |
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| container_start_page | 107 |
| container_title | Agricultural water management |
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| creator | Yin, Yuanyuan Zhang, Xingming Lin, Degen Yu, Han Wang, Jing’ai Shi, Peijun |
| description | •An extended GEPIC model (GEPIC-V-R) for crop drought risk assessment is developed.•Maize drought vulnerability and global maize drought risk are modeled by GEPIC-V-R.•High risk is present in South Africa, West and Central Europe, Southeast Asia, etc.•Risk pattern of China is low in southern regions and high in northern regions.•Coefficients between the model results and ‘real-world’ data are greater than 0.48.
In recent years, food losses caused by drought accounted for approximately 60% of the total world food loss, seriously threatening the world's food security and sustainable development. Against the background of frequent extreme climate events and “local warming and drying”, frequency and potential risks of global drought have tended to increase. As the scientific basis for disaster prevention and mitigation, disaster risk assessment has drawn widespread attention in the scientific community. Using the commonly used EPIC crop model, this study constructed a crop drought risk assessment model – GEPIC-V-R model – suitable for large regional scale, with functions to fit vulnerability curves and calculate risk. Additionally, global maize drought risk was assessed. From a global perspective, South Africa, Chile, Western and Central Europe, Russia and southeastern regions have elevated risks of maize drought; Chinese maize drought risk distribution is characterized by low risk in southern regions and high risk in northern regions. For once in 10- and 30-years, Pearson values between converted maize loss rate (CMLR) or Harikishan Jayanthi's loss rate and loss rate are greater than 0.7, with a S.D. of 0.01. Rank correlation analyses of 28 provinces in China and seven countries in Africa generated Pearson, Kendall and Spearman values greater than 0.48, with a S.D. of 0.05. There was a close correlation between the results and statistical predictions or existing results. Therefore, the simulation results supply the theoretical support for acting based on local conditions to manage drought and drought risk. |
| doi_str_mv | 10.1016/j.agwat.2014.05.017 |
| format | article |
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In recent years, food losses caused by drought accounted for approximately 60% of the total world food loss, seriously threatening the world's food security and sustainable development. Against the background of frequent extreme climate events and “local warming and drying”, frequency and potential risks of global drought have tended to increase. As the scientific basis for disaster prevention and mitigation, disaster risk assessment has drawn widespread attention in the scientific community. Using the commonly used EPIC crop model, this study constructed a crop drought risk assessment model – GEPIC-V-R model – suitable for large regional scale, with functions to fit vulnerability curves and calculate risk. Additionally, global maize drought risk was assessed. From a global perspective, South Africa, Chile, Western and Central Europe, Russia and southeastern regions have elevated risks of maize drought; Chinese maize drought risk distribution is characterized by low risk in southern regions and high risk in northern regions. For once in 10- and 30-years, Pearson values between converted maize loss rate (CMLR) or Harikishan Jayanthi's loss rate and loss rate are greater than 0.7, with a S.D. of 0.01. Rank correlation analyses of 28 provinces in China and seven countries in Africa generated Pearson, Kendall and Spearman values greater than 0.48, with a S.D. of 0.05. There was a close correlation between the results and statistical predictions or existing results. Therefore, the simulation results supply the theoretical support for acting based on local conditions to manage drought and drought risk.</description><identifier>ISSN: 0378-3774</identifier><identifier>EISSN: 1873-2283</identifier><identifier>DOI: 10.1016/j.agwat.2014.05.017</identifier><identifier>CODEN: AWMADF</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Agricultural and forest climatology and meteorology. Irrigation. Drainage ; Agronomy. Soil science and plant productions ; Biological and medical sciences ; Crops ; Disaster management ; Drought risk ; Droughts ; Foods ; Fundamental and applied biological sciences. Psychology ; General agronomy. Plant production ; Global ; Large scale risk assessment model (GEPIC-VR model) ; Maize ; Mathematical models ; Risk ; Risk assessment ; Vulnerability curves ; Zea mays</subject><ispartof>Agricultural water management, 2014-10, Vol.144, p.107-119</ispartof><rights>2014 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c399t-608941abcd75b91e58fb5ccee52440caf6e428ba903f6216ba23b3675719fdcf3</citedby><cites>FETCH-LOGICAL-c399t-608941abcd75b91e58fb5ccee52440caf6e428ba903f6216ba23b3675719fdcf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,786,790,27957,27958</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28663620$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Yin, Yuanyuan</creatorcontrib><creatorcontrib>Zhang, Xingming</creatorcontrib><creatorcontrib>Lin, Degen</creatorcontrib><creatorcontrib>Yu, Han</creatorcontrib><creatorcontrib>Wang, Jing’ai</creatorcontrib><creatorcontrib>Shi, Peijun</creatorcontrib><title>GEPIC-V-R model: A GIS-based tool for regional crop drought risk assessment</title><title>Agricultural water management</title><description>•An extended GEPIC model (GEPIC-V-R) for crop drought risk assessment is developed.•Maize drought vulnerability and global maize drought risk are modeled by GEPIC-V-R.•High risk is present in South Africa, West and Central Europe, Southeast Asia, etc.•Risk pattern of China is low in southern regions and high in northern regions.•Coefficients between the model results and ‘real-world’ data are greater than 0.48.
In recent years, food losses caused by drought accounted for approximately 60% of the total world food loss, seriously threatening the world's food security and sustainable development. Against the background of frequent extreme climate events and “local warming and drying”, frequency and potential risks of global drought have tended to increase. As the scientific basis for disaster prevention and mitigation, disaster risk assessment has drawn widespread attention in the scientific community. Using the commonly used EPIC crop model, this study constructed a crop drought risk assessment model – GEPIC-V-R model – suitable for large regional scale, with functions to fit vulnerability curves and calculate risk. Additionally, global maize drought risk was assessed. From a global perspective, South Africa, Chile, Western and Central Europe, Russia and southeastern regions have elevated risks of maize drought; Chinese maize drought risk distribution is characterized by low risk in southern regions and high risk in northern regions. For once in 10- and 30-years, Pearson values between converted maize loss rate (CMLR) or Harikishan Jayanthi's loss rate and loss rate are greater than 0.7, with a S.D. of 0.01. Rank correlation analyses of 28 provinces in China and seven countries in Africa generated Pearson, Kendall and Spearman values greater than 0.48, with a S.D. of 0.05. There was a close correlation between the results and statistical predictions or existing results. Therefore, the simulation results supply the theoretical support for acting based on local conditions to manage drought and drought risk.</description><subject>Agricultural and forest climatology and meteorology. Irrigation. Drainage</subject><subject>Agronomy. Soil science and plant productions</subject><subject>Biological and medical sciences</subject><subject>Crops</subject><subject>Disaster management</subject><subject>Drought risk</subject><subject>Droughts</subject><subject>Foods</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General agronomy. Plant production</subject><subject>Global</subject><subject>Large scale risk assessment model (GEPIC-VR model)</subject><subject>Maize</subject><subject>Mathematical models</subject><subject>Risk</subject><subject>Risk assessment</subject><subject>Vulnerability curves</subject><subject>Zea mays</subject><issn>0378-3774</issn><issn>1873-2283</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqNkEtvEzEUha2qSE0Lv4CNN5XYzHD9nqnEoopKiKgE4rW1PJ7r1OlkHOwJiH_PhFQsEau7-c45uh8hLxnUDJh-va3d5qebag5M1qBqYOaMLFhjRMV5I87JAoRpKmGMvCCXpWwBQII0C_J-dfdxvay-VZ_oLvU43NBbulp_rjpXsKdTSgMNKdOMm5hGN1Cf0572OR02DxPNsTxSVwqWssNxek6eBTcUfPF0r8jXt3dflu-q-w-r9fL2vvKibadKQ9NK5jrfG9W1DFUTOuU9ouJSgndBo-RN51oQQXOmO8dFJ7RRhrWh90FckVen3n1O3w9YJruLxeMwuBHToVimJedaKKP-A-Wm1WDkERUndH6xlIzB7nPcufzLMrBHy3Zr_1i2R8sWlJ0tz6nrpwFXvBtCdqOP5W-UN1oLzWHm3pw4nMX8iJht8RFHj33M6Cfbp_jPnd8hVpGe</recordid><startdate>20141001</startdate><enddate>20141001</enddate><creator>Yin, Yuanyuan</creator><creator>Zhang, Xingming</creator><creator>Lin, Degen</creator><creator>Yu, Han</creator><creator>Wang, Jing’ai</creator><creator>Shi, Peijun</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7SN</scope><scope>7ST</scope><scope>7TG</scope><scope>7U1</scope><scope>7U2</scope><scope>7U6</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>SOI</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20141001</creationdate><title>GEPIC-V-R model: A GIS-based tool for regional crop drought risk assessment</title><author>Yin, Yuanyuan ; Zhang, Xingming ; Lin, Degen ; Yu, Han ; Wang, Jing’ai ; Shi, Peijun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-608941abcd75b91e58fb5ccee52440caf6e428ba903f6216ba23b3675719fdcf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Agricultural and forest climatology and meteorology. Irrigation. Drainage</topic><topic>Agronomy. Soil science and plant productions</topic><topic>Biological and medical sciences</topic><topic>Crops</topic><topic>Disaster management</topic><topic>Drought risk</topic><topic>Droughts</topic><topic>Foods</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General agronomy. Plant production</topic><topic>Global</topic><topic>Large scale risk assessment model (GEPIC-VR model)</topic><topic>Maize</topic><topic>Mathematical models</topic><topic>Risk</topic><topic>Risk assessment</topic><topic>Vulnerability curves</topic><topic>Zea mays</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yin, Yuanyuan</creatorcontrib><creatorcontrib>Zhang, Xingming</creatorcontrib><creatorcontrib>Lin, Degen</creatorcontrib><creatorcontrib>Yu, Han</creatorcontrib><creatorcontrib>Wang, Jing’ai</creatorcontrib><creatorcontrib>Shi, Peijun</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Sustainability Science Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Agricultural water management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yin, Yuanyuan</au><au>Zhang, Xingming</au><au>Lin, Degen</au><au>Yu, Han</au><au>Wang, Jing’ai</au><au>Shi, Peijun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>GEPIC-V-R model: A GIS-based tool for regional crop drought risk assessment</atitle><jtitle>Agricultural water management</jtitle><date>2014-10-01</date><risdate>2014</risdate><volume>144</volume><spage>107</spage><epage>119</epage><pages>107-119</pages><issn>0378-3774</issn><eissn>1873-2283</eissn><coden>AWMADF</coden><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><abstract>•An extended GEPIC model (GEPIC-V-R) for crop drought risk assessment is developed.•Maize drought vulnerability and global maize drought risk are modeled by GEPIC-V-R.•High risk is present in South Africa, West and Central Europe, Southeast Asia, etc.•Risk pattern of China is low in southern regions and high in northern regions.•Coefficients between the model results and ‘real-world’ data are greater than 0.48.
In recent years, food losses caused by drought accounted for approximately 60% of the total world food loss, seriously threatening the world's food security and sustainable development. Against the background of frequent extreme climate events and “local warming and drying”, frequency and potential risks of global drought have tended to increase. As the scientific basis for disaster prevention and mitigation, disaster risk assessment has drawn widespread attention in the scientific community. Using the commonly used EPIC crop model, this study constructed a crop drought risk assessment model – GEPIC-V-R model – suitable for large regional scale, with functions to fit vulnerability curves and calculate risk. Additionally, global maize drought risk was assessed. From a global perspective, South Africa, Chile, Western and Central Europe, Russia and southeastern regions have elevated risks of maize drought; Chinese maize drought risk distribution is characterized by low risk in southern regions and high risk in northern regions. For once in 10- and 30-years, Pearson values between converted maize loss rate (CMLR) or Harikishan Jayanthi's loss rate and loss rate are greater than 0.7, with a S.D. of 0.01. Rank correlation analyses of 28 provinces in China and seven countries in Africa generated Pearson, Kendall and Spearman values greater than 0.48, with a S.D. of 0.05. There was a close correlation between the results and statistical predictions or existing results. Therefore, the simulation results supply the theoretical support for acting based on local conditions to manage drought and drought risk.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.agwat.2014.05.017</doi><tpages>13</tpages></addata></record> |
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| ispartof | Agricultural water management, 2014-10, Vol.144, p.107-119 |
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| subjects | Agricultural and forest climatology and meteorology. Irrigation. Drainage Agronomy. Soil science and plant productions Biological and medical sciences Crops Disaster management Drought risk Droughts Foods Fundamental and applied biological sciences. Psychology General agronomy. Plant production Global Large scale risk assessment model (GEPIC-VR model) Maize Mathematical models Risk Risk assessment Vulnerability curves Zea mays |
| title | GEPIC-V-R model: A GIS-based tool for regional crop drought risk assessment |
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