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Journal of Integrative Agriculture  2013, Vol. 12 Issue (12): 2310-2320    DOI: 10.1016/S2095-3119(13)60587-0
Agricultural Economics And Management Advanced Online Publication | Current Issue | Archive | Adv Search |
Assessment of Flood Catastrophe Risk for Grain Production at the Provincial Scale in China Based on the BMM Method
 XU Lei, ZHANG Qiao, ZHOU Ai-lian, HUO Ran
1.Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
2.Key Laboratory of Agri-Information Service Technology, Ministry of Agriculture, Beijing 100081, P.R.China
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摘要  Flood catastrophe risk assessment is imperative for the steady development of agriculture under the context of global climate change, and meanwhile, it is an urgent scientific issue need to be solved in agricultural risk assessment discipline. This paper developed the methodology of flood catastrophe risk assessment, which can be shown as the standard process of crop loss calculation, Monte Carlo simulation, the generalized extreme value distribution (GEV) fitting, and risk evaluation. Data on crop loss were collected based on hectares covered by natural disasters, hectares affected by natural disasters, and hectares destroyed by natural disasters using the standard equation. Monte Carlo simulation based on appropriate distribution was used to expand sample size to overcome the insufficiency of crop loss data. Block maxima model (BMM) approach based on the extreme value theory was for modeling the generalized extreme value distribution (GEV) of flood catastrophe loss, and then flood catastrophe risk at the provincial scale in China was calculated. The Type III Extreme distribution (Weibull) has a weighted advantage of modeling flood catastrophe risk for grain production. The impact of flood catastrophe to grain production in China was significantly serious, and high or very high risk of flood catastrophe mainly concentrates on the central and eastern regions of China. Given the scenario of suffering once-in-a-century flood disaster, for majority of the major-producing provinces, the probability of 10% reduction of grain output is more than 90%. Especially, the probabilities of more than 15% decline in grain production reach up to 99.99, 99.86, 99.69, and 91.60% respectively in Anhui, Jilin, Liaoning, and Heilongjiang. Flood catastrophe assessment can provide multifaceted information about flood catastrophe risk that can help to guide management of flood catastrophe.

Abstract  Flood catastrophe risk assessment is imperative for the steady development of agriculture under the context of global climate change, and meanwhile, it is an urgent scientific issue need to be solved in agricultural risk assessment discipline. This paper developed the methodology of flood catastrophe risk assessment, which can be shown as the standard process of crop loss calculation, Monte Carlo simulation, the generalized extreme value distribution (GEV) fitting, and risk evaluation. Data on crop loss were collected based on hectares covered by natural disasters, hectares affected by natural disasters, and hectares destroyed by natural disasters using the standard equation. Monte Carlo simulation based on appropriate distribution was used to expand sample size to overcome the insufficiency of crop loss data. Block maxima model (BMM) approach based on the extreme value theory was for modeling the generalized extreme value distribution (GEV) of flood catastrophe loss, and then flood catastrophe risk at the provincial scale in China was calculated. The Type III Extreme distribution (Weibull) has a weighted advantage of modeling flood catastrophe risk for grain production. The impact of flood catastrophe to grain production in China was significantly serious, and high or very high risk of flood catastrophe mainly concentrates on the central and eastern regions of China. Given the scenario of suffering once-in-a-century flood disaster, for majority of the major-producing provinces, the probability of 10% reduction of grain output is more than 90%. Especially, the probabilities of more than 15% decline in grain production reach up to 99.99, 99.86, 99.69, and 91.60% respectively in Anhui, Jilin, Liaoning, and Heilongjiang. Flood catastrophe assessment can provide multifaceted information about flood catastrophe risk that can help to guide management of flood catastrophe.
Keywords:  flood catastrophe       risk assessment       block maxima model (BMM)       provincial scale       China  
Received: 17 December 2012   Accepted:
Fund: 

This work was jointly funded by the National Natural Science Foundation of China (41201551) and the Key Technology R&D Program of China (2012BAH20B04-2).

Corresponding Authors:  ZHANG Qiao, Tel: +86-10-82109883, Fax: +86-10-82106261, E-mail: zhangqiao@caas.cn     E-mail:  zhangqiao@caas.cn
About author:  XU Lei, Tel: +86-10-82105209, Fax: +86-10-82106261, E-mail: xuleicaas@hotmail.com

Cite this article: 

XU Lei, ZHANG Qiao, ZHOU Ai-lian, HUO Ran. 2013. Assessment of Flood Catastrophe Risk for Grain Production at the Provincial Scale in China Based on the BMM Method. Journal of Integrative Agriculture, 12(12): 2310-2320.

[1]Balica S F, Wright N G. 2010. Reducing the complexity of the flood vulnerability index. Environmental Hazards, 9, 321-339

[2]Botts R R, Boles J N. 1958. Use of normal-curve theory in crop insurance rate making. Journal of Farm Economics, 40, 733-740

[3]Huang C F, Ruan D. 2008. Fuzzy risks and updating algorithm with new observations. Risk Analysis, 28, 681- 693.

[4]Castillo E. 1988. Extreme Value Theory in Engineering. Academic Press, London, UK.

[5]Chen S, Miranda M. 2004. Modeling multivariate crop yield densities with frequent extreme values. In: Paper Presented at the American Agricultural Economics Association Annual Meeting. Denver, Colorado, USA. pp. 1-4

[6]Coles S. 2001. An Introduction to Statistical Modeling of Extreme Values. Springer-Verlag, London.

[7]Huang D, Zhang R, Huo Z, Mao F, Youhao E, Zheng W. 2012. An assessment of multidimensional flood vulnerability at the provincial scale in China based on the DEA method. Natural Hazards, 64, 1575-1586

[8]Deng G, Wang A S, Zhou Y S. 2002. Grain yield risk level calculated by probability distribution. Journal of Nanjing Institute of Meteorology, 25, 481-488 (in Chinese)

[9]Dilley M, Chen R S, Deichmann U, Lerner-Lam A L, Arnold M. 2005. Natural Disaster Hotspots: A Global Risk Analysis Synthesis Report. The World Bank, Hazard Management Unit, Washington, D.C. pp. 1-125

[10]Fisher R A, Tippett L H C.1928. Limiting forms of the frequency distribution of the largest or smallest member of a sample. Proceedings of the Cambridge Philosophical Society, 24, 180-190

[11]Gallagher P. 1987. U.S. soybean yields: estimation and forecasting with nonsymmetric disturbances. American Journal of Agricultural Economics, 71, 796-803

[12]Glauber J W. 2004. Crop insurance reconsidered. American Journal of Agricultural Economics, 86, 1179-1195

[13]Gnedenko B V. 1943. Sur la Distribution limite du Terme maximum du terme maximum d’une serie aleatoire. Annals of Mathematics, 44, 423-453 (in French)

[14]Goodwin B K, Mahul O. 2004. Risk modeling concepts relating to the design and rating of agricultural insurance contracts. In: Working Paper No. 3392. World Bank, Washington, D.C. pp. 1-37

[15]Goodwin B K, Roberts M C, Coble K H. 2000. Measurement of price risk in revenue insurance: tmplications of distributional assumptions. Journal of Agricultural and Resource Economics, 25, 195-214

[16]Gumbel E. 1958. Statistics of Extremes. Columbia University Press, New York.

[17]Hao J. 2005. Modeling and financing weather risk: three essays. PhD thesis, University of Kentucky, Lexington.

[18]IPCC. 2007. Climate change 2007-impacts, adaptation and vulnerability. In: Contribution of Working Group II to the Fourth Assessment Report of the IPCC. Technical report, International Panel on Climate Change, Cambridge.

[19]Lawas C P. 2005. Crop insurance premium rate impacts of flexible parametric yield distributions: an evaluation of Johnson family of distributions. MSc thesis, Texas Tech University, Lubbock, TX.

[20]Leadbetter M R, Lindgren G, Rootzen H. 1983. Extremes and Related Properties of Random Sequences and Processes. Springer-Verlag, New York.

[21]Xu L, Zhang Q, Zhang X. 2011. Evaluating agricultural catastrophic risk. China Agricultural Economic Review,3, 451-461

[22]Xu L. 2012. Study on assessment model of agricultural catastrophe risk. PhD thesis, Graduate School of Chinese Academy of Agricultural Sciences, Beijing. (in Chinese)

[23]Liu L F, Zou J, Liu X N. 2002. Assessment and analysis of the vulnerability of agricultural flood-waterloggingdisaster - a case study of Hengyang, Hunan. ResourceEnvironment in the Yangtze Basin, 11, 291-295 (inChinese)

[24]Ma D G, Liu Y, Chen J, Zheng L, Zhang W J. 2007.Farmer’s vulnerability to flooding in the Poyang Lake region. Acta Geogr Sinica, 62, 321-332. (in Chinese)

[25]Messner F, Meyer V. 2006. Flood damage, vulnerability andrisk perception-challenges for flood damage research.In: Schanze J, Zeman E, Marsalek J, eds., Flood Risk Management-Hazards, Vulnerability and Mitigation Measures, Nato Science Series IV. Springer, Dordrecht.pp. 149-167

[26]Nelson C H, Preckel P V. 1989. The conditional beta distribution as a stochastic production function.American Journal of Agricultural Economics, 71, 370-378

[27]Ramirez O A, Misra S, Field J. 2003. Crop-yield distributions revisited. American Journal of AgriculturalEconomic, 85, 108-120

[28]Sherrick B J, Zanini F C, Schnitkey G D, Hirwin S.2004. Crop insurance valuation under alternative yield distributions. American Journal of Agricultural Economics, 86, 406-419

[29]Zhao S. 2010. A Study on the Theory and Applicationof Scene-Driven Risk Analysis of Regional Natural Disaster. Beijing Normal University, Beijing. (inChinese)

[30]Tapsell S M, Penning-Rowsell E C, Tunstall S M, WilsonT. 2002. Vulnerability to flooding: health and social dimensions. Philosophical Transactions of the Royal Society (A), 360, 1511-1525

[31]Zhang Q, Wang K. 2011. Assessment and regional planningof chinese agricultural natural disaster risks. Chinese Journal of Agricultural Resources and Regional Planning, 32, 32-36

[32](in Chinese)Zhu H C, Li D R. 2005. Analysis on changes of the vulnerability of flood-waterlogging disaster in Dong-tingLake area. Journal of Central China Normal University(Natural Science), 39, 283-286 (in Chinese)
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