Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period

  • Xiaodong Ming
  • Wei Xu
  • Ying Li
  • Juan Du
  • Baoyin Liu
  • Peijun Shi
Original Paper
First Online:

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.

Keywords

Multi-hazard Risk Vulnerability surface Copula Joint probability distribution 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grant No. 41201547 and Grant No. 41321001), Programme of Introducing Talents of Discipline to Universities (Grant No. B08008), National Basic Research Program of China (973 Program) (Grant No. 2012CB955404), and Key Project in the National Science & Technology Pillar Program in the Eleventh Five-year Plan Period (Grant No. 2008BAK50B07), and the Integrated Risk Governance Project (2013DFG20710) by the Ministry of Science and Technology of China.

References

  1. Annaka T, Satake K, Sakakiyama T et al (2007) Logic-tree approach for probabilistic tsunami hazard analysis and its applications to the Japanese coasts. Pure appl Geophys 164(2–3):577–592CrossRefGoogle Scholar
  2. Arnold M, Chen RS, Deichmann U et al (2006) Natural Disaster Hotspots Case Studies. World Bank Publications, WashingtonCrossRefGoogle Scholar
  3. Bell R, Glade T (2004) Multi-hazard analysis in natural risk assessment. WIT Press, BostonGoogle Scholar
  4. Benjamin J, Cornell C (1970) Probability, statistics, and decision for civil engineers. McGraw-Hill, New YorkGoogle Scholar
  5. Cardona OD (2003) The need for rethinking the concepts of vulnerability and risk from a holistic perspective: a necessary review and criticism for effective. In: Bankoff G, Frerks G, Hilhorst D (eds) Mapping vulnerability: Disasters, development and people. Earthscan, London, pp 37–51Google Scholar
  6. Corotis RB (2007) An introduction to the special issue: an overview, history and context for the consideration of risk in the built environment. Int J Risk Assess Manag 7(6):759–772CrossRefGoogle Scholar
  7. Crichton D (1999) Natural Disaster Management. In: Ingleton J (ed) The risk triangle. Tudor Rose, London, pp 102–103Google Scholar
  8. Dilley M (2005) Natural disaster hotspots: a global risk analysis. World Bank Publications, WashingtonCrossRefGoogle Scholar
  9. Ehrlich D, Zeug G, Gallego J et al (2010) Quantifying the building stock from optical high-resolution satellite imagery for assessing disaster risk. Geocarto Int 25(4):281–293CrossRefGoogle Scholar
  10. Eskelinen H, Hirvonen T (2006) Positioning Finland in a European Space. Ministry of the Environment, HelsinkiGoogle Scholar
  11. Favre A-C, El Adlouni S, Perreault L et al (2004) Multivariate hydrological frequency analysis using copulas. Water Resour Res 40(1):1–12CrossRefGoogle Scholar
  12. FEMA (2011) Getting started with HAZUS-MH 2.1. Technical Manual. Department of Homeland Security, WashingtonGoogle Scholar
  13. Fleischhauer M, Greiving S, Schlusemann B (2005) Multi-risk assessment of spatially relevant hazards in Europe. European Safety Management Group Symposium. Nürnberg, Germany, pp 1–14Google Scholar
  14. Ge Y, Dou W, Gu Z et al (2013) Assessment of social vulnerability to natural hazards in the Yangtze River Delta, China. Stoch Env Res Risk Assess 27(8):1899–1908CrossRefGoogle Scholar
  15. Genest C, Favre A-C (2007) Everything you always wanted to know about copula modeling but were afraid to ask. J Hydrol Eng 12(4):347–368CrossRefGoogle Scholar
  16. Greiving S (2006) Integrated risk assessment of multi-hazards: a new methodology. Natural and technological hazards and risks affecting the spatial development of European regions. Geol Surv of Finl, Special Paper 42:75–82Google Scholar
  17. Grünthal G, Thieken HA, Schwarz J et al (2006) Comparative risk assessments for the city of cologne—storms, floods earthquakes. Nat Hazards 38(1–2):21–44CrossRefGoogle Scholar
  18. Kappes MS, Keiler M, Elverfeldt K, Glade T (2012) Challenges of analyzing multi-hazard risk: a review. Nat Hazards 64(2):1925–1958CrossRefGoogle Scholar
  19. Khanduri AC, Morrow GC (2003) Vulnerability of buildings to windstorms and insurance loss estimation. J Wind Eng Ind Aerodyn 91(4):455–467CrossRefGoogle Scholar
  20. Lee KH, Rosowsky DV (2006) Fragility analysis of woodframe buildings considering combined snow and earthquake loading. Struct Saf 28(3):289–303CrossRefGoogle Scholar
  21. Li Y, Van de Lindt JW (2012) Loss-based formulation for multiple hazards with application to residential buildings. Eng Struct 38:123–133CrossRefGoogle Scholar
  22. Li N, Liu X, Xie W et al (2013) The return period analysis of natural disasters with statistical modeling of bivariate joint probability distribution. Risk Anal 33(1):134–145CrossRefGoogle Scholar
  23. Liu B (2011) Multi-hazard Risk Assessment in the Yangtze River Delta Region: A Case Study on Human Life. Dissertation, Beijing Normal University (in Chinese)Google Scholar
  24. Melchiori, M. (2003). Which Archimedean copula is the right one? YieldCurve.com e-Journal. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1123135. Accessed 25 May 2013
  25. Merriam DF, Lippert RH (1966) Geologic model studies using trend-surface analysis. J Geol 74(3):344–357CrossRefGoogle Scholar
  26. Mosquera-Machado S, Dilley M (2009) A comparison of selected global disaster risk assessment results. Nat Hazards 48(3):439–456CrossRefGoogle Scholar
  27. Murray AT (2009) Quantitative Geography. Journal of Regional Science’s 50th Anniversary Conference. Federal Reserve Bank of New York, New YorkGoogle Scholar
  28. Nelson R (1999) An introduction to Copulas. Springer, New YorkCrossRefGoogle Scholar
  29. Pammenter NW, Vander Willigen C (1998) A mathematical and statistical analysis of the curves illustrating vulnerability of xylem to cavitation. Tree Physiol 18(8–9):589–593CrossRefGoogle Scholar
  30. Rahman A, Weinmann PE, Hoang TMT, Laurenson EM (2002) Monte Carlo simulation of flood frequency curves from rainfall. J Hydrol 256(3–4):196–210CrossRefGoogle Scholar
  31. Rodriguez JC (2007) Measuring financial contagion: a Copula approach. J Empir Finance 14(3):401–423CrossRefGoogle Scholar
  32. Saucier WJ (2003) Principles of meteorological analysis. Courier Dover Publications, New YorkGoogle Scholar
  33. Schmidt J, Matcham I, Reese S et al (2011) Quantitative multi-risk analysis for natural hazards: a framework for multi-risk modelling. Nat Hazards 58(3):1169–1192CrossRefGoogle Scholar
  34. Schmidt-thomé P, Kallio H, Greiving S, Fleischhauer M (2003) Development of Natural Hazard maps for European Regions. EU-MEDIN Forum on Disaster Research “The Road to Harmonisation”. Thessaloniki, Greece, pp 26–27Google Scholar
  35. Shankman D, Keim BD, Song J (2006) Flood frequency in China’s Poyang Lake region: trends and teleconnections. Int J Climatol 26(9):1255–1266CrossRefGoogle Scholar
  36. Shi P (2011) Atlas of Natural Disaster Risk of China. Science Press, BeijingGoogle Scholar
  37. Shi P, Shuai J, Chen W, Lu L (2010) Study on large-scale disaster risk assessment and risk transfer models. Int J Disaster Risk 1(2):1–8Google Scholar
  38. Shi P, Wang J, Fang X et al (2013) Natural Disaster Risk Assessment in the Yangtze River Delta region of China: Multi-hazard risk assessment and risk mapping. Science Press, Beijing in ChineseGoogle Scholar
  39. Shiau JT (2003) Return period of bivariate distributed extreme hydrological events. Stoch Env Res Risk Assess 17(1–2):42–57CrossRefGoogle Scholar
  40. Thierry P, Stieltjes L, Kouokam E et al (2008) Multi-hazard risk mapping and assessment on an active volcano: the GRINP project at Mount Cameroon. Nat Hazards 45(3):429–456CrossRefGoogle Scholar
  41. Toro GR, Resio DT, Divoky D et al (2010) Efficient joint-probability methods for hurricane surge frequency analysis. Ocean Eng 37(1):125–134CrossRefGoogle Scholar
  42. Unwin DJ (1975) An introduction to trend surface analysis. Geo Abstracts Limited, NorwichGoogle Scholar
  43. Wang XL, Zhang J (2007) A nonlinear model for assessing multiple probabilistic risks: a case study in south five-island of Changdao National Nature Reserve in China. J Environ Manage 85(4):1101–1108CrossRefGoogle Scholar
  44. Yin YJ, Li Y (2011) Probabilistic loss assessment of light-frame wood construction subjected to combined seismic and snow loads. Eng Struct 33(2):380–390CrossRefGoogle Scholar
  45. Zhang L, Singh V (2006) Bivariate flood frequency analysis using the copula method. J Hydrol Eng 11:150–164. doi: 10.1061/(ASCE)1084-0699(2006)11:2(150) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Xiaodong Ming
    • 1
    • 2
    • 3
  • Wei Xu
    • 1
    • 2
    • 3
  • Ying Li
    • 1
    • 2
    • 3
  • Juan Du
    • 1
    • 2
    • 3
  • Baoyin Liu
    • 4
  • Peijun Shi
    • 1
    • 2
    • 3
  1. 1.State Key Laboratory of Earth Surface Processes and Resource EcologyBeijing Normal UniversityBeijingChina
  2. 2.Key Laboratory of Environmental Change and Natural Disasters of Ministry of EducationBeijing Normal UniversityBeijingChina
  3. 3.Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of EducationBeijing Normal UniversityBeijingChina
  4. 4.School of Earth and EnvironmentUniversity of LeedsLeedsUK

Personalised recommendations