Natural Hazards

, Volume 49, Issue 3, pp 421–436 | Cite as

Typhoon disaster in China: prediction, prevention, and mitigation

Original Paper

Abstract

Typhoon-induced disaster is one of the most important factors influencing the economic development and more than 250 million in China. In view of the existing state of typhoon disaster prediction, prevention, and mitigation, this paper proposes a new probability model, Multivariate Compound Extreme Value Distribution (MCEVD), to predict typhoon-induced extreme disaster events. This model establishes prevention criteria for coastal areas, offshore structures, and estuarine cities, and provides an appropriate mitigation scheme for disaster risk management and decision-making.

Keywords

Typhoon disaster Multivariate Compound Extreme Value Distribution Prediction Prevention criteria 

References

  1. Army Corps of Engineers (2005) History of lake pontchartrain and vicinity hurricane protection project. Report of US Government Accountability Office GAO-06-244T, pp 1–4Google Scholar
  2. Gray WM (2003) Twentieth century challenges and milestones. In: Hurricane! Coping with disaster. American Geophysical Union. doi:10.1029/055SP02
  3. Joby W, Michael G (2005) Investigators link levee failures to design flaws. Washington Post, October 24, 2005:A01Google Scholar
  4. Kirby WH, Moss ME (1987) Summary of flood-frequency analysis in the united states. J Hydrol (Amst) 96:5–14. doi:10.1016/0022-1694(87)90139-9 CrossRefGoogle Scholar
  5. Langley RM, El-Shaarawi AH (1986) On the calculation of extreme wave height: a review. Ocean Eng 13(1):93–118. doi:10.1016/0029-8018(86)90006-5 CrossRefGoogle Scholar
  6. Liu TF (Liu DF) (1982) Long term distributions of hurricane characteristics. Proc Offshore Tech Conf OTC 4325:305–313Google Scholar
  7. Liu TF (Liu DF), Ma F (1980) Prediction of extreme wave heights and wind velocities. J Waterw Port Coast Ocean Div ASCE 106(WW4):469–479Google Scholar
  8. Liu DF, Shuqin W, Liping W (2002a) Poisson-Gumbel mixed compound distribution and its application. Chin Sci Bull 47(22):1901–1904. doi:10.1360/02tb9416 CrossRefGoogle Scholar
  9. Liu DF, Yan S, Shuqing W, Shuqin W (2002b) Combined environmental loads criteria for marine structures, vol 14191. In: Proceedings of offshore technology conference, Houston, TX, OTC, pp 1749–1755Google Scholar
  10. Liu DF, Song Y, Shi H, Yu Y, Ma L (2003) Poisson-logistic compound bivariate extreme distribution and its application for designing of platform deck clearance. In: Proceedings in offshore mechanics & arctic engineering, Cancun, Mexico, OMAE 2003-37395Google Scholar
  11. Liu DF, Liping W, Yan S (2004a) Theory of multivariate compound extreme value distribution and its applications in engineering. J Ocean Univ China 34(5):893–902 (in Chinese)Google Scholar
  12. Liu DF, Ma L, Jing K (2004b) Risk analysis of the disaster prevention design criteria for an estuarine city—Shanghai. In: Proceedings in offshore mechanics & arctic engineering, Canada, OMAE 2004-51505Google Scholar
  13. Liu DF, Pang L, Fu G, Shi H, Fan W (2006a) Joint probability analysis of hurricane Katrina 2005, vol 3. In: Proceeding of international offshore and polar engineering conference, San Francisco, USA, pp 74–80Google Scholar
  14. Liu DF, Shi HD, Pang L (2006b) Disaster prevention design criteria for the estuarine cities: New Orleans and Shanghai. Acta Oceanol Sin 25(4):131–142Google Scholar
  15. Liu DF, Wang LP, Pang L (2006c) Theory of multivariate compound extreme value distribution and its application to extreme sea state prediction. Chin Sci Bull 51(23):2926–2930. doi:10.1007/s11434-006-2186-x CrossRefGoogle Scholar
  16. Ochi MK (1982) Stochastic analysis and probabilistic prediction of random seas. Adv Hydrol 13:5–14Google Scholar
  17. Schwerdt RW, Ho FP, Watking RR (1979) Meteorological criteria for standard project hurricane and probable maximum hurricane wind fields, Gulf and East Coast of the United States. NOAA Technical Report NWS 23Google Scholar
  18. Shi D, Zhou S (1999) Moment estimation for multivariate extreme value distribution in a nested logistic model. Ann Inst Stat Math 51(2):253–264CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Disaster Prevention Research InstituteOcean University of ChinaQingdaoChina

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