Natural Hazards

, Volume 93, Issue 2, pp 887–903 | Cite as

Estimation of the compound hazard severity of tropical cyclones over coastal China during 1949–2011 with copula function

  • Yanting Ye
  • Weihua FangEmail author
Original Paper


In China, the intensity of a tropical cyclone (TC) is officially classified into six scales based on the 2-min sustained winds. However, the destructive potential of a TC is determined not only by winds, but also by other hazards such as rainfall. Therefore, the integration of the effects of all the various hazards to reflect the compound severity of a TC is of great importance. In this paper, the maximum wind speed (MWS) at landfall and total precipitation volume (TP) over land in China are obtained as two representative hazards. Copulas, having the capability to construct joint probability distributions of two or more random variables with an unidentified dependence structure among the variables, are utilized to construct the dependence of TC wind and rainfall. Firstly, the cumulative probability distribution functions (CDFs) are fitted separately based on four probability models with 218 historical TC records from 1985 to 2011 over China. Three copula functions are employed to construct the joint probability using the best-fitted marginal CDFs. The best probability functions are determined according to the OLS value. Secondly, the univariate return periods of MWS and TP, and two types of joint return period (\(RP_{ \cup }\) and \(RP_{ \cap }\)) are calculated. We found that the univariate return periods are discrepant for individual hazards and neither is a proper indicator for TC compound hazard severity. The joint return period \(RP_{ \cap }\) is found to be a better indicator of the compound hazard severity of TCs. Finally, the economic losses of 178 historical TCs from 1985 to 2010 are normalized by eliminating the impacts of inflation, increased assets, and population. We find that the joint return period \(RP_{ \cap }\) has a better capacity for representing TC-induced loss. This study highlights the improved capacity of multi-hazard joint return period over univariate return periods for representing the compound hazard severity and damage.


Tropical cyclone Compound hazard severity Disaster loss Copula Joint return period 



This work is supported by the National Key Research and Development Program of China (NO. 2017YFA0604903), and Special Fund for Climate Change of China Meteorological Administration (Grant No. CCSF201719).

Supplementary material

11069_2018_3329_MOESM1_ESM.xlsx (349 kb)
Supplementary material 1 (XLSX 348 kb)


  1. Bell GD, Halpert MS, Schnell RC, Higgins RW, Lawrimore J, Kousky VE, Tinker R, Thiaw W, Chelliah M, Artusa A (2000) Climate Assessment for 1999. Bull Am Meteor Soc 81:s1–s50CrossRefGoogle Scholar
  2. Brunner MI, Seibert J, Favre AC (2016) Bivariate return periods and their importance for flood peak and volume estimation. Wiley Interdiscip Rev Water 3(6):819–833CrossRefGoogle Scholar
  3. CMA (China Meteorological Administration) (1972–1989) Yearbook of Typhoon 1971–1988. China Meteorological Press, Beijing (in Chinese)Google Scholar
  4. CMA (China Meteorological Administration) (1986–2011) China meteorological disaster yearbook 1985–2010. China Meteorological Press, Beijing (in Chinese)Google Scholar
  5. CMA (China Meteorological Administration) (1990–2012) Yearbook of Tropical Cyclone 1989–2011. China Meteorological Press, Beijing (in Chinese)Google Scholar
  6. CMA (China Meteorological Administration) (2006) Grade of Tropical Cyclones, GB/T 19201–2006. Standards Press of China, BeijingGoogle Scholar
  7. Corbella S, Stretch DD (2012) Multivariate return periods of sea storms for coastal erosion risk assessment. Nat Hazards Earth Syst Sci 12(8):2699–2708CrossRefGoogle Scholar
  8. De Michele C, Salvadori G (2003) A generalized Pareto intensity–duration model of storm rainfall exploiting 2-copulas. J Geophys Res Atmos 108:D2CrossRefGoogle Scholar
  9. Dong Sheng, Jiao Chun-Shuo, Tao Shan-Shan (2017) Joint return probability analysis of wind speed and rainfall intensity in typhoon-affected sea area. Nat Hazards 86(3):1193–1205CrossRefGoogle Scholar
  10. Emanuel Kerry (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436(7051):686–688CrossRefGoogle Scholar
  11. Favre AC, El Adlouni S, Perreault L, Thiémonge N, Bobée B (2004) Multivariate hydrological frequency analysis using copulas. Water Resour Res 40:1CrossRefGoogle Scholar
  12. Grimaldi Salvatore, Serinaldi Francesco (2006) Asymmetric copula in multivariate flood frequency analysis. Adv Water Resour 29(8):1155–1167CrossRefGoogle Scholar
  13. Gumbel Emil Julius (1941) The return period of flood flows. Ann Math Stat 12(2):163–190CrossRefGoogle Scholar
  14. Holland G J, Done J, NCAR Regional Climate Research Group (2011) A global index for tropical cyclone damage potential. In: AGU Fall Meeting AbstractsGoogle Scholar
  15. Joe H (1997) Multivariate models and multivariate dependence concepts. CRC PressGoogle Scholar
  16. Kantha Lakshmi (2006) Time to replace the Saffir-Simpson hurricane scale? Eos, Trans Am Geophys Un 87(1):3–6CrossRefGoogle Scholar
  17. Li N, Gu X, Liu X (2013a) Return period analysis based on joint distribution of three hazards in dust storm disaster. Adv Earth Sci 28(004):490–496 (in Chinese) Google Scholar
  18. Li N, Liu X, Xie W, Wu J, Zhang P (2013b) The return period analysis of natural disasters with statistical modeling of bivariate joint probability distribution. Risk Anal 33(1):134–145CrossRefGoogle Scholar
  19. Ming X, Xu W, Li Y, Du J, Liu B, Shi P (2015) Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period. Stoch Environ Res Risk Assess 29(1):35–44CrossRefGoogle Scholar
  20. NBS (National Bureau of Statistics) (1986–2001) China Statistical Yearbook 1986–2001. China Statistics Press, Beijing (in Chinese)Google Scholar
  21. NBS (National Bureau of Statistics) (2010) China Compendium of Statistics 1949–2008. China Statistics Press, Beijing (in Chinese) Google Scholar
  22. NBS (National Bureau of Statistics) (2013) China Statistical Yearbook 2013. China Statistics Press, Beijing (in Chinese) Google Scholar
  23. NBS (National Bureau of Statistics) (2014) China Statistical Yearbook 2014. China Statistics Press, Beijing (in Chinese) Google Scholar
  24. Pielke RA Jr, Landsea CW (1998) Normalized hurricane damages in the United States: 1925–95. Weather Forecast 13(3):621–631CrossRefGoogle Scholar
  25. Pielke RA Jr, Gratz J, Landsea CW, Collins D, Saunders MA, Musulin R (2008) Normalized hurricane damage in the United States: 1900–2005. Nat Hazards Rev 9(1):29–42CrossRefGoogle Scholar
  26. Powell Mark D, Reinhold Timothy A (2007) Tropical cyclone destructive potential by integrated kinetic energy. Bull Am Meteor Soc 88(4):513–526CrossRefGoogle Scholar
  27. Shiau JT (2003) Return period of bivariate distributed extreme hydrological events. Stoch Environ Res Risk Assess 17(1):42–57CrossRefGoogle Scholar
  28. Shiau JT (2006) Fitting drought duration and severity with two-dimensional copulas. Water Resour Manage 20(5):795–815CrossRefGoogle Scholar
  29. Shiau JT, Modarres R (2009) Copula-based drought severity-duration-frequency analysis in Iran. Meteorol Appl 16(4):481–489CrossRefGoogle Scholar
  30. Shiau JT, Feng S, Nadarajah S (2007) Assessment of hydrological droughts for the Yellow River, China, using copulas. Hydrol Process 21(16):2157–2163CrossRefGoogle Scholar
  31. Si Y (1998) The World’s most catastrophic dam failures. Qing (1998a), 25–38Google Scholar
  32. Sraj M, Bezak N, Brilly M (2015) Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River. Hydrol Process 29(2):225–238CrossRefGoogle Scholar
  33. Trepanier JC, Needham HF, Elsner JB, Jagger TH (2015) Combining surge and wind risk from hurricanes using a copula model: an example from Galveston, Texas. Prof Geogr 67(1):52–61CrossRefGoogle Scholar
  34. Um MJ, Joo K, Nam W, Heo JH (2017) A comparative study to determine the optimal copula model for the wind speed and precipitation of typhoons. Int J Climatol 37(4):2051–2062CrossRefGoogle Scholar
  35. Wu ZK, Zhao L, Ge YJ (2010) Statistical analysis of wind velocity and rainfall intensity joint probability distribution of Shanghai area in typhoon condition. Acta Aerodynamica Sinica 28(4):393–399Google Scholar
  36. Ying M, Zhang W, Yu H, Lu X, Feng J, Fan Y et al (2014) An overview of the China Meteorological Administration tropical cyclone database. J Atmos Oceanic Technol 31(2):287–301CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical ScienceBeijing Normal UniversityBeijingPeople’s Republic of China
  2. 2.Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of EducationBeijing Normal UniversityBeijingPeople’s Republic of China

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