Natural Hazards

, Volume 91, Issue 2, pp 759–783 | Cite as

Assessing typhoon damages to Taiwan in the recent decade: return period analysis and loss prediction

  • Chia-Jeng Chen
  • Tsung-Yu Lee
  • Che-Min Chang
  • Jun-Yi Lee
Original Paper

Abstract

Devastating typhoons that induce enormous losses to various sectors of the economy underline the importance of an improved understanding of the regional hazard-to-loss relationship. This study utilizes the up-to-date loss data of typhoons in Taiwan from 2006 to 2015 to analyze the interannual variations in the annual aggregate losses (AALs) and develop a loss prediction model for the major administrative divisions. Return period analysis applied to the AALs identifies western-to-southwestern Taiwan as the high-risk region, among which Chiayi and Pingtung exhibit the highest 10-year AALs over 100 million. The gamma hurdle model (GHM) is adopted for loss prediction for its ability to stepwise model the loss occurrence and amount, leading to straightforward discussion regarding the explanatory power and statistical significance of meteorological predictors in their marginal and joint space. In the first part of the GHM, maximum daily rainfall and maximum gust wind are selected as the two most significant meteorological predictors for the logistic regression model of the loss occurrence, showing a remarkable model accuracy of \({\sim 0.9}\). In the second part of the GHM, maximum sustained wind is added to the gamma generalized linear model of the loss amount, generating the cross-validated Nash–Sutcliffe efficiency (mean absolute error) values higher (lower) than 0.6 (3 million) for several southwestern cities. Event assessment for Typhoons Soudelor (2015) and Morakot (2009) further demonstrates the utility of the GHM and illustrates the essential for accounting for the combination effect of rainfall and wind on loss estimation.

Keywords

Catastrophe modeling Statistical analysis Loss assessment Typhoon rainfall Typhoon wind Tropical cyclone 

Notes

Acknowledgements

Work by C-J. Chen was supported by Taiwan’s Ministry of Science and Technology (MOST) under grants: MOST 105-2621-M-005-004-MY3 and MOST 106-2625-M-005-004-. Work by T-Y. Lee was also supported by MOST under grant: MOST 106-2116-M-003-004. We also thank the CWB and the COA for maintaining the archives of typhoon and loss data, respectively.

References

  1. Alexander D (2002) Principles of emergency planning and management. Oxford University Press, OxfordGoogle Scholar
  2. Cameron AC, Trivedi PK (2013) Regression analysis of count data. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  3. Chang FJ, Chiang YM, Cheng WG (2013) Self-organizing radial basis neural network for predicting typhoon-induced losses to rice. Paddy Water Environ 11:369–380CrossRefGoogle Scholar
  4. Cheung YB (2002) Zeroinflated models for regression analysis of count data: a study of growth and development. Stat Med 21(10):1461–1469CrossRefGoogle Scholar
  5. Chiang YM, Cheng WG, Chang FJ (2002) A hybrid artificial neural network-based agri-economic model for predicting typhoon-induced losses. Nat Hazards 63(2):769–787CrossRefGoogle Scholar
  6. Chien FC, Kuo HC (2011) On the extreme rainfall of Typhoon Morakot (2009). J Geophys Res 116(D05):104Google Scholar
  7. Cutter SL (1996) Vulnerability to environmental hazards. Prog Hum Geogr 20(4):529–539CrossRefGoogle Scholar
  8. Dutta D, Herath S, Musiake K (2003) A mathematical model for flood loss estimation. J Hydrol 277(1):24–49CrossRefGoogle Scholar
  9. Huang JC, Lee TY, Lee JY (2014) Observed magnified runoff response to rainfall intensification under global warming. Environ Res Lett 9(3):034008CrossRefGoogle Scholar
  10. Huang WK, Wang JJ (2015) Typhoon damage assessment model and analysis in Taiwan. Nat Hazards 79(1):497–510CrossRefGoogle Scholar
  11. Kang JL, Su MD, Chang LF (2005) Loss functions and framework for regional flood damage estimation in residential area. J Mar Sci Technol 13(3):193–199Google Scholar
  12. Kao SJ, Huang JC, Lee TY, Walling DE (2011) The changing rainfallrunoff dynamics and sediment response of small mountainous rivers in taiwan under a warming climate. Sediment problems and sediment management in Asian river basins (IAHS Publ 349). Hyderabad, India, pp 114–129Google Scholar
  13. Lambert D (1992) Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics 34(1):1–14CrossRefGoogle Scholar
  14. Lee CS, Huang LR, Shen HS, Wang ST (2006) A climatology model for forecasting typhoon rainfall in Taiwan. Nat Hazards 37(1):87–105CrossRefGoogle Scholar
  15. Lee TY, Huang JC, Lee JY, Jien SH, Zehetner F, Kao SJ (2015) Magnified sediment export of small mountainous rivers in Taiwan: chain reactions from increased rainfall intensity under global warming. PLoS ONE 10(9):e0138283CrossRefGoogle Scholar
  16. Li H, Dai A, Zhou T, Lu J (2010) Responses of East Asian summer monsoon to historical SST and atmospheric forcing during 1950–2000. Clim Dyn 34(4):501–514CrossRefGoogle Scholar
  17. Li HC, Chen YC, Guo MJ (2013) The development and application of Taiwan typhoon loss assessment system (TLAS). J Chin Agric Eng 59(4):42–55Google Scholar
  18. Liu KF, Li HC, Hsu YC (2009) Debris flow hazard assessment with numerical simulation. Nat Hazards 49(1):137–161CrossRefGoogle Scholar
  19. McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics, chap 4. Academic Press, New York, pp 105–142Google Scholar
  20. Mei W, Xie SP (2016) Intensification of landfalling typhoons over the northwest Pacific since the late 1970s. Nat Geosci 9:753–757CrossRefGoogle Scholar
  21. Nelder JA, Wedderburn RWM (1972) Generalized linear models. J R Stat Soc A 135:370–384CrossRefGoogle Scholar
  22. Roberts NJ, Nadim F, Kalsnes B (2009) Quantification of vulnerability to natural hazards. Georisk 3(3):164–173Google Scholar
  23. Schwierz C, Köllner-Heck P, Zenklusen ME, Bresch DN, Vidale PL, Wild M, Schär C (2010) Modelling European winter wind storm losses in current and future climate. Clim Change 101(3):485–514CrossRefGoogle Scholar
  24. Shaw D, Huang HH, Horng MJ, Lu MM, Lo YL (2007) A probabilistic flood risk analysis of household losses in the Danshuei River basin. Taiwan Econ Forecast Policy 27(3):31–53Google Scholar
  25. Swinbank R, Kyouda M, Buchanan P, Froude L, Hamill TM, Hewson TD, Keller JH, Matsueda M, Methven J, Pappenberger F, Scheuerer M (2016) The TIGGE project and its achievements. Bull Am Meteorol Soc 97(1):49–67CrossRefGoogle Scholar
  26. Tu JY, Chou C, Chu PS (2009) The abrupt shift of typhoon activity in the vicinity of Taiwan and its association with western North Pacific East Asian climate change. J Clim 22:3617–3628CrossRefGoogle Scholar
  27. Tu JY, Chou C, Huang P, Huang R (2011) An abrupt increase of intense typhoons over the western North Pacific in early summer. Environ Res Lett 6(034):013Google Scholar
  28. Weibull W (1939) A statistical theory of the strength of materials. Ing Vetensk Akad 151:45–55Google Scholar
  29. Wu CC, Kuo YH (1999) Typhoons affecting Taiwan: current understanding and future challenges. Bull Am Meteorol Soc 80(1):67–80CrossRefGoogle Scholar
  30. Yu FC, Chen LK (2010) Investigation on hillside disasters due to the flood ravage on 8th August 2009. J Civil Hydraul Eng 37(1):32–40 (in Chinese)Google Scholar
  31. Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM (2009) Zero-truncated and zero-inflated models for count data. In: Mixed effects models and extensions in ecology with R. Springer, New York, pp 261–293Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.National Chung Hsing UniversityTaichungTaiwan
  2. 2.National Taiwan Normal UniversityTaipeiTaiwan
  3. 3.National Taiwan UniversityTaipeiTaiwan

Personalised recommendations