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Anticipating catastrophes through extreme value modelling

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  • Stuart Coles
  • Luis Pericchi

Abstract

Summary. When catastrophes strike it is easy to be wise after the event. It is also often argued that such catastrophic events are unforeseeable, or at least so implausible as to be negligible for planning purposes. We consider these issues in the context of daily rainfall measurements recorded in Venezuela. Before 1999 simple extreme value techniques were used to assess likely future levels of extreme rainfall, and these gave no particular cause for concern. In December 1999 a daily precipitation event of more than 410 mm, almost three times the magnitude of the previously recorded maximum, caused devastation and an estimated 30000 deaths. We look carefully at the previous history of the process and offer an extreme value analysis of the data—with some methodological novelty—that suggests that the 1999 event was much more plausible than the previous analyses had claimed. Deriving design parameters from the results of such an analysis may have had some mitigating effects on the consequences of the subsequent disaster. The themes of the new analysis are simple: the full exploitation of available data, proper accounting of uncertainty, careful interpretation of asymptotic limit laws and allowance for non‐stationarity. The effect on the Venezuelan data analysis is dramatic. The broader implications are equally dramatic; that a naïve use of extreme value techniques is likely to lead to a false sense of security that might have devastating consequences in practice.

Suggested Citation

  • Stuart Coles & Luis Pericchi, 2003. "Anticipating catastrophes through extreme value modelling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(4), pages 405-416, October.
  • Handle: RePEc:bla:jorssc:v:52:y:2003:i:4:p:405-416
    DOI: 10.1111/1467-9876.00413
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    Cited by:

    1. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 203-225, March.
    2. Xiaoxia Huang & Liying Song, 2018. "An emergency logistics distribution routing model for unexpected events," Annals of Operations Research, Springer, vol. 269(1), pages 223-239, October.
    3. Henryk Gzyl & German Molina & Enrique ter Horst, 2009. "Assessment and propagation of input uncertainty in tree‐based option pricing models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 275-308, May.
    4. Kenneth T. Bogen & Edwin D. Jones, 2006. "Risks of Mortality and Morbidity from Worldwide Terrorism: 1968–2004," Risk Analysis, John Wiley & Sons, vol. 26(1), pages 45-59, February.
    5. Powell, J.H. & Mustafee, N. & Chen, A.S. & Hammond, M., 2016. "System-focused risk identification and assessment for disaster preparedness: Dynamic threat analysis," European Journal of Operational Research, Elsevier, vol. 254(2), pages 550-564.
    6. Caunhye, Aakil M. & Nie, Xiaofeng & Pokharel, Shaligram, 2012. "Optimization models in emergency logistics: A literature review," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 4-13.
    7. Chi, Hong & Li, Jialian & Shao, Xueyan & Gao, Mingang, 2017. "Timeliness evaluation of emergency resource scheduling," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1022-1032.
    8. Kenneth T. Bogen & Edwin D. Jones & Larry E. Fischer, 2007. "Hurricane Destructive Power Predictions Based on Historical Storm and Sea Surface Temperature Data," Risk Analysis, John Wiley & Sons, vol. 27(6), pages 1497-1517, December.
    9. T. D. Pol & S. Gabbert & H.-P. Weikard & E. C. Ierland & E. M. T. Hendrix, 2017. "A Minimax Regret Analysis of Flood Risk Management Strategies Under Climate Change Uncertainty and Emerging Information," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 68(4), pages 1087-1109, December.
    10. Altay, Nezih & Green III, Walter G., 2006. "OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 175(1), pages 475-493, November.
    11. Li, Gong & Shi, Jing, 2012. "Applications of Bayesian methods in wind energy conversion systems," Renewable Energy, Elsevier, vol. 43(C), pages 1-8.
    12. Thomas D. Pol & Ekko C. Ierland & Silke Gabbert, 2017. "Economic analysis of adaptive strategies for flood risk management under climate change," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(2), pages 267-285, February.
    13. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 203-225, March.
    14. Sanaz Moghim & Mohammad Sina Jahangir, 2022. "Reliability framework for characterizing heat wave and cold spell events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(2), pages 1503-1525, June.

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