Volume 13 Issue 4
Aug.  2022
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Charles Doktycz, Mark Abkowitz, Hiba Baroud. Extreme Weather Loss and Damage Estimation Using a Hybrid Simulation Technique[J]. International Journal of Disaster Risk Science, 2022, 13(4): 592-601. doi: 10.1007/s13753-022-00430-5
Citation: Charles Doktycz, Mark Abkowitz, Hiba Baroud. Extreme Weather Loss and Damage Estimation Using a Hybrid Simulation Technique[J]. International Journal of Disaster Risk Science, 2022, 13(4): 592-601. doi: 10.1007/s13753-022-00430-5

Extreme Weather Loss and Damage Estimation Using a Hybrid Simulation Technique

doi: 10.1007/s13753-022-00430-5
  • Available Online: 2022-09-09
  • History has shown that occurrences of extreme weather are becoming more frequent and with greater impact, regardless of one’s geographical location. In a risk analysis setting, what will happen, how likely it is to happen, and what are the consequences, are motivating questions searching for answers. To help address these considerations, this study introduced and applied a hybrid simulation model developed for the purpose of improving understanding of the costs of extreme weather events in the form of loss and damage, based on empirical data in the contiguous United States. Model results are encouraging, showing on average a mean cost estimate within 5% of the historical cost. This creates opportunities to improve the accuracy in estimating the expected costs of such events for a specific event type and geographic location. In turn, by having a more credible price point in determining the cost-effectiveness of various infrastructure adaptation strategies, it can help in making the business case for resilience investment.
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  • Blackwell, C. 2014. Power law or lognormal? Distribution of normalized hurricane damages in the United States, 1900-2005. Natural Hazards Review 16(3). https://doi.org/10.1061/(ASCE)NH.1527-6996.0000162.
    Botzen, W.J.W., and J.C.J.M. Van Den Bergh. 2009. Managing natural disaster risks in a changing climate. Environmental Hazards 8(3):209-225.
    Botzen, W.J.W., O. Deschenes, and M. Sanders. 2020. The economic impacts of natural disasters:A review of models and empirical studies. Review of Environmental Economics and Policy 13(2):167-188.
    Choi, D., D.O. Kasdan, and D.K. Yoon. 2019. Analyzing disaster loss trends:A comparison of normalization methodologies in South Korea. Risk Analysis 39(4):859-870.
    Cutter, S., B. Boruff, and L. Shirley. 2003. Social vulnerability to environmental hazards. Social Science Quarterly 84(2):242-261.
    Doktycz, C., and M. Abkowitz. 2019. Loss and damage estimation for extreme weather events:State of the practice. Sustainability 11(15):Article 4243.
    Flanagan, B.E., E.W. Gregory, E.J. Hallisey, J.L. Heitgerd, and B. Lewis. 2011. A social vulnerability index for disaster management. Journal of Homeland Security and Emergency Management 8(1):Article 3.
    French, J., P. Kokoszka, S. Stoev, and L. Hall. 2018. Quantifying the risk of heat waves using extreme value theory and spatio-temporal functional data. Computational Statistics and Data Analysis 131:176-193.
    Integrated Research on Disaster Risk. 2014. Peril classification and hazard glossary. IRDR data publication No. 1. Beijing:Integrated Research on Disaster Risk.
    Kahn, M.E. 2005. The death toll from natural disasters:The role of income, geography, and institutions. Review of Economics and Statistics 87(2):271-284.
    Karl, T.R., and W.J. Koss. 1984. Regional and national monthly, seasonal, and annual temperatures weighted by area, 1895-1983. Historical Climatology Series 4-3. Asheville, NC:National Climatic Data Center.
    Katz, R.W. 2020. Statistical issues in detection of trends in losses from extreme weather and climate events. In Evaluating climate change impacts, ed. V. Lyubchich, Y.R. Gel, K.H. Kilbourne, T.J. Miller, N.K. Newlands, and A.B. Smith, 165-186. London:Chapman and Hall/CRC.
    Kunruether, H. 2008. Reducing losses from catastrophic risks through long term insurance and mitigation. Social Research:An International Quarterly 75(3):905-930.
    Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, et al. 2021. Climate change 2021:The physical science basis. Contribution of Working Group I to the sixth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, UK:Cambridge University Press.
    McNeil, A.J. 1997. Estimating the tails of loss severity distributions using extreme value theory. ASTIN Bulletin:The Journal of the IAA 27(1):117-137.
    Nordhaus, W. 2010. The economics of hurricanes and implications of global warming. Climate Change Economics 1(1):1-20.
    Shen, G., L. Zhou, Y. Wu, and Z. Cai. 2018. A global expected risk analysis of fatalities, injuries, and damages by natural disasters. Sustainability 10(7):Article 2573.
    Tarling, H.A. 2017. Comparative analysis of social vulnerability indices:CDC's SVI and SoVI®. Masters thesis, Lund University, Lund, Sweden. http://lup.lub.lu.se/student-papers/record/8928519. Accessed 16 May 2022.
    Weinkle, J., C. Landsea, D. Collins, R. Musulin, R.P. Crompton, P.J. Klotzbach, and R. Pielke Jr. 2018. Normalized hurricane damage in the continental United States 1900-2017. Nature Sustainability 1:808-813.
    Wood, E., M. Sanders, and T. Frazier. 2021. The practical use of social vulnerability indicators in disaster management. International Journal of Disaster Risk Reduction 63:Article 102464.
    Zimbidis, A., N. Frangos, and A. Pantelous. 2007. Modeling earthquake risk via extreme value theory and pricing the respective catastrophe bonds. ASTIN Bulletin:The Journal of the IAA 37(1):163-183.
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