Natural Hazards

, Volume 67, Issue 2, pp 387–410 | Cite as

US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases

Original Paper

Abstract

This paper focuses on the US Billion-dollar Weather/Climate Disaster report by the National Oceanic and Atmospheric Administration’s National Climatic Data Center. The current methodology for the production of this loss dataset is described, highlighting its strengths and limitations including sources of uncertainty and bias. The Insurance Services Office/Property Claims Service, the US Federal Emergency Management Agency’s National Flood Insurance Program and the US Department of Agriculture’s crop insurance program are key sources of quantified disaster loss data, among others. The methodology uses a factor approach to convert from insured losses to total direct losses, one potential limitation. An increasing trend in annual aggregate losses is shown to be primarily attributable to a statistically significant increasing trend of about 5 % per year in the frequency of billion-dollar disasters. So the question arises of how such trend estimates are affected by uncertainties and biases in the billion-dollar disaster data. The net effect of all biases appears to be an underestimation of average loss. In particular, it is shown that the factor approach can result in a considerable underestimation of average loss of roughly 10–15 %. Because this bias is systematic, any trends in losses from tropical cyclones appear to be robust to variations in insurance participation rates. Any attribution of the marked increasing trends in crop losses is complicated by a major expansion of the federally subsidized crop insurance program, as a consequence encompassing more marginal land. Recommendations concerning how the current methodology can be improved to increase the quality of the billion-dollar disaster dataset include refining the factor approach to more realistically take into account spatial and temporal variations in insurance participation rates.

Keywords

Natural disasters Losses Statistics of extreme events Data sources 

References

  1. Barthel F, Neumayer E (2012) A trend analysis of normalized insured damage from natural disasters. Climatic Change 113:215–237CrossRefGoogle Scholar
  2. Berger JO (1985) Statistical decision theory and bayesian analysis, 2nd edn. Springer, New York, p 617CrossRefGoogle Scholar
  3. Brooks HE, Doswell CA (2001) Normalized damage from major tornadoes in the United States: 1890–1999. Wea. Forecasting 16:168–176CrossRefGoogle Scholar
  4. Changnon SA, Hewings G (2001) Losses from weather extremes in the US. Nat Haz Rev 2:113–123CrossRefGoogle Scholar
  5. Cleveland WS (1979) Robust locally-weighted regression and smoothing scatterplots. J Am Stat Assoc 74:829–836CrossRefGoogle Scholar
  6. Cummins JD, Suher M, Zanjani G (2010) Federal financial exposure to natural catastrophe risk. In: Lucas D (ed) Measuring and managing federal financial risk, University of Chicago Press, Chicago, pp 61–96Google Scholar
  7. Dixon L, Clancy N, Seabury SA, Overton A (2006) The National Flood Insurance Program’s market penetration rate: estimates and policy implications. RAND Corporation, February, Santa Monica, CaliforniaGoogle Scholar
  8. Downton M, Pielke RA Jr (2005) How accurate are disaster loss data? The case of US flood damage. Nat Haz 35:211–228CrossRefGoogle Scholar
  9. FEMA (2011) Flood Insurance Manual. Revised October 2011, Available online: http://www.fema.gov/pdf/nfip/manual201110/index.pdf
  10. Gall M, Borden KA, Emrich CT, Cutter SL (2011) The unsustainable trend of natural hazard losses in the United States. Sustainability 3:2157–2181CrossRefGoogle Scholar
  11. Helsel DR, Hirsch RM (1993) Statistical methods in water resources. Elsevier, Amsterdam, p 522Google Scholar
  12. Hollander M, Wolfe DA (1973) Nonparametric statistical methods. Wiley, New York, p 503Google Scholar
  13. Jagger TH, Elsner JB, Burch RK (2011) Climate and solar signals in property damage losses from hurricanes affecting the United States. Nat Haz 58:541–557CrossRefGoogle Scholar
  14. Johnson DM (2012) Estimating US crop yields. Winrock International Bioenergy Workshop, Crystal City, VA. Available online: https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/Presentations/Johnson_Winrock_12.pdf
  15. Johnson NL, Kotz S (1970) Continuous univariate distributions—2, WileyGoogle Scholar
  16. Katz RW (2002) Stochastic modeling of hurricane damage. J Appl Meteorol 41:754–762CrossRefGoogle Scholar
  17. Katz RW (2010) Discussion on “Predicting losses of residential structures in the state of Florida by the public hurricane loss evaluation model” by S. Hamid et al. Stat Method 7:592–595CrossRefGoogle Scholar
  18. Katz RW (2012) Economic impact of extreme events: an approach based on extreme value theory. In extreme events: observations, modeling and economics, M. Ghil, J. Urrutia-Fucugauchi, and M. Chavez (eds), Geophysical Monograph Series, American Geophysical Union (accepted for publication)Google Scholar
  19. Kunreuther H, Michel-Kerjan E (2011) At war with the weather, Paperback edn. MIT Press, CambridgeGoogle Scholar
  20. Lobell DB, Asner GP (2003) Climate and management contributions to recent trends in US agricultural yields. Science 299, 1032Google Scholar
  21. Major JA (1999) Index hedge performance: Insurer market participation and basis risk. In: Froot KA (ed) The financing of catastrophe risk. University of Chicago Press, Chicago, pp 391–432Google Scholar
  22. Mearns LO (1988) Technological change, climatic variability, and winter wheat yields. Ph.D. thesis, University of California, Los Angeles (available as National Center for Atmospheric Research Technical Note, NCAR/CT-111)Google Scholar
  23. Michel-Kerjan E, Lemoyne de Forges S, Kunreuther H (2011) Policy tenure under the US National Flood Insurance Program (NFIP). Risk Analysis, doi:10.1111/j.1539-6924.2011.01671.x
  24. Munich Re (2012) Severe weather in North America, perils, risks, insurance. Munich Re Group, Munich, p 274Google Scholar
  25. NCDC (2012) Billion-Dollar Weather/Climate Events. Available online: http://www.ncdc.noaa.gov/billions
  26. NOAA (2008) The Easter Freeze of April 2007. A NOAA/USDA Technical Report. 2008-01, pp 47 http://www1.ncdc.noaa.gov/pub/data/techrpts/tr200801/tech-report-200801.pdf
  27. Nordhaus WD (2010) The economics of hurricanes and implications of global warming. Climate Change Economics 1:1–20CrossRefGoogle Scholar
  28. Pielke RA Jr, Gratz J, Landsea CW, Collins D, Saunders MA, Musulin R (2008) Normalized hurricane damage in the United States: 1900–2005. Nat Haz Rev 9:29–42CrossRefGoogle Scholar
  29. PricewaterhouseCoopers (1999) Study of the economic effects of charging actuarially base premium rates for pre-FIRM structuresGoogle Scholar
  30. Texas Governor’s Office (2008) Texas rebounds helping our communities recover from the 2008 Hurricane Season, November 2008, pp 41. Available online: http://governor.state.tx.us/files/press-office/Texas-Rebounds-report.pdf
  31. USDA/RMA Summary of Business and NASS Principal Crop Acreage (2012) Available online: http://www.rma.usda.gov/data/sob.html
  32. Vellinga P, Mills E (2001) Insurance and other financial services. IPCC Third Assessment Report, Working Group 2. Cambridge University Press, Cambridge, pp 417–450Google Scholar
  33. Villarini G, Serinaldi F, Smith JA, Frajewski WF (2009) On the stationarity of annual flood peaks in the continental United States during the 20th century. Water Resour Res 45:W08417. doi:10.1029/2008WR007645 Google Scholar
  34. Willoughby HE (2012) Distributions and trends of death and destruction from hurricanes in the United States, 1900–2008. Nat Haz Rev 13:57–64CrossRefGoogle Scholar

Copyright information

© US Government 2013

Authors and Affiliations

  1. 1.NOAA National Climatic Data CenterAshevilleUSA
  2. 2.National Center for Atmospheric ResearchBoulderUSA

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