Natural Hazards

, Volume 91, Issue 2, pp 671–696 | Cite as

Selection of hazard-consistent hurricane scenarios for regional combined hurricane wind and flood loss estimation

  • Bin Pei
  • Weichiang Pang
  • Firat Y. Testik
  • Nadarajah Ravichandran
  • Fangqian Liu
Original Paper


This paper presents a new methodology for selecting hazard-consistent hurricane scenarios (with similar return periods) for estimating the regional losses due to combined hurricane wind and flood. An in-house stochastic hurricane simulation program was used to simulate 50,000 years of full-track synthetic hurricanes. A wind field model along with a boundary layer model was utilized to compute the surface wind speeds. As illustrative examples, the SLOSH (Sea, Lake, and Overland Surges from Hurricanes) model was employed to calculate the corresponding flood elevations for two loss estimation domains (Charleston Peninsula, South Carolina and Miami Beach, Florida) with each of them divided into census blocks. The peak wind speeds and maximum flood elevations at the centroids of respective census blocks were utilized to determine the joint mean recurrence intervals (MRIs) of individual hurricane events. For regional loss estimation purpose, the joint MRIs of hurricanes were weighted by the population of every census block. A hurricane selection procedure was developed to select hazard-consistent hurricane scenarios with a joint MRI of 100 years. Three hurricane ensembles, selected based on only wind speeds, only flood elevations, and joint wind speeds and flood elevations, were imported into the HAZUS-MH (Hazards US Multi-Hazards) program to perform combined wind and flood loss estimations. The results indicate that hurricane selection using either only wind speeds or only flood elevations can overestimate the combined losses. The different characteristics of the selected hurricane scenarios for the two loss estimation domains are also discussed.


Combined hurricane wind and flood Hazard-consistent hurricane scenario Joint mean recurrence interval HAZUS-MH loss estimation 



Funding was provided by the Glenn Department of Civil Engineering, Clemson University (Robust Design Research Fund)


  1. American Society of Civil Engineers (ASCE) (2010) Minimum design loads for buildings and other structures (ASCE/SEI 7-10). ASCE, Reston, VAGoogle Scholar
  2. Apivatanagul P, Davidson R, Blanton B, Nozick L (2011) Long-term regional hurricane hazard analysis for wind and storm surge. Coast Eng 58:499–509CrossRefGoogle Scholar
  3. Atlantic Oceanographic and Meteorological Laboratory (AOML) (2013) HURDAT 2. AOML. Accessed 23 Aug 2013
  4. Berg RJ (2009) Tropical cyclone report: Hurricane Ike, 1–14 September 2008. National Hurricane Center, MiamiGoogle Scholar
  5. Blake ES, Landsea CW, Gibney EJ (2011) The deadliest, costliest and most intense United States tropical cyclones from 1851 to 2010 (and other frequently requested hurricane facts). NOAA Technical Memorandum NWS NHC-6, National Weather Service, National Hurricane Center, MiamiGoogle Scholar
  6. Blake ES, Kimberlain TB, Berg RJ, Cangialosi JP, Beven JL II (2013) Tropical cyclone report: Hurricane Sandy, 22–29 October 2012. National Hurricane Center, MiamiGoogle Scholar
  7. English EC, Friedland CJ, Orooji F (2017) Combined flood and wind mitigation for hurricane damage prevention: case for amphibious construction. J Struct Eng. Google Scholar
  8. Federal Emergency Management Agency (FEMA) (2012a) HAZUS-MH 2.1 hurricane model technical manual. FEMA, WashingtonGoogle Scholar
  9. Federal Emergency Management Agency (FEMA) (2012b) HAZUS-MH 2.1 flood model technical manual. FEMA, WashingtonGoogle Scholar
  10. Friedland CJ, Levitan ML (2011) Development of a loss-consistent wind and flood damage scale for residential buildings. Solutions to coastal disasters 2011. ASCE, Reston, pp 666–677CrossRefGoogle Scholar
  11. Georgiou PN (1985) Design wind speeds in tropical cyclone-prone regions. Dissertation, University of Western Ontario, London, Ontario, CanadaGoogle Scholar
  12. Harper BA, Kepert JD, Ginger JD (2008) Guidelines for converting between various wind averaging periods in tropical cyclone conditions. World Meteorological Organization, GenevaGoogle Scholar
  13. Jelesnianski CP, Chen J, Shaffer WA (1992) SLOSH: Sea, lake, and overland surges from hurricanes. NOAA Technical Report NWS 48, National Weather Service, Silver Spring, MDGoogle Scholar
  14. Knabb RD, Rhome JR, Brown DP (2005) Tropical cyclone report: Hurricane Katrina, 23–30 August 2005. National Hurricane Center, MiamiGoogle Scholar
  15. Lee K, Rosowsky D (2007) Synthetic hurricane wind speed records: development of a database for hazard analyses and risk studies. Nat Hazards Rev 8:23–34CrossRefGoogle Scholar
  16. Legg MR, Nozick LK, Davidson RA (2010) Optimizing the selection of hazard-consistent probabilistic scenarios for long-term regional hurricane loss estimation. Struct Saf 32:90–100CrossRefGoogle Scholar
  17. Li Y, van de Lindt JW, Dao T, Bjarnadottir S, Ahuja A (2012) Loss analysis for combined wind and surge in hurricanes. Nat Hazards Rev 13:1–10CrossRefGoogle Scholar
  18. Liu F (2014) Projections of future US design wind speeds due to climate change for estimating hurricane losses. Dissertation, Clemson University, Clemson, SCGoogle Scholar
  19. National Centers for Environmental Information (NCEI) (2017) U.S. billion-dollar weather and climate disasters. NCEI. Accessed 20 July 2017
  20. Pei B (2015) Hazard quantification and loss estimation for combined hurricane wind and flood. Dissertation, Clemson University, Clemson, SCGoogle Scholar
  21. Pei B, Pang W, Testik FY, Ravichandran N, Liu F (2014) Mapping joint hurricane wind and surge hazards for Charleston, South Carolina. Nat Hazards 74:375–403CrossRefGoogle Scholar
  22. Pei B, Pang W, Testik FY, Ravichandran N (2015) An agent-based framework for modeling the effectiveness of hurricane mitigation incentives. In: Proceedings of the 12th international conference on applications of statistics and probability in civil engineering (ICASP12), Vancouver, CanadaGoogle Scholar
  23. Phan LT, Simiu E, McInerney MA, Taylor AA, Glahn B, Powell MD (2007) Methodology for development of design criteria for joint hurricane wind speed and storm surge events: proof of concept. NIST Technical Note 1482, National Institute of Standards and Technology, Gaithersburg, MDGoogle Scholar
  24. Powell M, Soukup G, Cocke S, Gulati S, Morisseau-Leroy N, Hamid S, Dorst N, Axe L (2005) State of Florida hurricane loss projection model: atmospheric science component. J Wind Eng Ind Aerodyn 93:651–674CrossRefGoogle Scholar
  25. Rappaport E (1993) Preliminary report: Hurricane Andrew, 16–28 August 1992. National Hurricane Center, MiamiGoogle Scholar
  26. Simpson RH (1974) The hurricane disaster potential scale. Weatherwise 27:169CrossRefGoogle Scholar
  27. Smith AB, Katz RW (2013) US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases. Nat Hazards 67:387–410CrossRefGoogle Scholar
  28. Stewart SR (2004) Tropical cyclone report: Hurricane Ivan, 2–24 September 2004. National Hurricane Center, MiamiGoogle Scholar
  29. Taylor A (2011) Sea Lake and Overland Surge from Hurricanes (SLOSH). National Weather Service. Accessed 16 Sept 2012
  30. 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 67:52–61CrossRefGoogle Scholar
  31. Vickery PJ, Wadhera D (2008) Statistical models of Holland pressure profile parameter and radius to maximum winds of hurricanes from flight level pressure and H*Wind data. J Appl Meteorol 47:2497–2527CrossRefGoogle Scholar
  32. Vickery PJ, Skerlj PF, Steckley AC, Twisdale LA (2000a) Hurricane wind field model for use in hurricane simulations. J Struct Eng 126:1203–1221CrossRefGoogle Scholar
  33. Vickery PJ, Skerlj PF, Twisdale LA (2000b) Simulation of hurricane risk in the US using empirical track model. J Struct Eng 126:1222–1237CrossRefGoogle Scholar
  34. Vickery PJ, Lin J, Skerlj PF, Twisdale LA, Huang K (2006) HAZUS-MH hurricane model methodology. I: hurricane hazard, terrain, and wind load modeling. Nat Hazards Rev 7:82–93 (Special issue: Multihazards loss estimation and HAZUS) CrossRefGoogle Scholar
  35. Vickery PJ, Wadhera D, Powell MD, Chen Y (2009a) A hurricane boundary layer and wind field model for use in engineering applications. J Appl Meteorol Climatol 48:381–405CrossRefGoogle Scholar
  36. Vickery PJ, Wadhera D, Twisdale LA, Lavelle F (2009b) U.S. hurricane wind speed risk and uncertainty. J Struct Eng 135:301–320CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Impact ForecastingAon BenfieldChicagoUSA
  2. 2.Glenn Department of Civil EngineeringClemson UniversityClemsonUSA
  3. 3.Civil and Environmental Engineering DepartmentUniversity of Texas at San AntonioSan AntonioUSA
  4. 4.Applied Research AssociatesRaleighUSA

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