Natural Hazards

, Volume 78, Issue 1, pp 355–391 | Cite as

Extreme storm surge hazard estimation in lower Manhattan

Clustered separated peaks-over-threshold simulation (CSPS) method
  • Madeleine LopemanEmail author
  • George Deodatis
  • Guillermo Franco
Original Paper


The coastal destruction wreaked by Hurricane Sandy in 2012 prompted motivation to estimate the event’s return period. The Clustered Separated Peaks-over-threshold Simulation (CSPS) method for estimating return periods uses a Monte Carlo simulation of storm surge activity based on statistics derived from tidal gauge data at the Battery, New York in lower Manhattan. The data are separated into three independent components (storm surge, tidal cycle and sea level rise) because different physical processes govern different components of water level. Peak storm surge heights are fit to the generalized Pareto distribution, chosen for its ability to fit a wide tail to limited data. The algorithm incorporates the evolution of storm surge over surge duration. The CSPS suggests that the return period of Hurricane Sandy’s peak water level is 103 years (95% confidence interval 38–452 years), significantly lower than previously published return periods. The estimated 100-year water level is 5.23 m above the station datum (or 3.39 m above the North American Vertical Datum of 1988, or 3.45 m above mean sea level). With 1 m of sea level rise (holding all other climatological conditions constant), this water level would become the 28-year event. Although the method’s exclusion of surge-tide interaction and its reliance on a 90-year tidal gauge time history may limit the reliability of high return period estimates, application of the CSPS method to lower Manhattan suggests that storm surge hazard in the New York Harbor has, until now, been underestimated.


Storm surge Natural hazards Extreme events Peaks-over-threshold Hurricane Sandy Climate change 


  1. Batstone C, Lawless M, Tawn J, Horsburgh K, Blackman D, McMillan A, Worth D, Laeger S, Hunt T (2013) A UK best-practice approach for extreme sea-level analysis along complex topographic coastlines. Ocean Eng 71:28–39CrossRefGoogle Scholar
  2. Bendat JS, Piersol AG (1986) Random data: analysis and measurement Procedures, 2nd edn. Wiley, New YorkGoogle Scholar
  3. Blake ES, Kimberlain TB, Berg RJ, Cangialosi JP, Beven II JL (2013) Tropical cyclone report: Hurricane Sandy (AL182012), 22–29 October 2012. Technical report, National Hurricane CenterGoogle Scholar
  4. Brabson BB, Palutikof JP (2000) Tests of the generalized pareto distribution for predicting extreme wind speeds. J Appl Meteorol (1988) 39(9):1627CrossRefGoogle Scholar
  5. Coles S, Simiu E (2003) Estimating uncertainty in the extreme value analysis of data generated by a hurricane simulation model. J Eng Mech 129(11):1288–1294CrossRefGoogle Scholar
  6. Coastal climate resilience (2013) Designing for flood risk. Technical report, New York City Planning Commission, New York, NY.
  7. Coles SG, Walshaw D (1994) Directional modelling of extreme wind speeds. J R Stat Soc Ser C (Appl Stat) 43(1):139–157Google Scholar
  8. Colle BA, Buonaiuto F, Bowman MJ, Wilson RE, Flood R, Hunter R, Mintz A, Hill D (2008) New York City’s vulnerability to coastal flooding. Bull Am Meteorol Soc 89(6):829–841CrossRefGoogle Scholar
  9. Cook NJ (1985) The designer’s guide to wind loading of building structures, part 1. Butterworths, London Google Scholar
  10. DuMouchel WH (1983) Estimating the stable index \(\alpha\) in order to measure tail thickness: a critique. Annal Stat 11(4):1019–1031Google Scholar
  11. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & Hall Inc, LondonCrossRefGoogle Scholar
  12. Fisher RA, Tippett LHC (1928) On the estimation of the frequency distributions of the largest or smallest member of a sample. Proc Cambridge Philoso Soc 24:180–190CrossRefGoogle Scholar
  13. Gnedenko BV (1943) Sur la distribution limite du terme maximum d’une serie aleatoire. Ann Math 44:423–453CrossRefGoogle Scholar
  14. Gornitz V, Couch S, Hartig EK (2001) Impacts of sea level rise in the New York City metropolitan area. Global Planet Change 32(1):61–88CrossRefGoogle Scholar
  15. Gusella V (1991) Estimation of extreme winds from short-term records. J Struct Eng 117(2):375–390CrossRefGoogle Scholar
  16. Hall TM, Sobel AH (2013) On the impact angle of Hurricane Sandy’s New Jersey landfall. Geophys Res Lett 40(10):2312–2315CrossRefGoogle Scholar
  17. Heaps NS (1983) Storm surges, 1967–1982. Geophys J Lond 74(1):331–376CrossRefGoogle Scholar
  18. Hicks SD (2006) Understanding tides. Technical report, U. S. Department of Commerce, National Oceanic and Atmospheric Administration, National Ocean ServiceGoogle Scholar
  19. Hoffman RN, Dailey P, Hopsch S, Ponte RM, Quinn K, Hill EM, Zachry B (2010) An estimate of increases in storm surge risk to property from sea level rise in the first half of the twenty-first century. Weather Clim Soc 2(4):271–293CrossRefGoogle Scholar
  20. Horton R, Gornitz V, Bowman M, Blake R (2010) Climate change adaptation in New York City: building a risk management response: New York City panel on climate change 2010 report. chapter 3: climate observations and projections. Ann N Y Acad Sci 1196(1):41–62CrossRefGoogle Scholar
  21. Intergovernmental Panel on Climate Change Fourth Assessment Report: Climate Change 2007: The AR4 Synthesis Report. IPCC, Geneva, 2007Google Scholar
  22. Jacob K, Deodatis G, Atlas J, Whitcomb M, Lopeman M, Markogiannaki O, Kennett Z, Morla A, Leichenko R, Vancura P (2011) Transportation. Ann N Y Acad Sci 1244(1):299–362Google Scholar
  23. Landsea CW (1993) A climatology of intense (or Major) Atlantic hurricanes. Mon Weather Rev 121(6):1703–1713CrossRefGoogle Scholar
  24. Lin N, Emanuel K, Oppenheimer M, Vanmarcke E (2012) Physically based assessment of hurricane surge threat under climate change. Nat Clim Change 2(6):462–467CrossRefGoogle Scholar
  25. Lin N, Emanuel KA, Smith JA, Vanmarcke E (2010) Risk assessment of hurricane storm surge for New York City. J Geophys Res Atmos 115(18). doi: 10.1029/2009JD013630
  26. Pickands J (1975) Statistical inference using extreme order statistics. Annal Stat 3(1):119–131CrossRefGoogle Scholar
  27. Powell M, Bowman D, Gilhousen D, Murillo S, Carrasco N, St. Fleur R (2004) Tropical cyclone winds at landfall: the ASOSC-MAN wind exposure documentation project. Bull Am Meteorol Soc 85(6):845–851CrossRefGoogle Scholar
  28. Prescott P, Walden AT (1980) Maximum likelihood estimation of the parameters of the generalized extreme-value distribution. Biometrika 67(3):723–724CrossRefGoogle Scholar
  29. Pugh DT, Vassie JM (1980) Applications of the joint probability method for extreme sea level computations. Proc Inst Civil Eng 69(4):959–975CrossRefGoogle Scholar
  30. Scarrott C, MacDonald A (2012) A review of extreme value threshold estimation and uncertainty quantification. Revstat Stat J 10(1):33Google Scholar
  31. Smith AB, Katz RW (2013) US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases. Nat Hazards 67(2):387–410CrossRefGoogle Scholar
  32. Smith RL, Weissman I (1994) Estimating the extremal index. J R Stat Soc Ser B (Methodol) 56(3):515–528Google Scholar
  33. Strutz T (2010) Data fitting and uncertainty (a practical introduction to weighted least squares and beyond). Vieweg+Teubner, GermanyGoogle Scholar
  34. Talke SA, Orton P, Jay DA (2014) Increasing storm tides in New York Harbor, 1844–2013. Geophys Res Lett 41(9):3149–3155 Google Scholar
  35. Tawn JA, Vassie JM (1989) Extreme sea levels—the joint probabilities method revisited and revised. Proceedings on institution of civil engineers. Part 2. Res Theory 87:429–442Google Scholar
  36. Tayfun MA (1979) Joint occurrences in coastal flooding. J Waterway Port Coast Ocean Eng ASCE 105(2):107Google Scholar
  37. Wolf J (2009) Coastal flooding: impacts of coupled wave-surge-tide models. Nat Hazard 49(2):241–260CrossRefGoogle Scholar
  38. Zervas C (2013) NOAA technical report NOS Co-OPS 067: extreme water levels of the United States 1893–2010. Technical report, National Oceanic and Atmospheric Administration/National Ocean Service/Center for Operational Oceanographic Products and Services, Silver Spring, MDGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Madeleine Lopeman
    • 1
    Email author
  • George Deodatis
    • 2
  • Guillermo Franco
    • 3
  1. 1.Columbia UniversityNew YorkUSA
  2. 2.Columbia UniversityNew YorkUSA
  3. 3.Guy Carpenter & Company Ltd.LondonUK

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