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Spatially explicit mapping of hurricane risk in New England, USA using ArcGIS

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Abstract

Hurricanes are one of the major natural disturbances affecting human livelihoods in coastal zones worldwide. Assessing hurricane risk is an important step toward mitigating the impact of tropical storms on human life and property. This study uses NOAA’s historical tropical cyclone database (HURDAT or ‘best-track’), geographic information systems, and kernel smoothing techniques to generate spatially explicit hurricane risk maps for New England. Southern New England had the highest hurricane risk across the region for all storm intensities. Long Island, western Connecticut, western Massachusetts, and southern Cape Cod, Martha’s Vineyard, and Nantucket had high storm probabilities and wind speeds. Results from this study suggest that these locations may be of central importance for focusing risk amelioration resources along the Long Island and New England coastlines. This paper presents a simple methodology for hurricane risk assessment that could be applied to other regions where long-term spatial storm track data exist.

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References

  • Boose ER, Foster DR, Fluet M (1994) Hurricane impacts to tropical and temperate forest landscapes. Ecol Monogr 64:369–400

    Article  Google Scholar 

  • Comaniciu D (2003) An algorithm for data-driven bandwidth selection. IEEE Trans Pattern Anal Mach Intell 25(2):281–288

    Article  Google Scholar 

  • Elsner JB, Jagger TH (2004) A hierarchical bayesian approach to seasonal hurricane modeling. J Climate 17:2813–2827

    Article  Google Scholar 

  • Elsner JB, Jagger TH (2006) Prediction models for annual U.S. hurricane counts. J Climate 19:2935–2952

    Article  Google Scholar 

  • Elsner JB, Murnane RJ, Jagger TH (2006) Forecasting U.S. hurricanes 6 months in advance. Geophys Res Lett 33:L10704

    Article  Google Scholar 

  • Elsner JB, Jagger TH, Dickinson M, Rowe D (2008) Improving multisession forecasts of North Atlantic hurricane activity. J Climate 21:1209–1219

    Article  Google Scholar 

  • ESRI ArcMap version 9.3 (2008) ESRI Inc. Redlands, CA

    Google Scholar 

  • Foster DR (1988a) Species and stand response to catastrophic wind in central New England. USA J Ecol 76:135–151

    Article  Google Scholar 

  • Foster DR (1988b) Disturbance history, community organization, and vegetation dynamics of the old-growth Pisgah forest, southwestern New Hampshire. USA J Ecol 76:105–134

    Article  Google Scholar 

  • Hall TM, Jewson S (2007) Statistical modeling of North Atlantic tropical cyclone tracks. Tellus 59A:486–498

    Google Scholar 

  • Hall TM, Jewson S (2008) Comparison of local and basin-wide methods for risk assessment of tropical cyclone landfall. J Appl Meteorol Climatol 47:361–367

    Article  Google Scholar 

  • Hall P, Sheather SJ, Jones MC, Marron JS (1991) On optimal data-based bandwidth selection in kernel density estimation. Biometrika 78:263–270

    Article  Google Scholar 

  • Houston SH, Shaffer WA, Powell MD, Chen J (1999) Comparisons of HRD and SLOSH surface wind fields in hurricanes: implications from storm surge modeling. Weather Forecast 14:671–686

    Article  Google Scholar 

  • Izenman AJ (1991) Recent developments in nonparametric density estimation. J Am Stat Assoc 86:205–224

    Article  Google Scholar 

  • Jagger T, Elnser JB, Niu X (2001) A dynamic probability model of hurricane winds in coastal counties of the United States. Appl Stat 40:853–863

    Google Scholar 

  • James MK, Mason LB (2005) Synthetic tropical cyclone database. J Waterw Port Coastal Ocean Eng 131:181–192

    Article  Google Scholar 

  • Jarvinen BR, Neumann CJ, Davis MAS (1984) A tropical cyclone data tape for the north Atlantic basin, 1886–1983. Contens, limitations and uses. NOAA Tech Memo, NWS NHC 22, Miami

    Google Scholar 

  • Jones MC, Marron JS, Sheather SJ (1996) A brief survey of bandwidth selection for kernel estimation. J Am Stat Assoc 91:401–407

    Article  Google Scholar 

  • Landsea CW (2007) Counting Atlantic tropical cyclones back to 1900. Eos Trans AGU 88(18):197–202

    Article  Google Scholar 

  • Landsea CW, Anderson C, Charles N, Clark G, Dunion J, Fernandez-Partagas J, Hungerford P, Neumann C, Zimmer M (2004) The Atlantic hurricane database reanalysis project: documentation for the 1851–1910 alterations and additions to the HURDAT database. In: Murnane RJ, Liu K-B (eds) Hurricanes and typhoons: past, present and future. Columbia University Press, New York, pp 177–221

    Google Scholar 

  • Landsea CW, Glenn DA, Bredemeyer W, Chenoweth M, Ellis R, Gamache J, Hufstetler L, Mock C, Perez R, Prieto R, Sánchez-Sesma J, Thomas D, Woolcock L (2008) A reanalysis of the 1911–1920 Atlantic Hurricane database. J Climate 21:2138–2168

    Article  Google Scholar 

  • Li Q, Racine J (2004) Cross-validated local linear nonparametric regression. Statistica Sinnica 14:485–512

    Google Scholar 

  • Marron JS (1987) A comparison of cross-validation techniques in density estimation. Ann Stat 15:152–162

    Article  Google Scholar 

  • Mercado A (1994) On the use of NOAA’s storm surge model, SLOSH, in managing coastal hazards- the experience in Puerto Rico. Nat Haz 10:235–246

    Article  Google Scholar 

  • Neumann CJ, Jarvinen BR, McAdie CJ, Elms JD (1999) Tropical cyclones of the North Atlantic Ocean, 1871–1998. Hist Climatol Ser, 6–2, NCDC, Asheville, N. C

  • Partagas JF, Diaz HF (1995) A reconstruction of historical tropical cyclone frequency in the Atlantic from documentary, other historical sources 1851 to 1880. Parts I and II: 1871–1880. Climate Diagnostics Center NOAA, Boulder

    Google Scholar 

  • Partagas JF, Diaz HF (1996) A reconstruction of historical tropical cyclone frequency in the Atlantic from documentary and other historical sources. Parts I-IV: 1881–1890. Climate Diagnostics Center, NOAA, Boulder

    Google Scholar 

  • Peltola H (2006) Mechanical stability of trees under static loads. Am J Bot 93(10):1501–1511

    Article  Google Scholar 

  • Poulos HM, Camp AE (In review) Mapping threats to power line corridors for connecticut right-of-way management. Env Manage

  • R Development Core Team (2009) A language and environment for statistical computing Version 2.10.0. R Foundation for statistical computing, Vienna, Austria. Available [online] at: http: www.r-project.org. Accessed 12/28/09

  • Rumpf J, Weindl H, Höppe P, Rauch E, Schmidt V (2007) Stochastic modeling of tropical cyclone tracks. Math Meth Oper Res 66(3):475–490

    Article  Google Scholar 

  • Rumpf J, Weindl H, Höppe P, Rauch E, Schmidt V (2009) Tropical cyclone hazard assessment using model-based track simulation. Nat Hazards 48:383–398

    Article  Google Scholar 

  • Sain SR, Baggerly KA, Scott DW (1994) Cross-validation of multivariate densities. J Am Stat Assoc 89:807–817

    Article  Google Scholar 

  • Scott DW (1992) Multivariate density estimation: theory, practice, and visualization. John Wiley, New York

    Book  Google Scholar 

  • Seaman DE, Powell RA (1996) An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology 77:2075–2085

    Article  Google Scholar 

  • Shimazaki H, Shinomoto S (2009) Kernel bandwidth optimization in spike rate estimation. J Comput Neruosci. doi: 10.1007/s10827-009-0180-4

  • Silverman BW (1986) Density estimation for statistics and data analysis. Chapman & Hall, New York

    Google Scholar 

  • Simonoff JS (1996) Smoothing methods in statistics. Springer-Verlag, New York

    Google Scholar 

  • Vickery PJ, Skerlj PF, Twisdale LA (2000) Simulation of hurricane risk in the U.S. using empirical track model. J Struct Eng 126(10):1222–1237

    Article  Google Scholar 

  • Vickery PJ, Masters FJ, Powell MD, Wadhera D (2009) Hurricane hazard modelling: the past, present, and future. J WindEng Ind Aerodyn 97(9):392–405

    Article  Google Scholar 

  • Wand MP, Jones MC (1995) Kernel Smoothing. Chapman & Hall, New York

    Google Scholar 

  • Xu W, Zhu L (2009) Kernel-based generalized cross-validation in non-parametric mixed-effect models. Scand J Stat 36(2):229–247

    Article  Google Scholar 

  • Zhang X, King ML, Hyndman RJ (2004) Bandwidth selection for multivariate kernel density estimation using MCMC. Monash econometrics and business statistics working papers 9/04, Monash University, Department of Economics Business Statistics

  • Zhang X, King ML, Hyndman RJ (2006) Bandwidth selection for multivariate kernel density estimation using MCMC. Computational Stat Data Anal 50(11):3009–3031

    Article  Google Scholar 

  • Zhang K, Xiao C, Shen J (2008) Comparison of the CEST and SLOSH models for storm. J Coast Res 24(2):489–499

    Article  Google Scholar 

Download references

Acknowledgments

The author wishes to thank Professor Ann Camp of the Yale School of Forestry and Environmental Studies and Tony Johnson of Northeast Utilities for assisting with this project. Funding for this research was provided by Northeast Utilities.

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Correspondence to H. M. Poulos.

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Poulos, H.M. Spatially explicit mapping of hurricane risk in New England, USA using ArcGIS. Nat Hazards 54, 1015–1023 (2010). https://doi.org/10.1007/s11069-010-9502-0

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