Articles | Volume 26, issue 1
https://doi.org/10.5194/nhess-26-531-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-531-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantifying the influence of coastal flood hazards on building habitability following Hurricane Irma
Benjamin Nelson-Schmitz
CORRESPONDING AUTHOR
Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
Tessa Swanson
Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, USA
Seth Guikema
Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, USA
Jeremy Bricker
CORRESPONDING AUTHOR
Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Related authors
No articles found.
Hanqing Xu, Elisa Ragno, Sebastiaan N. Jonkman, Jun Wang, Jeremy D. Bricker, Zhan Tian, and Laixiang Sun
Hydrol. Earth Syst. Sci., 28, 3919–3930, https://doi.org/10.5194/hess-28-3919-2024, https://doi.org/10.5194/hess-28-3919-2024, 2024
Short summary
Short summary
A coupled statistical–hydrodynamic model framework is employed to quantitatively evaluate the sensitivity of compound flood hazards to the relative timing of peak storm surges and rainfall. The findings reveal that the timing difference between these two factors significantly affects flood inundation depth and extent. The most severe inundation occurs when rainfall precedes the storm surge peak by 2 h.
Cited articles
Akaike, H.: A new look at the statistical model identification, IEEE T. Automatic Control, 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974.
Asher, T. G. and Luettich Jr., R. A.: A hindcast of coastal flooding from hurricane Irma, Ocean Model., 197, 102582, https://doi.org/10.1016/j.ocemod.2025.102582, 2025.
Cangialosi, J. P., Latto, A. S., and Berg, R.: National Hurricane Center tropical cyclone report – Hurricane Irma, National Hurricane Center, 111 pp., https://www.nhc.noaa.gov/data/tcr/AL112017_Irma.pdf (last access: 30 October 2025), 2021.
Charvet, I., Suppasri, A., Kimura, H., Sugawara, D., and Imamura, F.: A multivariate generalized linear tsunami fragility model for Kesennuma City based on maximum flow depths, velocities and debris impact, with evaluation of predictive accuracy, Nat. Hazards, 79, 2073–2099, https://doi.org/10.1007/s11069-015-1947-8, 2015.
Deltares: D-Flow Flexible Mesh User Manual, Version: 2022.02, SVN Revision: 75614, https://content.oss.deltares.nl/delft3d/D-Flow_FM_User_Manual.pdf (last access: 1 May 2025), 2022a.
Deltares: D-Waves User Manual, Version: 1.2, SVN Revision: 75624, https://content.oss.deltares.nl/delft3d/D-Waves_User_Manual.pdf (last access: 1 May 2025), 2022b.
Demuth, J. L., DeMaria, M., and Knaff, J. A.: Improvement of advanced microwave sounding unit tropical cyclone intensity and size estimation algorithms, J. Appl. Meteorol. Clim., 45, 1573–1581, https://doi.org/10.1175/JAM2429.1, 2006.
De Risi, R., Goda, K., Yasuda, T., and Mori, N.: Is flow velocity important in tsunami empirical fragility modeling?, Earth-Sci. Rev., 166, 64–82, https://doi.org/10.1016/j.earscirev.2016.12.015, 2017.
Dewitz, J. and USGS: National Land Cover Database (NLCD) 2019 Products (ver. 3.0, February 2024), U.S. Geological Survey data release [data set], https://doi.org/10.5066/P9KZCM54, 2024.
Diaz Loaiza, M. A., Bricker, J. D., Meynadier, R., Duong, T. M., Ranasinghe, R., and Jonkman, S. N.: Development of damage curves for buildings near La Rochelle during storm Xynthia based on insurance claims and hydrodynamic simulations, Nat. Hazards Earth Syst. Sci., 22, 345–360, https://doi.org/10.5194/nhess-22-345-2022, 2022.
Dobbelaere, T., Curcic, M., Le Hénaff, M., and Hanert, E.: Impacts of Hurricane Irma (2017) on wave-induced ocean transport processes, Ocean Model., 171, 101947, https://doi.org/10.1016/j.ocemod.2022.101947, 2022.
Egbert, G. D. and Erofeeva, S. Y.: Efficient inverse modeling of barotropic ocean tides, J. Atmos. Ocean. Tech., 19, 183–204, https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002.
FEMA: Hazus Flood Model Technical Manual – Hazus 6.1, https://www.fema.gov/sites/default/files/documents/fema_hazus-flood-model-technical-manual-6-1.pdf (last access: 30 October 2025), 2024a.
FEMA: Hazus Hurricane Model Technical Manual – Hazus 6.1, https://www.fema.gov/flood-maps/tools-resources/flood-map-products/hazus/documentation (last access: 30 October 2025), 2024b.
Fothergill, A. and Peek, L. A.: Poverty and disasters in the United States: a review of recent sociological findings, Nat. Hazards, 32, 89–110, https://doi.org/10.1023/B:NHAZ.0000026792.76181.d9, 2004.
GEBCO: Gridded Bathymetry Data, General Bathymetric Chart of the Oceans [data set], https://www.gebco.net/data-products/gridded-bathymetry-data (last access: 1 May 2025), 2023.
Gori, A., Lin, N., Xi, D., and Emanuel, K.: Tropical cyclone climatology change greatly exacerbates US extreme rainfall–surge hazard, Nat. Clim. Change, 12, 171–178, https://doi.org/10.1038/s41558-021-01272-7, 2022.
Hallegatte, S., Vogt-Schilb, A., Rozenberg, J., Bangalore, M., and Beaudet, C.: From poverty to disaster and back: a review of the literature, Economics of Disasters and Climate Change, 4, 223–247, https://doi.org/10.1007/s41885-020-00060-5, 2020.
Hodge, T. and Lee, A.: Hurricane Irma cut power to nearly two-thirds of Florida's electricity customers, U.S. Energy Information Administration, https://www.eia.gov/todayinenergy/detail.php?id=32992 (last access: 16 October 2024), 2017.
Holland, G.: A revised hurricane pressure–wind model, Mon. Weather Rev., 136, 3432–3445, https://doi.org/10.1175/2008MWR2395.1, 2008.
Holland, G., Belanger, J. I., and Fritz, A.: A revised model for radial profiles of hurricane winds, Mon. Weather Rev., 138, 4393–4401, https://doi.org/10.1175/2010MWR3317.1, 2010.
Hughes, W. and Zhang, W.: Evaluation of post-disaster home livability for coastal communities in a changing climate, Int. J. Disast. Risk Re., 96, 103951, https://doi.org/10.1016/j.ijdrr.2023.103951, 2023.
Hydrologic Engineering Center: HEC-RAS 2D User's Manual, https://www.hec.usace.army.mil/confluence/rasdocs/r2dum/latest (last access: 1 May 2025), 2021.
Issa, A., Ramadugu, K., Mulay, P., Hamilton, J., Siegel, V., Harrison, C., Campbell, C. M., Blackmore, C., Bayleyegn, T., and Boehmer, T.: Deaths related to Hurricane Irma – Florida, Georgia, and North Carolina, September 4–October 10, 2017, MMWR-Morbid. Mortal. W., 67, 829–832, https://doi.org/10.15585/mmwr.mm6730a5, 2018.
Joyce, B. R., Gonzalez-Lopez, J., Van der Westhuysen, A. J., Yang, D., Pringle, W. J., Westerink, J. J., and Cox, A. T.: U.S. IOOS coastal and ocean modeling testbed: hurricane-induced winds, waves, and surge for deep ocean, reef-fringed islands in the Caribbean, J. Geophys. Res.-Oceans, 124, 2876–2907, https://doi.org/10.1029/2018JC014687, 2019.
Landsea, C. W. and Franklin, J. L.: Atlantic hurricane database uncertainty and presentation of a new database format, Mon. Weather Rev., 141, 3576–3592, https://doi.org/10.1175/MWR-D-12-00254.1, 2013.
Li, Y., Chen, Q., Kelly, D. M., and Zhang, K.: Hurricane Irma simulation at South Florida using the parallel CEST model, Front. Clim., 3, https://doi.org/10.3389/fclim.2021.609688, 2021.
Loos, S., Lallemant, D., Khan, F., McCaughey, J. W., Banick, R., Budhathoki, N., and Baker, J. W.: A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters, Commun. Earth Environ., 4, 1–12, https://doi.org/10.1038/s43247-023-00699-4, 2023.
Luppichini, M., Favalli, M., Isola, I., Nannipieri, L., Giannecchini, R., and Bini, M.: Influence of topographic resolution and accuracy on hydraulic channel flow simulations: case study of the Versilia River (Italy), Remote Sensing, 11, 1630, https://doi.org/10.3390/rs11131630, 2019.
Mendelsohn, R., Emanuel, K., Chonabayashi, S., and Bakkensen, L.: The impact of climate change on global tropical cyclone damage, Nat. Clim. Change, 2, 205–209, https://doi.org/10.1038/nclimate1357, 2012.
Mitsova, D., Esnard, A-M., Sapat, A., and Lai, B. S.: Socioeconomic vulnerability and electric power restoration timelines in Florida: The case of Hurricane Irma, Nat. Hazards, 94, 689–709, https://doi.org/10.1007/s11069-018-3413-x, 2018.
Muñoz, D. F., Moftakhari, H., and Moradkhani, H.: Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models, Hydrol. Earth Syst. Sci., 28, 2531–2553, https://doi.org/10.5194/hess-28-2531-2024, 2024.
Musinguzi, A., Reddy, L., and Akbar, M. K.: Evaluation of wave contributions in Hurricane Irma storm surge hindcast. Atmos., 13, 404, https://doi.org/10.3390/atmos13030404, 2022.
Neumann, B., Vafeidis, A. T., Zimmermann, J., and Nicholls, R. J.: Future coastal population growth and exposure to sea-level rise and coastal flooding – a global assessment, PLOS ONE, 10, e0118571, https://doi.org/10.1371/journal.pone.0118571, 2015.
NOAA NCEI: Digital Elevation Models Global Mosaic (Elevation Values), NOAA NCEI [data set], https://noaa.maps.arcgis.com/home/item.html?id=c876e3c96a8642ab8557646a3b4fa0ff (last access: 1 May 2025), 2022.
Nofal, O., Rosenheim, N., Kameshwar, S., Patil, J., Zhou, X., van de Lindt, J. W., Duenas-Osorio, L., Cha, E. J., Endrami, A., Sutley, E., Cutler, H., Lu, T., Wang, C., and Jeon, H.: Community-level post-hazard functionality methodology for buildings exposed to floods, Comput.-Aided Civ. Inf., 39, 1099–1122, https://doi.org/10.1111/mice.13135, 2024.
Nofal, O. M., van de Lindt, J. W., and Do, T. Q.: Multi-variate and single-variable flood fragility and loss approaches for buildings, Reliab. Eng. Syst. Safe., 202, 106971, https://doi.org/10.1016/j.ress.2020.106971, 2020.
Paprotny, D., Kreibich, H., Morales-Nápoles, O., Wagenaar, D., Castellarin, A., Carisi, F., Bertin, X., Merz, B., and Schröter, K.: A probabilistic approach to estimating residential losses from different flood types, Nat. Hazards, 105, 2569–2601, https://doi.org/10.1007/s11069-020-04413-x, 2021.
Paul, N., Galasso, C., and Baker, J.: Household displacement and return in disasters: a review, Nat. Hazards Rev., 25, 03123006, https://doi.org/10.1061/NHREFO.NHENG-1930, 2024.
Pistrika, A. K. and Jonkman, S. N.: Damage to residential buildings due to flooding of New Orleans after Hurricane Katrina, Nat. Hazards, 54, 413–434, https://doi.org/10.1007/s11069-009-9476-y, 2010.
Schwarz, G.: Estimating the dimension of a model, The Annals of Statistics, 6, 461–464, 1978.
Sheather, S. J.: Diagnostics and transformations for multiple linear regression, in: A Modern Approach to Regression with R, edited by: Sheather, S., Springer, New York, NY, 151–225, https://doi.org/10.1007/978-0-387-09608-7_6, 2009.
Smith, A. B.: U.S. billion-dollar weather and climate disasters, 1980–present, NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/stkw-7w73, 2020.
Smith, S. D. and Banke, E. G.: Variation of the sea surface drag coefficient with wind speed, Q. J. Royal Meteor. Soc., 101, 665–673, https://doi.org/10.1002/qj.49710142920, 1975.
Suppasri, A., Mas, E., Charvet, I., Gunasekera, R., Imai, K., Fukutani, Y., Abe, Y., and Imamura, F.: Building damage characteristics based on surveyed data and fragility curves of the 2011 Great East Japan tsunami, Nat. Hazards, 66, 319–341, https://doi.org/10.1007/s11069-012-0487-8, 2013.
Swanson, T.: Towards new measures of resilience: leveraging location based services data for evaluating hazard-induced changes in access to essential services and community recovery, PhD thesis, University of Michigan, https://doi.org/10.7302/22240, 2023.
Swanson, T. and Guikema, S.: Using mobile phone data to evaluate access to essential services following natural hazards, Risk Anal., 44, 883–906, https://doi.org/10.1111/risa.14201, 2024.
Thieken, A. H., Müller, M., Kreibich, H., and Merz, B.: Flood damage and influencing factors: new insights from the August 2002 flood in Germany, Water Resour. Res., 41, https://doi.org/10.1029/2005WR004177, 2005.
Tomiczek, T., Kennedy, A., and Rogers, S.: Survival analysis of elevated homes on the Bolivar Peninsula after Hurricane Ike, Advances in Hurricane Engineering, ASCE, 108–118, https://doi.org/10.1061/9780784412626.010, 2013.
Tsubaki, R., Bricker, J. D., Ichii, K., and Kawahara, Y.: Development of fragility curves for railway embankment and ballast scour due to overtopping flood flow, Nat. Hazards Earth Syst. Sci., 16, 2455–2472, https://doi.org/10.5194/nhess-16-2455-2016, 2016.
Washington, V., Guikema, S., Mondisa, J., and Misra, A.: A data-driven method for identifying the locations of hurricane evacuations from mobile phone location data, Risk Anal., 44, 390–407, https://doi.org/10.1111/risa.14188, 2024.
Woodruff, J. D., Irish, J. L., and Camargo, S. J.: Coastal flooding by tropical cyclones and sea-level rise, Nature, 504, 44–52, https://doi.org/10.1038/nature12855, 2013.
Wu, J.: Wind-stress coefficients over sea surface from breeze to hurricane, J. Geophys. Res., 87, 9704–9706, https://doi.org/10.1029/JC087iC12p09704, 1982.
Xie, L., Bao, S., Pietrafesa, L. J., Foley, K., and Fuentes, M.: A real-time hurricane surface wind forecasting model: formulation and verification, Mon. Weather Rev., 134, 1355–1370, https://doi.org/10.1175/MWR3126.1, 2006.
Xu, C., Nelson-Mercer, B. T., Bricker, J. D., Davlasheridze, M., Ross, A. D., and Jia, J.: Damage curves derived from Hurricane Ike in the West of Galveston Bay based on insurance claims and hydrodynamic simulations, Int. J. Disast. Risk Sc., 14, 932–946, https://doi.org/10.1007/s13753-023-00524-8, 2023.
Yabe, T., Tsubouchi, K., Fujiwara, N., Sekimoto, Y., and Ukkusuri, S. V.: Understanding post-disaster population recovery patterns, J. R. Soc. Interface, 17, 20190532, https://doi.org/10.1098/rsif.2019.0532, 2020.
Short summary
Habitability functions are developed to estimate the probability of a building becoming uninhabitable due to coastal flooding. These functions are created by combining a Hurricane Irma flood model with cell phone data showing which buildings people returned to following Irma. We find that unit discharge is the best predictor of building habitability. By quantifying the dependence of building habitability on flood hazards, this work improves how coastal communities prepare for flood events.
Habitability functions are developed to estimate the probability of a building becoming...
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