Hurricane flood risk assessment for the Yucatan and Campeche State coastal area

  • Wilmer ReyEmail author
  • E. Tonatiuh Mendoza
  • Paulo Salles
  • Keqi Zhang
  • Yi-Chen Teng
  • Miguel A. Trejo-Rangel
  • Gemma L. Franklin
Original Paper


In this study, the first ever Sea, Lake, Overland Surges from Hurricanes (SLOSH) grid was built for the Yucatan Peninsula. The SLOSH model was used to simulate storm surges in the coastal area of the states of Yucatan and Campeche (Mexico). Based on climatology, more than 39,900 hypothetical hurricanes covering all possible directions of motion were synthesized. The storm intensity (category), forward speed, radius of maximum winds and the tide anomaly were varied for each hypothetical track. According to these scenarios, the potential storm surge and associated inundation threat were computed. Subsequently, the Maximum Envelope of Water (MEOW) and the Maximum of the MEOWs (MOMs) were calculated to assess the flood hazard induced by tropical cyclones under varying conditions. In addition, for each MOM, the socioeconomic vulnerability aspects were taken into account in order to assess the hurricane flood risk for the states of Yucatan and Campeche. Results show that the most vulnerable areas are the surroundings of Terminos lagoon, Campeche City and its neighboring areas in the state of Campeche. For Yucatan, the towns located in the Northwest (Celestun, Hunucma and Progreso) and the eastern part of the state presented the highest risk values. The methodology used in this study can be applied to other coastal zones of Mexico as well as places with similar attributes. Furthermore, the MEOW and MOM are very useful as a decision-making tool for prevention, preparedness, evacuation plans, mitigation of the flood hazard and its associated risk, and also for insurance companies.


Flood modeling Flood hazard Flood vulnerability Flood risk MEOW Yucatan Peninsula 



W.R. was supported by a doctoral scholarship (CVU 308,087) from the Mexican National Council for Science and Technology (CONACYT), and from the Council of the National Research Training (COLCIENCIAS). This research was supported by the Coordinación de Estudios de Posgrado and Posgrado de Ingeniería of the UNAM, FOBESII, CONACYT INFR projects 2014-01-115561, 252354 and 271544, as well as Engineering Institute project 5341. W.R. thanks Jamie Rhome and Keqi Zhang for the opportunity to carry out an internship at the National Hurricane Center in collaboration with the International Hurricane Research Center-IHRC as part of his Ph.D. research. Cody Fritz and Tarah M. Sharon provided assistance in setting up the SLOSH grid and modeling the inundation threat, respectively. The first author also wishes to acknowledge contributions from Brian C. Zachry and Cristina Forbes for their suggestions on validating the SLOSH model and ideas for improving this research as well as to DHI Water and Environment for facilitating a student license of MIKE 21 hydrodynamic model. Niels Van Kuik and Pablo Ruíz Salcines are acknowledged for reviewing and making suggestions to enhance this paper, and Gonzalo U. Martín-Ruiz for computational support.


  1. Alcántara-Ayala I (2002) Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries. Geomorphology 47:107–124. CrossRefGoogle Scholar
  2. Amante C, Eakins BW (2009) ETOPO1 1 arc-minute global relief model: procedures, data sources and analysis. In: NOAA technical memorandum NESDIS NGDC-24. National Hurricane Center, Boulder, p 19Google Scholar
  3. Andersen OB (1995) Global ocean tides from ERS 1 and TOPEX/POSEIDON altimetry. J Geophys Res 100:25249–25259CrossRefGoogle Scholar
  4. Appendini CM, Hernández-Lasheras J, Meza-Padilla R, Kurczyn JA (2018) Effect of climate change on wind waves generated by anticyclonic cold front intrusions in the Gulf of Mexico. Clim Dyn 0:1–17. Google Scholar
  5. Arakawa A, Lamb VR (1977) Computational design of the basic dynamical processes of the UCLA general circulation model. In: Chang J (ed) Methods of computational physics, vol 17. Academic Press, New York, Ny, USA, pp 173–265Google Scholar
  6. Balica SF, Popescu I, Beevers L, Wright NG (2013) Parametric and physically based modelling techniques for flood risk and vulnerability assessment: a comparison. Environ Model Softw 41:84–92. CrossRefGoogle Scholar
  7. Bronstert A (2003) Floods and climate change: interactions and impacts. Risk Anal 23:545–557. CrossRefGoogle Scholar
  8. Cenapred (2006) Guía básica para la elaboración de atlas estatales y municipales de peligros y riesgo: Fenómenos hidrometeorológicos. Secretaría de Gobernación, México, D.F., p 140Google Scholar
  9. Chen W, Cutter SL, Emrich CT, Shi P (2013) Measuring social vulnerability to natural hazards in the Yangtze River Delta region, China. Int J Disaster Risk Sci 4:169–181. CrossRefGoogle Scholar
  10. Chen J, Chen J, Liao A et al (2015) Global land cover mapping at 30 m resolution: a POK-based operational approach. ISPRS J Photogramm Remote Sens 103:7–27. CrossRefGoogle Scholar
  11. Chowdhury JU, Karim MF (1996) A risk-based zoning of storm surge prone area of the Ganges Tidal plans. J Civ Eng Inst Eng Bangladesh 24:221–233Google Scholar
  12. Clark GE, Moser SC, Ratick SJ et al (1998) Assessing the vulnerability of coastal communities to extreme storms: the case of revere, MA, USA. Mitig Adapt Strateg Glob Change 3:59–82CrossRefGoogle Scholar
  13. Cuevas-Jiménez A, Euán-Ávila J (2009) Morphodynamics of carbonate beaches in the Yucatán Peninsula. Ciencias Mar 35:307–319CrossRefGoogle Scholar
  14. Cutter SL, Barnes L, Berry M et al (2008) A place-based model for understanding community resilience to natural disasters. Glob Environ Change 18:598–606. CrossRefGoogle Scholar
  15. Cutter SL, Emrich CT, Morath DP, Dunning CM (2013) Integrating social vulnerability into federal flood risk management planning. J Flood Risk Manag 6:332–344. CrossRefGoogle Scholar
  16. DHI (2014) Mike 21 flow model FM: hydrodynamic module, user guide. DHI Water & Environment, Hoersholm, p 134Google Scholar
  17. Di Risio M, Bruschi A, Lisi I et al (2017) Comparative analysis of coastal flooding vulnerability and hazard assessment at national scale. J Mar Sci Eng 5:51. CrossRefGoogle Scholar
  18. Dinh Q, Balica S, Popescu I, Jonoski A (2012) Climate change impact on flood hazard, vulnerability and risk of the Long Xuyen Quadrangle in the Mekong Delta Climate change impact on flood hazard, vulnerability and risk of the Long Xuyen. Int J River Basin Manag 10:103–120. CrossRefGoogle Scholar
  19. Dorrestein R (1961) Wave set-up on a beach. In: Proceedings of 2nd technical conference on Hurricanes, Miami Beach, FL. National Hurricane Research Project 50. US Department of Commerce, pp 230–241Google Scholar
  20. DY (1988) El Diario de Yucatan. Copies from 16 to 30 of September. Yucatanense Library, MeridaGoogle Scholar
  21. Emanuel K (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436:686–688. CrossRefGoogle Scholar
  22. Emanuel K, Ravela S, Vivant E, Risi C (2006) A statistical deterministic approach to hurricane risk assessment. Bull Am Meteorol Soc 87:299–314. CrossRefGoogle Scholar
  23. Emanuel K, Sundararajan R, Williams J (2008) Hurricanes and global warming: results from downscaling IPCC AR4 simulations. Bull Am Meteorol Soc 89:347–367. CrossRefGoogle Scholar
  24. Fernandez P, Mourato S, Moreira M (2016) Social vulnerability assessment of flood risk using GIS-based multicriteria decision analysis. A case study of Vila Nova de Gaia (Portugal). Geomat Nat Hazards Risk 7:1367–1389. CrossRefGoogle Scholar
  25. Flather RA (2001) Storm Surges. In: Steele JH, Thorpe SA, Turekian KK (eds) Encyclopedia of ocean sciences. Academic, San Diego, pp 2882–2892CrossRefGoogle Scholar
  26. Forbes C, Rhome J, Mattocks C, Taylor A (2014) Predicting the storm surge threat of hurricane sandy with the national weather service SLOSH model. Mar Sci Eng 2:437–476. CrossRefGoogle Scholar
  27. Gallopín GC (2006) Linkages between vulnerability, resilience, and adaptive capacity. Glob Environ Change 16:293–303. CrossRefGoogle Scholar
  28. INEGI (2010) Censo de Población y Vivienda 2010. In: INEGI. Accessed 25 May 2017
  29. IPET (2009) Performance evaluation of the New Orleans and Southeast Louisiana hurricane protection system, vol 1. Executive summary and overview. US Army Corps of Engineers, Washington, DCGoogle Scholar
  30. Jarvinen BR, Neumann CJ, Davis MAS (1984) A tropical cyclone data tape for the North Atlantic basin, 1886–1983: contents, limitations, and uses. In: NOAA Technical Memo NWS NHC 22. Miami, Fla. National Hurricane Center, p 21Google Scholar
  31. Jelesnianski CP (1970) “Bottom stress time-history” in linearized equations of motion for storm surges. Mon Weather Rev 98:462–478CrossRefGoogle Scholar
  32. Jelesnianski C, Chen J, Shaffer W (1992) SLOSH: sea, lake, and overland surges from hurricanes. NOAA Technical Report NWS 48, United States Department Commerce NOAA/AOML Library, MiamiGoogle Scholar
  33. Jelesniansky CP (1967) Numerical computations of storm surges with bottom stress. Mon Weather Rev 95:740–756.;2 CrossRefGoogle Scholar
  34. Jenks GF (1963) Generalization in statistical mapping. Ann As Am Geogr 53:15–26. CrossRefGoogle Scholar
  35. Kim SC, Chen J (1999) Bottom stress of wind-driven currents over an inner shelf determined from depth-integrated storm surge model. J Coast Res 15:766–773Google Scholar
  36. Knutson TR (2015) Tropical cyclones and hurricanes|Tropical cyclones and climate change, 2nd edn. Elsevier, AmsterdamGoogle Scholar
  37. Knutson TR, McBride JL, Chan J et al (2010) Tropical cyclones and climate change. Nat Geosci 3:157–163. CrossRefGoogle Scholar
  38. Komac B, Zorn M, Kušar D (2012) New possibilities for assessing the damage caused by natural disasters in Slovenia—the case of the Real Estate Record. Geogr Vestn 84:113–127Google Scholar
  39. Krauss KW, Doyle TW, Doyle TJ et al (2009) Water level observations in mangrove swamps during two hurricanes in Florida. Wetlands 29:142–149. CrossRefGoogle Scholar
  40. Lin N, Chavas D (2012) On hurricane parametric wind and applications in storm surge modeling. J Geophys Res Atmos 117:1–19. CrossRefGoogle Scholar
  41. Lin N, Emanuel KA, Smith JA, Vanmarcke E (2010) Risk assessment of hurricane storm surge for New York City. J Geophys Res 115:1–11. Google Scholar
  42. Lin N, Emanuel K, Oppenheimer M, Vanmarcke E (2012) Physically based assessment of hurricane surge threat under climate change. Nat Clim Change 2:462–467. CrossRefGoogle Scholar
  43. Lin N, Lane P, Emanuel KA et al (2014) Heightened hurricane surge risk in northwest Florida revealed from climatological-hydrodynamic modeling and paleorecord reconstruction. J Geophys Res Atmos 119:8606–8623. CrossRefGoogle Scholar
  44. Longuet-Higgins MS, Stewart R (1963) A note on wave set-up. J Mar Res 21:4–10Google Scholar
  45. Martínez-Graña AM, Boski T, Goy JL et al (2016) Coastal-flood risk management in central Algarve: vulnerability and flood risk indices (South Portugal). Ecol Indic 71:302–316. CrossRefGoogle Scholar
  46. Massey WG, Gangai JW, Drei-Horgan E, Slover KJ (2007) History of coastal inundation models. Mar Technol Soc J 41:7–17. CrossRefGoogle Scholar
  47. Merz B, Thieken AH, Gocht M (2007) Flood risk mapping at the local scale: concepts and challenges. In: Begum S, Stive MJF, Hall J (eds) Advances in natural and technological hazards research. Springer, Dordrecht, pp 231–251Google Scholar
  48. Merz B, Hall J, Disse M, Schumann A (2010) Fluvial flood risk management in a changing world. Nat Hazards Earth Syst Sci 10:509–527CrossRefGoogle Scholar
  49. Meza-Padilla R, Appendini CM, Pedrozo-Acuña A (2015) Hurricane-induced waves and storm surge modeling for the Mexican coast. Ocean Dyn 65:1199–1211. CrossRefGoogle Scholar
  50. Morrow BH, Lazo JK, Rhome J, Feyen J (2015) Improving storm surge risk communication: stakeholder perspectives. Bull Am Meteorol Soc 96:35–48. CrossRefGoogle Scholar
  51. Nageswara Rao K, Subraelu P, Rao TV et al (2008) Sea-level rise and coastal vulnerability: an assessment of Andhra Pradesh coast, India through remote sensing and GIS. J Coast Conserv 12:195–207. CrossRefGoogle Scholar
  52. NHC (2014a) Storm surge overview. Accessed 5 May 2017
  53. NHC (2014b) Storm surge maximum envelope of water (MEOW). Accessed 5 May 2017
  54. NHC (2014c) Storm surge maximum of the maximum (MOM). Accessed 5 May 2017
  55. Nkwunonwo U, Whitworth M, Baily B (2015) Relevance of social vulnerability assessment to flood risk reduction in the Lagos Metropolis of Nigeria. Br J Appl Sci Technol 8:366–382. CrossRefGoogle Scholar
  56. Nott J, Green C, Townsend I, Callaghan J (2014) The world record storm surge and the most intense southern hemisphere tropical cyclone: new evidence and modeling. Bull Am Meteorol Soc 95:757–765. CrossRefGoogle Scholar
  57. Ojeda E, Appendini CM, Mendoza ET (2017) Storm-wave trends in Mexican waters of the Gulf of Mexico and Caribbean Sea. Nat Hazards Earth Syst Sci 17:1305–1317. CrossRefGoogle Scholar
  58. Patro S, Chatterjee C, Mohanty S et al (2009) Flood inundation modeling using MIKE FLOOD and remote sensing data. J Indian Soc Remote Sens 37:107–118. CrossRefGoogle Scholar
  59. Penning-Rowsell E, Fordham M, Correia F et al (1994) Flood hazard assessment, modelling and management: results from the EUROflood project. In: Penning-Rowsell E, Fordham M (eds) Floods across Europe: flood hazard assessment, modelling and management. University Press, London, pp 37–72Google Scholar
  60. Plate EEJ (2002) Flood risk and flood management. J Hydrol 267:2–11. CrossRefGoogle Scholar
  61. Platzman G (1963) The dynamic prediction of wind tides on Lake Erie. Meteorol Monogr Am Meteorol Soc 4:44Google Scholar
  62. Posada-Vanegas G, Durán-Valdez G, Silva-Casarin R et al (2011) Vulnerability to coastal flooding induced by tropical cyclones. In: Smith JM, Lynett P (eds) Coastal engineering proceedings. Shanghai, China, p 14Google Scholar
  63. Rey W, Salles P, Mendoza ET et al (2018) Assessment of coastal flooding and associated hydrodynamic processes on the Southeast coast of Mexico, during Central American Cold Surge events. Nat Hazards Earth Syst Sci 18:1681–1701. CrossRefGoogle Scholar
  64. Rosengaus-Moshinsky M, Jiménez-Espinosa M, Vázquez-Conde MT (2002) Atlas climatológico de ciclones tropicales en México. Centro Nacional de Prevención de Desastres, Instituto Mexicano de Tecnología del Agua, Ciudad de México, p 108Google Scholar
  65. Ruol P, Martinelli L, Favaretto C (2018) Vulnerability analysis of the venetian littoral and adopted mitigation strategy. Water 10:984. CrossRefGoogle Scholar
  66. Shaffer WA, Jelesniansky CP, Chen J (1989) Hurricane storm surge forecasting. In: Preprints, 11th conference on probability and statistics on atmospheric science. American Meteorology Society, Monterrey, pp 53–58Google Scholar
  67. Silva SF, Martinho M, Capitão R et al (2017) An index-based method for coastal-flood risk assessment in low-lying areas (Costa de Caparica, Portugal). Ocean Coast Manag 144:90–104. CrossRefGoogle Scholar
  68. Sleigh PA, Gaskell PH, Berzins M, Wright NG (1998) An unstructured finite volume algorithm for predicting flow in rivers and estuaries. Comput Fluids 27:479–508. CrossRefGoogle Scholar
  69. Stringfield VT, LeGrand HE (1974) Karst hydrology of Northern Yucatan Peninsula, Mexico. In: Weidie AE (ed) Proceedings of field seminar on water and carbonate rocks of the Yucatan Peninsula, Mexico. New Orleans Geological Society, New Orleans, pp 192–210Google Scholar
  70. Taylor A, Glahn B (2008) Probabilistic guidance for hurricane storm surge. In: Proceedings of the 88th annual meeting of the American Meteorological Society, New Orleans, pp 1–8Google Scholar
  71. Taylor A, Myckow A, Fritz A, et al (2013) Recent developments in probabilistic hurricane storm surge (P-Surge 2.0). In: Proceedings of the estuarine and coastal modeling conference XIII, ECM13, San DiegoGoogle Scholar
  72. Tingsanchali T, Karim MF (2005) Flood hazard and risk analysis in the southwest region of Bangladesh. Hydrol Process 19:2055–2069. CrossRefGoogle Scholar
  73. UNISDR (2009) UNISDR-terminology on disaster risk reduction. UNISDR, Geneva, p 30Google Scholar
  74. Whittingham H (1958) The Bathurst Bay Hurricane and associated storm surge. Aust Met Mag 23:14–36Google Scholar
  75. Wind HG, Nierop TM, De Blois CJ, De Kok JL (1999) Analysis of flood damages from the 1993 and 1995 Meuse floods. Water Resour Res 35:3459–3465. CrossRefGoogle Scholar
  76. WMO (2006) World meteorological organization’s world weather and climate extremes archive. Accessed 19 May 2017
  77. Zachry BC, Booth WJ, Rhome JR, Sharon TM (2015) A national view of storm surge risk and inundation. Weather Clim Soc 7:109–117. CrossRefGoogle Scholar
  78. Zhang K, Xiao C, Shen J (2008) Comparison of the CEST and SLOSH models for storm surge flooding comparison of the CEST and SLOSH models for storm. J Coast Res 24:489–499. CrossRefGoogle Scholar
  79. Zhang K, Liu H, Li Y et al (2012) The role of mangroves in attenuating storm surges. Estuar Coast Shelf Sci 102–103:11–23. CrossRefGoogle Scholar
  80. Zhang K, Li Y, Liu H et al (2013) Transition of the coastal and estuarine storm tide model to an operational storm surge forecast model: a case study of the Florida Coast. Weather Forecast 28:1019–1037. CrossRefGoogle Scholar
  81. Zhao DH, Shen HW, Tabios GQ et al (1994) Finite-volume two-dimensional unsteady-flow model for River Basins. J Hydraul Eng 120:863–883. CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Dirección General Marítima, Centro de Investigaciones Oceanográficas e Hidrográficas del Caribe, Barrio Bosque, Sector Manzanillo Escuela NavalCartagena de IndiasColombia
  2. 2.Laboratorio de Ingeniería y Procesos Costeros, Instituto de IngenieríaUNAMSisalMexico
  3. 3.Laboratorio Nacional de Resiliencia CosteraCONACyTMéridaMexico
  4. 4.International Hurricane Research CenterFlorida International UniversityMiamiUSA
  5. 5.Graduate Institute of Hydrological and Oceanic SciencesNational Central UniversityTaoyuanTaiwan
  6. 6.Michael Baker InternationalHamiltonUSA

Personalised recommendations