Natural Hazards

, Volume 76, Issue 2, pp 1143–1161 | Cite as

Hurricane event-based method to create regional hazard maps for heavy rainfall-induced translational landslides

  • Miguel A. Jaimes
  • Mauro Niño
  • Benjamín Huerta
Original Paper


This paper proposes a hurricane event-based method to construct a map for heavy rainfall-induced translational landslides. This method involves five steps: (1) construct a GIS-based geotechnical database; (2) identify the areas in the vicinity which are susceptible to translational landslides based on the slope of the terrain and the geological information available; (3) characterize the heavy rainfall hazard due to hurricanes (typhoon) as a set of collectively exhaustive and mutually exclusive stochastic events, that fully describe spatial distribution and annual frequency of occurrence (in accordance with storm category, storm position, distance between the eye of the storm and the site of interest, among others factors); (4) compute the ratio between the steady-state rainfall produced by a hurricane event affecting the tributary area of the slope under analysis, Q s, and the critical steady-state rainfall necessary to trigger slope instability, Q c, and finally; (5) carry out a probabilistic translational landslide hazard analysis to estimate the exceedance rate of a given ratio Q s/Q c. This method is applied to maps of Mexico for return periods of 50 and 150 years. The results shown in these maps are estimates of where the translational landslides may occur, and they should be useful to carry out local studies and to elaborate recommendations of site-specific hazard reduction plans as well as to calculate insurance rates. In addition to this, these results are useful for identifying actions of civil protection, regional and local risk management, and land use planning, as well as the promotion of more detailed vulnerability and risk studies on different scales.


Hurricane event-based methodology Translational landslide Landslide maps Hazard intensity Landslide hazard assessment 



We thank the Ministry of Finance (Secretaría de Hacienda y Crédito Público) for financing the project “System of quantification of losses, resource control and risk analysis for the FONDEN”.


  1. Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9:93–106CrossRefGoogle Scholar
  2. Akgun A, Türk N (2010) Landslide susceptibility mapping for Ayvalik, (Western Turkey) and its vicinity by multicriteria decision analysis. Environ Earth Sci 61:595–611CrossRefGoogle Scholar
  3. Alcántara-Ayala I (2008) On the historical account of disastrous landslides in Mexico: the challenge of risk management and disaster prevention. Adv Geosci 14:159–164CrossRefGoogle Scholar
  4. Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geol 81:432–445CrossRefGoogle Scholar
  5. Barredo JI, Benavides A, Hervàs J, van Westen CJ (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. Int J Appl Earth Obs Geoinf 2(1):9–23CrossRefGoogle Scholar
  6. Barton NR (1974) A review of the shear strength of filled discontinuities in rock. In: Norwegian Geotechnical Institute Publication No 105Google Scholar
  7. Brabb EE, Pampeyan EH, Bonilla MG (1972) Landslide susceptibility in San Mateo County, California. In: US Geological Survey Miscellaneous Field Studies Map, Scale 1: 62500Google Scholar
  8. Burroughs ER (1984) Landslide hazard rating for portions of the Oregon Coast Range: unpublished paper presented at symposium on effects of forest land use on erosion and slope stability, Honolulu, HawaiiGoogle Scholar
  9. Capra L, Lugo-Hubp J, Davila-Hernandez N (2003) Mass remotion phenomena in Zapotitlan de Mendez, Puebla: lithology and remotion type relationship. Mex J Geol Sci Inst Geol UNAM 20:95–106 (in Spanish)Google Scholar
  10. Cornell CA (1968) Engineering seismic risk analysis. Bull Seismol Soc Am 58:1583–1606Google Scholar
  11. Dietrich WE, Reiss R, Hsu M, Montgomery DR (1995) A process-based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrol Process 9:383–400CrossRefGoogle Scholar
  12. Esteva L (1968) Bases para la formulacion de decisiones de diseño sismico. Dissertation. National University of Mexico, Mexico CityGoogle Scholar
  13. Gonzalez L, Ferrer M, Ortuño L, Oteo C (2002) Geology engineering. Prentice Hall, Pearson Education, Upper Saddle RiverGoogle Scholar
  14. Gorsevski PV, Jankowski P, Gessler PE (2006) An heuristic approach for mapping landslide hazard by integrating fuzzy logic with analytic hierarchy process. Control Cybern 35(1):121–146Google Scholar
  15. Hasekioğullari GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards 63(2):1157–1179CrossRefGoogle Scholar
  16. Herrera S (2002) Regionalization of landslides in Mexico. Mexican Academy of Engineering, Mexico (in Spanish)Google Scholar
  17. Hoek E, Bray JW (1981) Rock slope engineering, 3rd edn. Institution of Mining and metallurgy, London, p 3Google Scholar
  18. INEGI (National Institute of Statistics and Geography), 2010. Scale 1:1000000
  19. Innes JL (1983) Debris flows. Prog Phys Geogr 7:469–501CrossRefGoogle Scholar
  20. Jaimes MA, Niño M, Reinoso E (2013) Una aproximación para la obtención de mapas de desplazamiento traslacional de laderas a nivel regional inducido por sismos. Revista de Ingeniería Sísmica 89:1–23 (in Spanish)Google Scholar
  21. Jibson RW, Harp EL, Michael JA (2000) A method for producing digital probabilistic seismic landslide hazard maps. Eng Geol 58:271–289CrossRefGoogle Scholar
  22. Kayastha P, Dhital MR, De Smedt F (2013) Evaluation and comparison of GIS based landslide susceptibility mapping procedures in Kulekhani watershed, Nepal. J Geol Soc India 81:219–231CrossRefGoogle Scholar
  23. Mondal S, Maiti R (2012) Landslide susceptibility analysis of Shiv-Khola watershed, Darjiling: a remote sensing and GIS based analytical hierarchy process (AHP). J Indian Soc Remote Sens 40(3):483–496CrossRefGoogle Scholar
  24. Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow landsliding. Water Resour Res 30(4):1153–1171CrossRefGoogle Scholar
  25. Montgomery DR, Sullivan K, Greenberg H (1998) Regional test of a model for shallow landsliding. Hydrol Process 12:943–955CrossRefGoogle Scholar
  26. Mora S, Vahrson WG (1993) A-priori assessment of landslides hazard in larger areas employing morphodynamic indices. Tecnol ICE 3:32–42 (in Spanish)Google Scholar
  27. Mora S, Vahrson WG (1994) Macrozonation methodology for landslide hazard determination. Bull As Eng Geol XXXI:49–58Google Scholar
  28. Moreiras SM (2005) Landslide susceptibility zonation in the Rio Mendoza Valley, Argentina. Geomorphology 66:345–357Google Scholar
  29. Naranjo JL, van Westen CJ, Soeters R (1994) Evaluating the use of training areas in bivariate statistical landslide hazard analysis: a case study in Colombia. ITC J 3:292–300Google Scholar
  30. Niño M, Jaimes MA, Reinoso E (2014) Seismic-event-based methodology to obtain earthquake induced translational landslide regional hazard maps. Nat Hazards 73(3):1697–1713Google Scholar
  31. O’Loughlin EM (1986) Prediction of surface saturation zones in natural catchments by topographic analysis. Water Resour Res 22:794–804CrossRefGoogle Scholar
  32. Oropeza O, Zamorano JJ, Ortı´z MA (1998) Geomorphological hazards in Mexico: mass remotion. Disasters in Mexico. Iberoamerican University, Mexico CityGoogle Scholar
  33. Reinoso E, Ordaz M, Huerta B, Zeballos A, Avelar C, Hernández J (2006) Metodología para el cálculo de pérdidas en edificios y naves industriales ante fenómenos hidrometereológicos ocurridos en México. XV Congreso Nacional de Ingeniería Estructural, Puerto VallartaGoogle Scholar
  34. Reneau SL, Dietrich WE (1987) Size and location of colluvial landslides in a steep forested landscape. In: Proceedings international symposium on erosion and sedimentation in the Pacific Rim, Corvallis, OR. International Association Hydrological Sciences Bulletin, vol 165, pp 39–48Google Scholar
  35. Rozos D, Bathrellos GD, Skillodimou HD (2011) Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: a case study from the Eastern Achaia County of Peloponnesus, Greece. Environ Earth Sci 63:49–63CrossRefGoogle Scholar
  36. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkGoogle Scholar
  37. Simpson RH, Riehl H (1981) The hurricane and its impact. Louisiana State University, Ba Ton RongeGoogle Scholar
  38. Van Westen CJ (1994) GIS in landslide hazard zonation: a review, with examples from the Andes of Colombia. In: Price M, Heywood I (eds) Mountain environments and geographic information systems. Taylor & Francis, Basingstoke, pp 135–165Google Scholar
  39. Wu CH, Chen SC (2009) Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method. Geomorphology 112:190–204CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Miguel A. Jaimes
    • 1
  • Mauro Niño
    • 1
  • Benjamín Huerta
    • 2
  1. 1.Instituto de IngenieríaUNAMDel. Coyoacán, MexicoMexico
  2. 2.ERNMexicoMexico

Personalised recommendations