, Volume 13, Issue 6, pp 1405–1419 | Cite as

A new landslide-induced tsunami simulation model and its application to the 1792 Unzen-Mayuyama landslide-and-tsunami disaster

  • Kyoji SassaEmail author
  • Khang Dang
  • Hideaki Yanagisawa
  • Bin He
Original Paper


By combining landslide dynamics research and tsunami research, we present an integrated series of numerical models quantitatively simulating the complete evolution of a landslide-induced tsunami. The integrated model simulating the landslide initiation and motion uses measured landslide dynamic parameters from a high-stress undrained dynamic-loading ring shear apparatus. It provides the numerical data of a landslide mass entering and moving under water to the tsunami simulation model as the trigger of tsunami. The series of landslide and tsunami simulation models were applied to the 1792 Unzen-Mayuyama megaslide and the ensuing tsunami disaster, which is the largest landslide disaster, the largest volcanic disaster, and the largest landslide-induced tsunami disaster to have occurred in Japan. Both the 1792 megaslide and the tsunami portions of the disaster are well documented, making this an excellent test of the reliability and precision of the new simulation model. The simulated tsunami heights at the coasts well match the historical tsunami heights recorded by “Tsunami-Dome-Ishi” (a stone showing the tsunami reaching point) and memorial stone pillars.


Unzen-Mayuyama landslide Landslide-induced tsunami Undrained ring shear test Computer simulation 



The authors appreciate the cooperation from the Unzen Restoration Office of the Ministry of Land Infrastructure and Transport (MLIT) of Japan and the Unzen Volcanic Area Global Geopark Office, the Unzen Museum, and Prof. Kaoru Takara (Disaster Prevention Research Institute, Kyoto University, Japan) and Dr. Kimio Inoue (Sabo Frontier Foundation, Tokyo, Japan) for their investigation of the Unzen Mayuyama megaslide. We acknowledge Mr. Yuji Sato and the GODAI Development Cooperation, Kanazawa, Japan, for their cooperation in the development of the user-friendly software LS-Tsunami and LS-Motion.

Note: This simulation was conducted by a laptop computer (CPU @1.80 GHz, 2.39 GHz, RAM 8.00 GB) and a desk top computer (CPU 3.50 GHz, 4.00 GHz, RAM 64.0 GB).


  1. Abe I, Goto K, Imamura F, Shimizu K (2008) Numerical simulation of the tsunami generated by the 2007 Noto Hanto Earthquake and implications for unusual tidal surges observed in Toyama Bay. Earth Planets Space 60:133–138CrossRefGoogle Scholar
  2. Aoi S, Enescu B, Suzuki W, Asano Y, Obara K, Kunugi T, Shiomi K (2010) Stress transfer in the Tokai subduction zone from the 2009 Suruga Bay earthquake in Japan. Nat Geosci 3:496–500CrossRefGoogle Scholar
  3. Ataie-Ashtiani B, Yavari-Ramshe S (2011) Numerical simulation of wave generated by landslide incidents in dam reservoirs. Landslide 8(4):417–432CrossRefGoogle Scholar
  4. Baba T, Matsumoto H, Kashiwase K, Hyakudome T, Kaneda Y, Sano M (2012) Microbathymetric evidence for the effect of submarine mass movement on tsunami generation during the 2009 Suruga bay earthquake, Japan. In: Yamada et al. (eds.) Submarine mass movements and their consequences. Advances in Natural and Technological Hazards Research 31. Springer, pp. 485–494Google Scholar
  5. Biscarini C (2010) Computational fluid dynamics modelling of landslide generated water waves. Landslide 7(2):117–124CrossRefGoogle Scholar
  6. Fraser SA, Power WL, Wang X, Wallace LM, Mueler C, Johnston (2014) Tsunami inundation in Napier, New Zealand, due to local earthquake sources. Nat Hazards 70:415–445CrossRefGoogle Scholar
  7. Glimsdal S, L’Heureus JS, Harbitz CB, Pedersen GK (2013) Modelling of the 1888 Landslide Tsunami, Trondheim, Norway. Proc. the Second World Landslide Forum “Landslide Science and Practice”, Springer, Vol. 5, pp. 73-79Google Scholar
  8. Imamura F (2009) Chapter 10. Tsunami modeling: calculating inundation and hazard maps. In: Bernard EN, Robinson AR (eds) THE SEA Tsunamis. Harvard University Press, London, pp 321–332Google Scholar
  9. Inoue K (1999) Shimabara-Shigatsusaku Earthquake and topographic changes by Shimabara catastrophe. J Jpn Soc Erosion Control Eng 52(4):45–54 (in Japanese)Google Scholar
  10. Intergovernmental Oceanographic Commission (IOC) (1997) Numerical method of tsunami simulation with the leap-frog scheme. IUGG/IOC Time Project IOC Manuals and Guides, No.3, UNESCO. 126 pGoogle Scholar
  11. Linsley RK, Franzini JB (1979) Water Resource Engineering, 3rd edition, McGraw-HillGoogle Scholar
  12. Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Water and Disaster Management Bureau, Coastal management office (2012) Guideline for the assessment for Tsunami inundation area. Ver.1.00,
  13. Sassa K, Fukuoka H, Wang G, Ishikawa N (2004) Undrained dynamic-loading ring-shear apparatus and its application to landslide dynamics. Landslide 1(1):7–19CrossRefGoogle Scholar
  14. Sassa K, Nagai O, Solidum R, Yamazaki Y, Ohta H (2010) An integrated model simulating the initiation and motion of earthquake and rain induced rapid landslides and its application to the 2006 Leyte landslide. Landslide 7(3):219–236CrossRefGoogle Scholar
  15. Sassa K, He B, Miyagi T, Strasser M, Konagai K, Ostric M, Setiawan H, Takara K, Nagai O, Yamashiki Y, Tutumi S (2012) A hypothesis of the Senoumi submarine megaslide in Suruga Bay in Japan—based on the undrained dynamic-loading ring shear tests and computer simulation. Landslide 9(4):439–455CrossRefGoogle Scholar
  16. Sassa K, Dang K, He B, Takara K, Inoue K, Nagai O (2014a) A new high-stress undrained ring-shear apparatus and its application to the 1792 Unzen–Mayuyama megaslide in Japan. Landslide 11(5):827–842CrossRefGoogle Scholar
  17. Sassa K, Bin H, Dang K, Nagai O, Takara K (2014b ) Plenary: progress in landslide dynamics. Landslide science for a safer geoenvironment, Vol. 1, Springer, 37-67Google Scholar
  18. Satake K (2001) Tsunami modeling from submarine landslides. Proceedings of the International Tsunami Symposium, 665–674Google Scholar
  19. Tinti S, Tonini R (2013) The UBO-TSUFD tsunami inundation model: valiation and application to a tsunami case study focused on the city of Catania, Italy. NatHazards Earth Syst Sci 13:1795–1816CrossRefGoogle Scholar
  20. Tsuji Y, Hino T (1993) Damage and Inundation height of the 1792 Shimabara landslide tsunami along the coast of Kumamoto Prefecture. Bull. Earthquake. Res. Inst., University of Tokyo, Vol. 68: 91-176 (in Japanese)Google Scholar
  21. Tsuji Y, Murakami Y (1997) Inundation height of the 1792 Mayuyama landslide tsunami in the Shimabara Peninsula side. Historical Earthquake, No.13, Soc. of Historical Earthquake Studies, pp: 135-197Google Scholar
  22. Unzen Restoration Office of the Ministry of Land, Infrastructure and Transport of Japan (2002) The Catastrophe in Shimabara—1791–92 eruption of Unzen–Fugendake and the sector collapse of Mayu-Yama. An English leaflet (23 pages)Google Scholar
  23. Unzen Restoration Office of the Ministry of Land, Infrastructure and Transport of Japan (2003) The Catastrophe in Shimabara—1791–92 eruption of Unzen–Fugendake and the sector collapse of Mayu-Yama. A Japanese leaflet (44 pages)Google Scholar
  24. Usami T (1996) Materials for comprehensive list of destructive earthquakes in Japan. University of Tokyo Press (in Japanese)Google Scholar
  25. Yanagisawa H, Aoki A, Sassa K, Inoue K (2014) Numerical simulation of 1792 Ariake-Kai Tsunami using Landslide-Tsunami model. J Jpn Soc Civ Eng Ser B2 (Coast Eng) 70(2):151–155 (in Japanese)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Kyoji Sassa
    • 1
    Email author
  • Khang Dang
    • 1
    • 2
  • Hideaki Yanagisawa
    • 3
  • Bin He
    • 4
  1. 1.International Consortium on LandslidesSakyo-kuJapan
  2. 2.VNU University of Science, Vietnam National UniversityThanh XuanVietnam
  3. 3.Department of Regional Design, Faculty of Liberal ArtsTohoku Gakuin UniversitySendaiJapan
  4. 4.State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and LimnologyChinese Academy of ScienceNanjingPeople’s Republic of China

Personalised recommendations