Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Giant tsunami monitoring, early warning and hazard assessment

Nature Reviews Earth & Environment volume 3, pages 557–572 (2022)Cite this article

Subjects

Abstract

Earthquake-triggered giant tsunamis can cause catastrophic disasters to coastal populations, ecosystems and infrastructure on scales over thousands of kilometres. In particular, the scale and tragedy of the 2004 Indian Ocean (about 230,000 fatalities) and 2011 Japan (22,000 fatalities) tsunamis prompted global action to mitigate the impacts of future disasters. In this Review, we summarize progress in understanding tsunami generation, propagation and monitoring, with a particular focus on developments in rapid early warning and long-term hazard assessment. Dense arrays of ocean-bottom pressure gauges in offshore regions provide real-time data of incoming tsunami wave heights, which, combined with advances in numerical and analogue modelling, have enabled the development of rapid tsunami forecasts for near-shore regions (within 3 minutes of an earthquake in Japan). Such early warning is essential to give local communities time to evacuate and save lives. However, long-term assessments and mitigation of tsunami risk from probabilistic tsunami hazard analysis are also needed so that comprehensive disaster prevention planning and structural tsunami countermeasures can be implemented by governments, authorities and local populations. Future work should focus on improving tsunami inundation, damage risk and evacuation modelling, and on reducing the uncertainties of probabilistic tsunami hazard analysis associated with the unpredictable nature of megathrust earthquake occurrence and rupture characteristics.

Key points

  • The scale and tragedy of the 2004 Indian Ocean Tsunami and the 2011 Tohoku Tsunami prompted the widespread deployment of tsunami observation networks and the development of tsunami modelling, which have enabled tsunami early warning systems to approach near-real-time inundation forecasts, based on the dense arrays of offshore observation data.

  • Earthquake magnitude alone does not characterize the size or impact of the ensuing tsunami disaster. The tsunami source (such as earthquake location and rupture characteristics), coastal geomorphic features, and exposure of densely populated areas have key roles in tsunami behaviour, inundation extent and the level of impact.

  • Probabilistic tsunami hazard assessment (PTHA) is a recently developed method of considering the variability of tsunami conditions for risk mitigation. PTHA can be used in engineering design and to draw up tsunami inundation maps at different return period levels, which can be used to plan local and regional hazard mitigation.

  • To mitigate future tsunami risks, we must be able to reproduce the inundation depth and flow velocity of tsunamis that run up to urban areas. A combination of numerical and physical models is needed to better understand the complex interactions between building layouts, structures, debris and non-hydrostatic flow.

  • Long-term tsunami assessments will inform authorities about requirements for software and hardware countermeasures. Hardware or structural measures (such as sea walls) can reduce loss of life and assets during an event, whereas software or non-structural measures (such as evaluation, assessments and planning) can reduce loss of life.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of tsunami generation, propagation, early warning and long-term assessment.
Fig. 2: Historical giant tsunamis.
Fig. 3: Ocean-bottom pressure monitoring network in Japan.
Fig. 4: Components of earthquake occurrence and rupture models.
Fig. 5: Hierarchy of length scales for tsunami simulations.
Fig. 6: Multi-hazard assessments combine risks from earthquake and tsunami hazards.
Fig. 7: Evacuation assessments for urban environments under different tsunami scenarios.

Similar content being viewed by others

References

  1. Talley, H. C. & Cloud, W. K. United States Earthquakes, 1960 (US Geological Survey, 1984).

  2. Satake, K. & Fujii, Y. Source models of the 2011 Tohoku earthquake and long-term forecast of large earthquakes. J. Disaster Res. 9, 272–280 (2014).

    Article  Google Scholar 

  3. Uchida, N. & Bürgmann, R. A decade of lessons learned from the 2011 Tohoku–Oki earthquake. Rev. Geophys. 59, e2020RG000713 (2021).

    Article  Google Scholar 

  4. International Federation of Red Cross and Red Crescent Societies (IFRC). World Disasters Report 2005: Focus on Information in Disasters (Kumarian Press, 2005).

  5. Hayes, G. P., Earle, P. S., Benz, H. M., Wald, D. J. & Briggs, R. W. 88 hours: the US Geological Survey National Earthquake Information Center response to the 11 March 2011 Mw 9.0 Tohoku earthquake. Seismol. Res. Lett. 82, 481–493 (2011).

    Article  Google Scholar 

  6. Wirth, E. A., Sahakian, V. J., Wallace, L. M. & Melnick, D. The occurrence and hazards of great subduction zone earthquakes. Nat. Rev. Earth Environ. 3, 125–140 (2022).

    Article  Google Scholar 

  7. Ide, S., Baltay, A. & Beroza, G. C. Shallow dynamic overshoot and energetic deep rupture in the 2011 Mw 9.0 Tohoku–Oki earthquake. Science 332, 1426–1429 (2011).

    Article  Google Scholar 

  8. Rudloff, A., Lauterjung, J., Münch, U. & Tinti, S. Preface “The GITEWS project (German-Indonesian Tsunami Early Warning System)”. Natural Haz. Earth Syst. Sci. 9, 1381–1382 (2009).

    Article  Google Scholar 

  9. Meinig, C., Stalin, S. E., Nakamura, A. I. & Milburn, H. B. Real-time Deep-ocean Tsunami Measuring, Monitoring, And Reporting System: The NOAA DART II Description And Disclosure 1–15 (NOAA, 2005).

  10. Mochizuki, M. et al. S-net project: performance of a large-scale seafloor observation network for preventing and reducing seismic and tsunami disasters. In OCEANS 2018-MTS/IEEE Kobe Techno-Oceans 1–4 (IEEE, 2018); https://ieeexplore.ieee.org/abstract/document/8558823.

  11. Araki, E., Kawaguchi, K., Kaneko, S. & Kaneda, Y. Design of deep ocean submarine cable observation network for earthquakes and tsunamis. In OCEANS 2008-MTS/IEEE Kobe Techno-Oceans 1–4 (IEEE, 2008); https://ieeexplore.ieee.org/document/4531071.

  12. Selva, J. et al. Probabilistic tsunami forecasting for early warning. Nat. Commun. 12, 5677 (2021).

    Article  Google Scholar 

  13. Selva, J. Long-term multi-risk assessment: statistical treatment of interaction among risks. Natural Haz. 67, 701–722 (2013).

    Article  Google Scholar 

  14. User’s guide for the Pacific Tsunami Warning Center enhanced products for the Pacific Tsunami Warning System. In International Oceanographic Commission Technical Series Vol. 105 (IOC, 2015): https://unesdoc.unesco.org/ark:/48223/pf0000220368.

  15. Whitmore, P. M. & Sokolowski, T. J. Predicting tsunami amplitudes along the North American coast from tsunamis generated in the northwest Pacific Ocean during tsunami warnings. Sci. Tsunami Haz. 14, 147–166 (1996).

    Google Scholar 

  16. Synolakis, C. E., Bernard, E. N., Titov, T. T., Kânoğlu, U. & González, F. I. Standards, criteria, and procedures for NOAA evaluation of tsunami numerical models. In NOAA Technical Memorandum OAR PMEL-135 (NOAA/Pacific Marine Environmental Laboratory, 2007).

  17. Aoi, S. et al. MOWLAS: NIED observation network for earthquake, tsunami and volcano. Earth Planets Space 72, 126 (2020).

    Article  Google Scholar 

  18. Earthquakes and tsunamis — disaster prevention and mitigation efforts. In JMA Brochure (Japan Meteorological Agency, 2021); https://www.jma.go.jp/jma/kishou/books/jishintsunami/en/jishintsunami_en.pdf.

  19. Kato, T., Terada, Y., Nishimura, H., Nagai, T. & Koshimura, S. Tsunami records due to the 2010 Chile earthquake observed by GPS buoys established along the Pacific coast of Japan. Earth Planets Space 63, e5–e8 (2011).

    Article  Google Scholar 

  20. Kawai, H., Satoh, M., Kawaguchi, K. & Seki, K. Characteristics of the 2011 Tohoku tsunami waveform acquired around Japan by NOWPHAS equipment. Coast. Engin. J. 55, 1350008 (2013).

    Article  Google Scholar 

  21. Mulia, I. E. & Satake, K. Developments of tsunami observing systems in Japan. Front. Earth Sci. 8, 145 (2020).

    Article  Google Scholar 

  22. Kaneda, Y. et al. Development and application of an advanced ocean floor network system for megathrust earthquakes and tsunamis. In Seafloor Observatories 643–662 (Springer, 2015).

  23. Tsushima, H., Hino, R., Fujimoto, H., Tanioka, Y. & Imamura, F. Near-field tsunami forecasting from cabled ocean bottom pressure data. J. Geophys. Res. Solid Earth 114, B06309 (2009).

    Article  Google Scholar 

  24. Tsushima, H., Hino, R., Ohta, Y., Iinuma, T. & Miura, S. tFISH/RAPiD: rapid improvement of near-field tsunami forecasting based on offshore tsunami data by incorporating onshore GNSS data. Geophys. Res. Lett. 41, 3390–3397 (2014).

    Article  Google Scholar 

  25. Maeda, T., Obara, K., Shinohara, M., Kanazawa, T. & Uehira, K. Successive estimation of a tsunami wavefield without earthquake source data: a data assimilation approach toward real-time tsunami forecasting. Geophys. Res. Lett. 42, 7923–7932 (2015).

    Article  Google Scholar 

  26. Gusman, A. R. et al. Tsunami data assimilation of Cascadia seafloor pressure gauge records from the 2012 Haida Gwaii earthquake. Geophys. Res. Lett. 43, 4189–4196 (2016).

    Article  Google Scholar 

  27. Wang, Y. & Satake, K. Real-time tsunami data assimilation of S-net pressure gauge records during the 2016 Fukushima earthquake. Seismol. Res. Lett. 92, 2145–2155 (2021).

    Article  Google Scholar 

  28. Wang, Y., Tsushima, H., Satake, K. & Navarrete, P. Review on recent progress in near-field tsunami forecasting using offshore tsunami measurements: source inversion and data assimilation. Pure Appl. Geophys. https://doi.org/10.1007/s00024-021-02910-z (2021).

    Article  Google Scholar 

  29. Mori, N., Goda, K. & Cox, D. T. Recent process in probabilistic tsunami hazard analysis (PTHA) for megathrust subduction earthquakes. In The 2011 Japan Earthquake and Tsunami: Reconstruction and Restoration (eds Santiago-Fandiño, V., Sato, S., Maki, N. & Iuchi, K.) 469–485 (Springer, 2017).

  30. Davies, G. et al. A global probabilistic tsunami hazard assessment from earthquake sources. Geol. Soc. Lond. Spec. Publ. 456, 219–244 (2018).

    Article  Google Scholar 

  31. Behrens, J. et al. Probabilistic tsunami hazard and risk analysis: a review of research gaps. Front. Earth Sci. 9, 114 (2021).

    Article  Google Scholar 

  32. Geist, E. L. & Parsons, T. Probabilistic analysis of tsunami hazards. Natural Haz. 37, 277–314 (2006).

    Article  Google Scholar 

  33. Grezio, A. et al. Probabilistic tsunami hazard analysis: multiple sources and global applications. Rev. Geophys. 55, 1158–1198 (2017).

    Article  Google Scholar 

  34. Davies, G. & Griffin, J. Sensitivity of probabilistic tsunami hazard assessment to far-field earthquake slip complexity and rigidity depth-dependence: case study of Australia. Pure Appl. Geophys. 177, 1521–1548 (2020).

    Article  Google Scholar 

  35. Tinti, S. & Armigliato, A. The use of scenarios to evaluate the tsunami impact in southern Italy. Mar. Geol. 199, 221–243 (2003).

    Article  Google Scholar 

  36. Baptista, M. A., Miranda, J. M., Omira, R. & Antuns, C. Potential inundation of Lisbon downtown by a 1755-like tsunami. Natural Haz. Earth Syst. Sci. 11, 3319–3326 (2011).

    Article  Google Scholar 

  37. Goda, K. Time-dependent probabilistic tsunami hazard analysis using stochastic rupture sources. Stoch. Environ. Res. Risk Assess. 33, 341–358 (2019).

    Article  Google Scholar 

  38. Geist, E. L. Complex earthquake rupture and local tsunamis. J. Geophys. Res. Solid. Earth 107, ESE–2 (2002).

    Article  Google Scholar 

  39. Melgar, D., Williamson, A. L. & Salazar-Monroy, E. F. Differences between heterogenous and homogenous slip in regional tsunami hazards modelling. Geophys. J. Int. 219, 553–562 (2019).

    Google Scholar 

  40. Løvholt, F. et al. Stochastic analysis of tsunami runup due to heterogeneous coseismic slip and dispersion. J. Geophys. Res. Ocean 117, C03047 (2012).

    Article  Google Scholar 

  41. Davies, G., Horspool, N. & Miller, V. Tsunami inundation from heterogeneous earthquake slip distributions: evaluation of synthetic source models. J. Geophys. Res. Solid Earth 120, 6431–6451 (2015).

    Article  Google Scholar 

  42. Mueller, C., Power, W., Fraser, S. & Wang, X. Effects of rupture complexity on local tsunami inundation: implications for probabilistic tsunami hazard assessment by example. J. Geophys. Res. Solid Earth 120, 488–502 (2015).

    Article  Google Scholar 

  43. Park, H. & Cox, D. T. Probabilistic assessment of near-field tsunami hazards: Inundation depth, velocity, momentum flux, arrival time, and duration applied to Seaside, Oregon. Coast. Eng. 117, 79–96 (2016).

    Article  Google Scholar 

  44. Sepúlveda, I., Liu, P. L. & Grigoriu, M. Probabilistic tsunami hazard assessment in South China Sea with consideration of uncertain earthquake characteristics. J. Geophys. Res. Solid Earth 124, 658–688 (2019).

    Article  Google Scholar 

  45. Goda, K. Multi-hazard portfolio loss estimation for time-dependent shaking and tsunami hazards. Front. Earth Sci. 8, 592444 (2020).

    Article  Google Scholar 

  46. Walton, M. et al. Toward an integrative geological and geophysical view of Cascadia subduction zone earthquakes. Ann. Rev. Earth Planet. Sci. 49, 367–398 (2021).

    Article  Google Scholar 

  47. Ogata, Y. Estimating the hazard of rupture using uncertain occurrence times of paleoearthquakes. J. Geophys. Res. Solid Earth 104, 17995–18014 (1999).

    Article  Google Scholar 

  48. Sykes, L. R. & Menke, W. Repeat times of large earthquakes: implications for earthquake mechanics and long-term prediction. Bull. Seismol. Soc. Am. 96, 1569–1596 (2006).

    Article  Google Scholar 

  49. Field, E. H. & Jordan, T. H. Time-dependent renewal-model probabilities when date of last earthquake is unknown. Bull. Seismol. Soc. Am. 105, 459–463 (2015).

    Article  Google Scholar 

  50. Shimazaki, K. & Nakata, T. Time-predictable recurrence model for large earthquakes. Geophys. Res. Lett. 7, 279–282 (1980).

    Article  Google Scholar 

  51. Kiremidjian, A. S. & Anagnos, T. Stochastic slip-predictable model for earthquake occurrences. Bull. Seismol. Soc. Am. 74, 739–755 (1984).

    Article  Google Scholar 

  52. Cornell, A. C. & Winterstein, S. R. Temporal and magnitude dependence in earthquake recurrence models. Bull. Seismol. Soc. Am. 78, 1522–1537 (1988).

    Google Scholar 

  53. Matthews, M. V., Ellsworth, W. L. & Reasenberg, P. A. A Brownian model for recurrent earthquakes. Bull. Seismol. Soc. Am. 92, 2233–2250 (2002).

    Article  Google Scholar 

  54. Abaimov, S. G. et al. Earthquakes: recurrence and interoccurrence times. In Earthquakes: Simulations, Sources and Tsunamis (eds Tiampo, K. F., Weatherley, D. K. & Weinstein, S. A.) 777–795 (Birkhäuser, 2008).

  55. Ceferino, L., Kiremidjian, A. & Deierlein, G. Probabilistic space- and time-interaction modeling of mainshock earthquake rupture occurrence. Bull. Seismol. Soc. Am. 110, 2498–2518 (2020).

    Article  Google Scholar 

  56. Mai, P. M. & Thingbaijam, K. K. SRCMOD: An online database of finite-fault rupture models. Seismol. Res. Lett. 85, 1348–1357 (2014).

    Article  Google Scholar 

  57. Blaser, L., Krüger, F., Ohrnberger, M. & Scherbaum, F. Scaling relations of earthquake source parameter estimates with special focus on subduction environment. Bull. Seismol. Soc. Am. 100, 2914–2926 (2010).

    Article  Google Scholar 

  58. Leonard, M. Earthquake fault scaling: self-consistent relating of rupture length, width, average displacement, and moment release. Bull. Seismol. Soc. Am. 100, 1971–1988 (2010).

    Article  Google Scholar 

  59. Strasser, F. O., Arango, M. C. & Bommer, J. J. Scaling of the source dimensions of interface and intraslab subduction-zone earthquakes with moment magnitude. Seismol. Res. Lett. 81, 941–950 (2010).

    Article  Google Scholar 

  60. Murotani, S., Satake, K. & Fujii, Y. Scaling relations of seismic moment, rupture area, average slip, and asperity size for M~9 subduction-zone earthquakes. Geophys. Res. Lett. 40, 5070–5074 (2013).

    Article  Google Scholar 

  61. Thingbaijam, K. K., Mai, P. M. & Goda, K. New empirical earthquake source-scaling laws. Bull. Seismol. Soc. Am. 107, 2225–2246 (2017).

    Article  Google Scholar 

  62. Goda, K. & De Risi, R. Multi-hazard loss estimation for shaking and tsunami using stochastic rupture sources. Int. J. Disaster Risk Reduct. 28, 539–554 (2018).

    Article  Google Scholar 

  63. Herrero, A. & Bernard, P. A kinematic self-similar rupture process for earthquakes. Bull. Seismol. Soc. Am. 84, 1216–1228 (1994).

    Article  Google Scholar 

  64. Mai, P. M. & Beroza, G. C. A spatial random field model to characterize complexity in earthquake slip. J. Geophys. Res. Solid Earth 107, ESE-10 (2002).

    Article  Google Scholar 

  65. Goda, K., Mai, P. M., Yasuda, T. & Mori, N. Sensitivity of tsunami wave profiles and inundation simulations to earthquake slip and fault geometry for the 2011 Tohoku earthquake. Earth Planets Space 66, 1–20 (2014).

    Article  Google Scholar 

  66. Goda, K., Yasuda, T., Mori, N. & Maruyama, T. New scaling relationships of earthquake source parameters for stochastic tsunami simulation. Coast. Eng. J. 58, 1650010 (2016).

    Article  Google Scholar 

  67. Melgar, D. & Hayes, G. P. The correlation lengths and hypocentral positions of great earthquakes. Bull. Seismol. Soc. Am. 109, 2582–2593 (2019).

    Article  Google Scholar 

  68. Li, L. et al. How heterogeneous coseismic slip affects regional probabilistic tsunami hazard assessment: a case study in the South China Sea. J. Geophys. Res. Solid Earth 121, 6250–6272 (2016).

    Article  Google Scholar 

  69. Scala, A. et al. Effect of shallow slip amplification uncertainty on probabilistic tsunami hazard analysis in subduction zones: use of long-term balanced stochastic slip models. Pure Appl. Geophys. 177, 1497–1520 (2020).

    Article  Google Scholar 

  70. Lomax, A., Michelini, A. & Piatanesi, A. An energy-duration procedure for rapid determination of earthquake magnitude and tsunamigenic potential. Geophys. J. Int. 170, 1195–1209 (2007).

    Article  Google Scholar 

  71. Howell, B. F. Jr On the saturation of earthquake magnitudes. Bull. Seismol. Soc. Am. 71, 1401–1422 (1981).

    Article  Google Scholar 

  72. Hanks, T. C. & Kanamori, H. A moment magnitude scale. J. Geophys. Res. Solid Earth 84, 2348–2350 (1979).

    Article  Google Scholar 

  73. Kanamori, H. & Rivera, L. Source inversion of W phase: speeding up seismic tsunami warning. Geophys. J. Int. 175, 222–238 (2008).

    Article  Google Scholar 

  74. Duputel, Z. et al. Real-time W phase inversion during the 2011 off the Pacific coast of Tohoku earthquake. Earth Planets Space 63, 535–539 (2011).

    Article  Google Scholar 

  75. Tsuboi, S., Abe, K., Takano, K. & Yamanaka, Y. Rapid determination of Mw from broadband P waveforms. Bull. Seismol. Soc. Am. 85, 606–613 (1995).

    Google Scholar 

  76. Lomax, A. & Michelini, A. Mwpd: a duration–amplitude procedure for rapid determination of earthquake magnitude and tsunamigenic potential from P waveforms. Geophys. J. Int. 176, 200–214 (2009).

    Article  Google Scholar 

  77. Katsumata, A., Ueno, H., Aoki, S., Yoshida, Y. & Barrientos, S. Rapid magnitude determination from peak amplitudes at local stations. Earth Planets Space 65, 843–853 (2013).

    Article  Google Scholar 

  78. Wang, D. et al. Automated determination of magnitude and source length of large earthquakes using backprojection and P wave amplitudes. Geophys. Res. Lett. 44, 5447–5456 (2017).

    Article  Google Scholar 

  79. Cienfuegos, R. et al. What can we do to forecast tsunami hazards in the near field given large epistemic uncertainty in rapid seismic source inversions? Geophys. Res. Lett. 45, 4944–4955 (2018).

    Article  Google Scholar 

  80. MacInnes, B. T., Gusman, A. R., LeVeque, R. J. & Tanioka, Y. Comparison of earthquake source models for the 2011 Tohoku event using tsunami simulations and near-field observations. Bull. Seismol. Soc. Am. 103, 1256–1274 (2013).

    Article  Google Scholar 

  81. Mori, N., Mai, P. M., Goda, K. & Yasuda, T. Tsunami inundation variability from stochastic rupture scenarios: application to multiple inversions of the 2011 Tohoku, Japan earthquake. Coast. Eng. 127, 88–105 (2017).

    Article  Google Scholar 

  82. Catalan, P. A. et al. Design and operational implementation of the integrated tsunami forecast and warning system in Chile (SIPAT). Coast. Eng. J. 62, 373–388 (2020).

    Article  Google Scholar 

  83. Greenslade, D. J. et al. Evaluation of Australian tsunami warning thresholds using inundation modelling. Pure Appl. Geophys. 177, 1425–1436 (2020).

    Article  Google Scholar 

  84. Glimsdal, S., Pedersen, G. K., Harbitz, C. B. & Løvholt, F. Dispersion of tsunamis: does it really matter? Natural Haz. Earth Syst. Sci. 13, 1507–1526 (2013).

    Article  Google Scholar 

  85. Rabinovich, A. B., Woodworth, P. L. & Titov, V. V. Deep-sea observations and modeling of the 2004 Sumatra tsunami in Drake Passage. Geophys. Res. Lett. 38, L16604 (2011).

    Article  Google Scholar 

  86. Bai, Y., Yamazaki, Y. & Cheung, K. F. Interconnection of multi-scale standing waves across the Pacific Basin from the 2011 Tohoku tsunami. Ocean Model. 92, 183–197 (2015).

    Article  Google Scholar 

  87. Watada, S., Kusumoto, S. & Satake, K. Travel time delay and initial phase reversal of distant tsunamis coupled with the self-gravitating elastic Earth. J. Geophys. Res. Solid Earth 119, 4287–4310 (2014).

    Article  Google Scholar 

  88. Allgeyer, S. & Cummins, P. Numerical tsunami simulation including elastic loading and seawater density stratification. Geophys. Res. Lett. 41, 2368–2375 (2014).

    Article  Google Scholar 

  89. Watada, S. Tsunami speed variations in density-stratified compressible global oceans. Geophys. Res. Lett. 40, 4001–4006 (2013).

    Article  Google Scholar 

  90. Ho, T.-C., Satake, K. & Watada, S. Improved phase corrections for transoceanic tsunami data in spatial and temporal source estimation: application to the 2011 Tohoku earthquake. J. Geophys. Res. Solid Earth 122, 10,155–10,175 (2017).

    Article  Google Scholar 

  91. Baba, T. et al. Accurate numerical simulation of the far-field tsunami caused by the 2011 Tohoku earthquake, including the effects of Boussinesq dispersion, seawater density stratification, elastic loading, and gravitational potential change. Ocean Model. 111, 46–54 (2017).

    Article  Google Scholar 

  92. Carvajal, M., Cisternas, M. & Catalán, P. A. Source of the 1730 Chilean earthquake from historical records: implications for the future tsunami hazard on the coast of Metropolitan Chile. J. Geophys. Res. Solid Earth 122, 3648–3660 (2017).

    Article  Google Scholar 

  93. Matsuyama, M., Ikeno, M., Sakakiyama, T. & Takeda, T. A study of tsunami wave fission in an undistorted experiment. In Tsunami and its Hazards in the Indian and Pacific Oceans 617–631 (Birkhäuser, 2007).

  94. Borrero, J. C., Lynett, P. J. & Kalligeris, N. Tsunami currents in ports. Phil. Trans. R. Soc. A 373, 20140372 (2015).

    Article  Google Scholar 

  95. Lynett, P. J. et al. Inter-model analysis of tsunami-induced coastal currents. Ocean Model. 114, 14–32 (2017).

    Article  Google Scholar 

  96. Mori, N., Cox, D. T., Yasuda, T. & Mase, H. Overview of the 2011 Tohoku earthquake tsunami damage and its relation to coastal protection along the Sanriku coast. Earthq. Spectra 29, 127–143 (2013).

    Article  Google Scholar 

  97. Suppasri, A., Koshimura, S. & Imamura, F. Developing tsunami fragility curves based on the satellite remote sensing and the numerical modeling of the 2004 Indian Ocean tsunami in Thailand. Natural Haz. Earth Syst. Sci. 11, 173–189 (2011).

    Article  Google Scholar 

  98. Suppasri, A. et al. Building damage characteristics based on surveyed data and fragility curves of the 2011 Great East Japan tsunami. Natural Haz. 66, 319–341 (2013).

    Article  Google Scholar 

  99. Shimozono, T. & Sato, S. Coastal vulnerability analysis during tsunami-induced levee overflow and breaching by a high-resolution flood model. Coast. Eng. 107, 116–126 (2016).

    Article  Google Scholar 

  100. Charvet, I., Suppasri, A., Kimura, H., Sugawara, D. & 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. Natural Haz. 79, 2073–2099 (2015).

    Article  Google Scholar 

  101. Attary, N., van de Lindt, J. W., Unnikrishnan, V., Barbosa, A. R. & Cox, D. T. Methodology for development of physics-based tsunami fragilities. J. Struct. Eng. 143, 04016223 (2017).

    Article  Google Scholar 

  102. Fukui, N., Prasetyo, A. & Mori, N. Numerical modeling of tsunami inundation using upscaled urban roughness parameterization. Coast. Eng. 152, 103534 (2019).

    Article  Google Scholar 

  103. Fukui, N. et al. Variations in building-resolving simulations of tsunami inundation in a coastal urban area. J. Waterw. Port Coast. Ocean Eng. 148, 04021044 (2022).

    Article  Google Scholar 

  104. Park, H., Cox, D. T., Lynett, P. J., Wiebe, D. M. & Shin, S. Tsunami inundation modeling in constructed environments: a physical and numerical comparison of free-surface elevation, velocity, and momentum flux. Coast. Eng. 79, 9–21 (2013).

    Article  Google Scholar 

  105. Prasetyo, A., Tomiczek, T., Yasuda, T., Mori, N. & Mase, H. Characteristics of a tsunami wave using a hybrid tsunami generator. In Coastal Structures and Solutions to Coastal Disasters 2015: Tsunamis 164–175 (American Society of Civil Engineers, 2017).

  106. Como, A. & Mahmoud, H. Numerical evaluation of tsunami debris impact loading on wooden structural walls. Eng. Struct. 56, 1249–1261 (2013).

    Article  Google Scholar 

  107. Park, H. & Cox, D. T. Effects of advection on predicting construction debris for vulnerability assessment under multi-hazard earthquake and tsunami. Coast. Eng. 153, 103541 (2019).

    Article  Google Scholar 

  108. Miyashita, T., Mori, N. & Goda, K. Uncertainty of probabilistic tsunami hazard assessment of Zihuatanejo (Mexico) due to the representation of tsunami variability. Coast. Eng. J. 62, 413–428 (2020).

    Article  Google Scholar 

  109. Chock, G. Y. K. Design for tsunami loads and effects in the ASCE 7-16 standard. J. Struct. Eng. 142, 04016093 (2016).

    Article  Google Scholar 

  110. Zamora, N., Catalán, P. A., Gubler, A. & Carvajal, M. Microzoning tsunami hazard by combining flow depths and arrival times. Front. Earth Sci. 8, 591514 (2021).

    Article  Google Scholar 

  111. Baker, J.W., Bradley, B. & Stafford, P. Seismic Hazard and Risk Analysis (Cambridge Univ. Press, 2021).

  112. Goda, K. & De Risi, R. Probabilistic tsunami loss estimation: stochastic earthquake scenario approach. Earthq. Spectra 33, 1301–1323 (2017).

    Article  Google Scholar 

  113. Tarbotton, C., Dall’Osso, F., Dominey-Howes, D. & Goff, J. The use of empirical vulnerability functions to assess the response of buildings to tsunami impact: comparative review and summary of best practice. Earth Sci. Rev. 142, 120–134 (2015).

    Article  Google Scholar 

  114. Attary, N., Unnikrishnan, V. U., van de Lindt, J. W., Cox, D. T. & Barbosa, A. R. Performance-based tsunami engineering methodology for risk assessment of structures. Eng. Struct. 141, 676–686 (2017).

    Article  Google Scholar 

  115. Petrone, C., Rossetto, T. & Goda, K. Fragility assessment of a RC structure under tsunami actions via nonlinear static and dynamic analyses. Eng. Struct. 136, 36–53 (2017).

    Article  Google Scholar 

  116. Park, H. & Cox, D. T. Effects of advection on forecasting construction debris for vulnerability assessment under multi-hazard earthquake and tsunami. Coast. Eng. 153, 103541 (2019).

    Article  Google Scholar 

  117. Kameshwar, S. et al. Probabilistic decision-support framework for community resilience: incorporating multi-hazards, infrastructure interdependencies, and target objectives in a Bayesian network. Reliab. Eng. Syst. Saf. 191, 106568 (2019).

    Article  Google Scholar 

  118. Park, H., Alam, M. S., Cox, D. T., Barbosa, A. R. & van de Lindt, J. W. Probabilistic seismic and tsunami damage analysis (PSTDA) of the Cascadia subduction zone applied to Seaside, Oregon. Int. J. Disaster Risk Reduct. 35, 101076 (2019).

    Article  Google Scholar 

  119. Attary, N., van de Lindt, J. W., Barbosa, A. R., Cox, D. T. & Unnikrishnan, V. U. Performance-based tsunami engineering for risk assessment of structures subjected to multi-hazards: tsunami following earthquake. J. Earthq. Eng. 25, 2065–2084 (2021).

    Article  Google Scholar 

  120. Goda, K. et al. Multi-hazard earthquake-tsunami loss estimation of Kuroshio Town, Kochi Prefecture, Japan considering the Nankai–Tonankai megathrust rupture scenarios. Int. J. Disaster Risk Reduct. 54, 102050 (2021).

    Article  Google Scholar 

  121. Goda, K. et al. Earthquake-tsunami risk assessment and critical multi-hazard loss scenarios: a case study in Japan under the Nankai–Tonankai mega-thrust. In Engineering for Extremes 235–254 (Springer, 2022).

  122. Li, L. et al. A modest 0.5-m rise in sea level will double the tsunami hazard in Macau. Sci. Adv. 4, eaat1180 (2018).

    Article  Google Scholar 

  123. Alhamid, A. K. et al. Framework for probabilistic tsunami hazard assessment considering the effects of sea-level rise due to climate change. Struct. Safety 94, 102152 (2022).

    Article  Google Scholar 

  124. Song, J. & Goda, K. Influence of elevation data resolution on tsunami loss estimation and insurance rate-making. Front. Earth Sci. 7, 246 (2019).

    Article  Google Scholar 

  125. Goda, K. Multi-hazard parametric catastrophe bond trigger design for subduction earthquakes and tsunamis. Earthq. Spectra. 37, 1827–1848 (2021).

    Article  Google Scholar 

  126. Shuto, N. & Fujima, K. Review: a short history of tsunami research and countermeasures in Japan. Proc. Jpn Acad. Ser. B 85, 267–275 (2009).

    Article  Google Scholar 

  127. Koshimura, S. & Shuto, N. Response to the 2011 Great East Japan earthquake and Tsunami disaster. Phil. Trans. R. Soc. A 373, 20140373 (2015).

    Article  Google Scholar 

  128. Kato, F., Suwa, Y., Watanabe, K. & Hatogai, S. Mechanisms of coastal dike failure induced by the Great East Japan earthquake tsunami. Coast. Eng. Proc. https://doi.org/10.9753/icce.v33.structures.40 (2012).

    Article  Google Scholar 

  129. Chen, J., Jiang, C., Yang, W. & Xiao, G. Laboratory study on protection of tsunami-induced scour by offshore breakwaters. Natural Haz. 81, 1229–1247 (2016).

    Article  Google Scholar 

  130. Tanaka, N., Yasuda, S., Iimura, K. & Yagisawa, J. Combined effects of coastal forest and sea embankment on reducing the washout region of houses in the Great East Japan tsunami. J. Hydro-Environ. Res. 8, 270–280 (2014).

    Article  Google Scholar 

  131. Park, H., Cox, D. T. & Barbosa, A. R. Comparison of inundation depth and momentum flux based fragilities for probabilistic tsunami damage assessment and uncertainty analysis. Coast. Eng. 122, 10–26 (2017).

    Article  Google Scholar 

  132. Guler, H. G., Baykal, C., Arikawa, T. & Yalciner, A. C. Numerical assessment of tsunami attack on a rubble mound breakwater using OpenFOAM. Appl. Ocean. Res. 72, 76–91 (2018).

    Article  Google Scholar 

  133. Jelínek, R. & Krausmann, E. Approaches to tsunami risk assessment. JRC Sci. Tech. Rep. 48713, 112 (2008).

    Google Scholar 

  134. Behrens, J. et al. Probabilistic tsunami hazard and risk analysis: a review of research gaps. Front. Earth Sci. https://doi.org/10.3389/feart.2021.628772 (2021).

    Article  Google Scholar 

  135. Salgado-Gálvez, M. A., Zuloaga-Romero, D., Bernal, G. A., Mora, M. G. & Cardona, O.-D. Fully probabilistic seismic risk assessment considering local site effects for the portfolio of buildings in Medellín, Colombia. Bull. Earthq. Eng. 12, 671–695 (2014).

    Article  Google Scholar 

  136. Ozer, S. C., Yalciner, A. C., Zaytsev, A., Suppasri, A. & Imamura, F. Investigation of hydrodynamic parameters and the effects of breakwaters during the 2011 Great East Japan tsunami in Kamaishi Bay. Pure Appl. Geophys. 172, 3473–3491 (2015).

    Article  Google Scholar 

  137. Syamsidik et al. Assessing the tsunami mitigation effectiveness of the planned Banda Aceh Outer Ring Road (BORR), Indonesia. Natural Haz. Earth Syst. Sci. 19, 299–312 (2019).

    Article  Google Scholar 

  138. Chock, G., Yu, G., Thio, H. K. & Lynett, P. J. Target structural reliability analysis for tsunami hydrodynamic loads of the ASCE 7 standard. J. Struct. Eng. 142, 04016092–04016112 (2016).

    Article  Google Scholar 

  139. Akiyama, M., Frangopol, D. M. & Ishibashi, H. Toward life-cycle reliability-, risk- and resilience-based design and assessment of bridges and bridge networks under independent and interacting hazards: emphasis on earthquake, tsunami and corrosion. Struct. Infrastruct. Eng. 16, 26–50 (2020).

    Article  Google Scholar 

  140. Muhari, A., Diposaptono, S. & Imamura, F. Toward an integrated tsunami disaster mitigation: lessons learned from previous tsunami events in Indonesia. J. Nat. Disaster Sci. 29, 13–19 (2007).

    Article  Google Scholar 

  141. Lunghino, B. et al. The protective benefits of tsunami mitigation parks and ramifications for their strategic design. Proc. Natl Acad. Sci. 117, 10740–10745 (2020).

    Article  Google Scholar 

  142. Tanaka, N. Effectiveness and limitations of coastal forest in large tsunami: conditions of Japanese pine trees on coastal sand dunes in tsunami caused by Great East Japan earthquake. J. Jpn Soc. Civil Eng. Ser. B1 68, II_7–II_15 (2012).

    Google Scholar 

  143. Osti, R., Tanaka, S. & Tokioka, T. The importance of mangrove forest in tsunami disaster mitigation. Disasters 33, 203–213 (2009).

    Article  Google Scholar 

  144. Danielsen, F. et al. The Asian tsunami: a protective role for coastal vegetation. Science 310, 643 (2005).

    Article  Google Scholar 

  145. Goda, K. et al. Cascading geological hazards and risks of the 2018 Sulawesi Indonesia earthquake and sensitivity analysis of tsunami inundation simulations. Front. Earth Sci. 7, 261 (2019).

    Article  Google Scholar 

  146. American Society of Civil Engineers. Minimum design loads and associated criteria for buildings and other structures 7–22 (ASCE-SEI, 2022).

  147. Taubenböck, H. et al. “Last-mile” preparation for a potential disaster — interdisciplinary approach towards tsunami early warning and an evacuation information system for the coastal city of Padang, Indonesia. Natural Haz. Earth Syst. Sci. 9, 1509–1528 (2009).

    Article  Google Scholar 

  148. Mas, E. et al. Recent advances in agent-based tsunami evacuation simulations: case studies in Indonesia, Thailand, Japan and Peru. Pure Appl. Geophys. 172, 3409–3424 (2015).

    Article  Google Scholar 

  149. Wood, N. J., Jones, J., Schmidtlein, M. C., Schelling, J. & Frazier, T. Pedestrian flow-path modeling to support tsunami evacuation and disaster relief planning in the US Pacific Northwest. Int. J. Disaster Risk Reduct. 18, 41–55 (2016).

    Article  Google Scholar 

  150. Muhammad, A. et al. Are current tsunami evacuation approaches safe enough? Stoch. Environ. Res. Risk Assess. 35, 759–779 (2021).

    Google Scholar 

  151. Wood, N. J. & Schmidtlein, M. C. Anisotropic path modeling to assess pedestrian evacuation potential from Cascadia-related tsunamis in the US Pacific Northwest. Natural Haz. 62, 275–300 (2012).

    Article  Google Scholar 

  152. Schmidtlein, M. C. & Wood, N. J. Sensitivity of tsunami evacuation modeling to direction and land cover assumptions. Appl. Geogr. 56, 154–163 (2015).

    Article  Google Scholar 

  153. Kitamura, F., Inazu, D., Ikeya, T. & Okayasu, A. An allocating method of tsunami evacuation routes and refuges for minimizing expected casualties. Int. J. Disaster Risk Reduct. 45, 101519 (2020).

    Article  Google Scholar 

  154. Mostafizi, A., Wang, H., Dong, S., Cox, D. T. & Cramer, L. Agent-based tsunami evacuation modeling with unplanned network disruptions for evidence-driven resource allocation and planning strategies. Natural Haz. 88, 1347–1372 (2017).

    Article  Google Scholar 

  155. Castro, S., Poulos, A., Herrera, J. C. & de la Llera, J. C. Modeling the impact of earthquake-induced debris on tsunami evacuation times of coastal cities. Earthq. Spectra 35, 137–158 (2019).

    Article  Google Scholar 

  156. Makinoshima, F., Imamura, F. & Abe, Y. Behavior from tsunami recorded in the multimedia sources at Kesennuma City in the 2011 Tohoku Tsunami and its simulation by using the evacuation model with pedestrian-car interaction. Coast. Eng. J. 8, 1640023 (2018).

    Google Scholar 

  157. Wilson, R. et al. Development and use of probabilistic tsunami hazard analysis maps in California. In Proc. 11th Nat. Conf. Earthq. Eng. 3411–3422 (EERI, 2018).

  158. Basic guidelines for reconstruction in response to the Great East Japan earthquake. In Reconstruction and Revitalization Period 18 (Japan Reconstruction Agency, 2016).

  159. Cosson, C. “Build back better”: between public policy and local implementation, the challenges in Tohoku’s reconstruction. Arch. Urban. Plan. 16, 1–4 (2020).

    Google Scholar 

  160. Tsunami Resilience On The Oregon Coast (OSSPAC, 2022); https://www.oregon.gov/oem/Documents/OSSPAC_Agenda_Packet_2022-05-10.pdf.

  161. Guidelines for evaluating and mitigation tsunami hazards in California. In California Geological Survey Special Publication 127 (in the press).

  162. National tsunami hazard mitigation program strategic plan: 2018–2023 (NOAA, 2018).

  163. Esteban, M. et al. Recent tsunamis events and preparedness: development of tsunami awareness in Indonesia, Chile and Japan. Int. J. Disaster Risk Reduct. 5, 84–97 (2013).

    Article  Google Scholar 

  164. Tohoku tsunami, March 11, 2011 main event page: global propagation animation of tsunami. In NOAA NCTR Experimental Research Product (NOAA, 2011); https://nctr.pmel.noaa.gov/honshu20110311/.

  165. Lynett, P. J. Precise prediction of coastal and overland flow dynamics: a grand challenge or a fool’s errand. J. Disaster Res. 11, 615–623 (2016).

    Article  Google Scholar 

  166. Goda, K. et al. Earthquake–tsunami risk assessment and critical multi-hazard loss scenarios: a case study in Japan under the Nankai–Tonankai mega-thrust. In Engineering For Extremes: Decision-making In An Uncertain World (eds Stewart, M. G. & Rosowsky, D. V.) Ch. 11 235–254 (Springer, 2022).

Download references

Acknowledgements

N.M. acknowledges funding from Grant-in-Aid for Scientific Research (KAKENHI) (grant numbers 20KK0095 and 21H04508), JST/JICA SATREPS Indonesia and the DPRI-ERI Research Fund (grant numbers 2019-K-01 and 2021-K-01). K.G. acknowledges funding from the Canada Research Chair programme (grant number 950-232015) and a Natural Sciences and Engineering Research Council Discovery Grant (grant number RGPIN-2019-05898). P.A.C. acknowledges funding from ANID; the Chile Centro de Investigación para la Gestión Integrada del Riesgo de Desastres (CIGIDEN) (grant number ANID/FONDAP/15110017) and the Centro Científico Tecnológico de Valparaíso (grant number ANID PIA/APOYO AFB180002). PMEL contribution #5397.

Author information

Authors and Affiliations

  1. Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan

    Nobuhito Mori, Tung-Cheng Ho & Takuya Miyashita

  2. Earthquake Research Institute, University of Tokyo, Tokyo, Japan

    Kenji Satake

  3. School of Civil and Construction Engineering, Oregon State University, Corvallis, OR, USA

    Daniel Cox

  4. Department of Earth Sciences, Western University, London, Canada

    Katsuichiro Goda

  5. Departamento de Obras Civiles, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Patricio A. Catalan

  6. International Research Institute of Disaster Science, Tohoku University, Sendai, Japan

    Fumihiko Imamura

  7. Department of Naval Architecture and Ocean Engineering, United States Naval Academy, Annapolis, MD, USA

    Tori Tomiczek

  8. Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, USA

    Patrick Lynett

  9. Badan Nasional Penanggulangan Bencana, Jakarta, Indonesia

    Abdul Muhari

  10. Pacific Marine Environmental Laboratory, National Oceanic and Atmospheric Administration, Seattle, WA, USA

    Vasily Titov

  11. California Geological Survey, California Department of Conservation, Sacramento, CA, USA

    Rick Wilson

Authors
  1. Nobuhito Mori
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kenji Satake
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Cox
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Katsuichiro Goda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Patricio A. Catalan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tung-Cheng Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fumihiko Imamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tori Tomiczek
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Patrick Lynett
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Takuya Miyashita
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Abdul Muhari
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Vasily Titov
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Rick Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.M. and K.G. led the writing and revision of the manuscript, with input and contributions from K.S., D.C., P.A.C., F.I., T.T., P.L., T.M., A.M., V.T., T.-C.H. and R.W. All authors made substantial contributions to the discussion of content.

Corresponding author

Correspondence to Nobuhito Mori.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Earth & Environment thanks F. Løvholt, T. Lay, C. Mueller, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Japan Meteorological Agency: Earthquakes and tsunamis–disaster prevention and mitigation efforts: https://www.jma.go.jp/jma/kishou/books/jishintsunami/en/jishintsunami_en.pdf

NOAA Global Historical Tsunami Database: https://www.ngdc.noaa.gov/hazard/tsu_db.shtml

NOAA Tohoku 2011 Tsunami Main Event Page: https://nctr.pmel.noaa.gov/honshu20110311/

The International Disaster Database (EM-DAT): https://www.emdat.be/

Supplementary information

Glossary

Long-term assessment

Estimation of hazard intensity and frequency based on historical data or model results.

Tsunami hazard

Height or velocity of tsunami, used in tsunami hazard assessments.

Risk

Combination of hazard, exposure and vulnerability.

Megathrust fault

The boundary between the two converging tectonic plates at a subduction zone

Megathrust earthquake tsunami

A tsunami that occurs at a subduction zone following a megathrust earthquake.

Deep-ocean Assessment and Reporting of Tsunamis

(DART). A tsunami monitoring system that consists of OBP sensors and moored surface buoys for real-time communication of data via satellites, developed by NOAA.

Far-field tsunami

Tsunami with waves that affect coastal regions far away (over 1,000 km) from the location of the tsunami source.

Ocean-bottom pressure

(OBP). A kind of sensor that monitors ocean-bottom pressure and converts it to sea-level heights, enabling detection of tsunamis in the deep ocean.

Seafloor Observation Network for Earthquakes and Tsunamis

(S-net). A network of 150 OBP stations connected by a network of over 5,800 km of submarine cables, installed along the Japan Trench after the 2011 Tohoku tsunami.

Deep Ocean-floor Network system for Earthquakes and Tsunamis

(DONET/DONET2). A Japanese network of approximately 50 OBP sensors connected by submarine cables along the Nankai trough.

Tsunami early warning systems

(TEWS). Real-time tsunami alert systems, in which estimates of tsunami heights are based on seismic and/or tsunami observation data.

Near-field tsunami

Tsunami with waves that affect regions near the location of the tsunami source.

Probabilistic tsunami hazard assessment

(PTHA). A probabilistic quantification of tsunami intensity and frequency, based on assessments of earthquake frequency, hazard footprints and damage susceptibility.

ShakeAlert

Earthquake early warning system developed by USGS and partners, which combines rapid earthquake detection with alert messages broadcast to a variety of people, infrastructure and devices, such as personal mobile phones.

Magnitude

A measure of an earthquake’s size or strength.

Palaeotsunami

A tsunami that occurred prior to historical records or has no written observations.

Gutenberg–Richter model

Empirical relation used to estimate earthquake frequency.

Moment magnitude

(Mw). A measure of earthquake magnitude based on its seismic moment.

Wave dispersion

Waves of different periods that travel at different phase speeds (waves with shorter periods travel at slower phase speeds).

Tsunami mitigation parks

Purpose-designed spaces in coastal regions that are built to reduce tsunami forces beyond the park, thereby helping to protect critical infrastructure or communities.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mori, N., Satake, K., Cox, D. et al. Giant tsunami monitoring, early warning and hazard assessment. Nat Rev Earth Environ 3, 557–572 (2022). https://doi.org/10.1038/s43017-022-00327-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43017-022-00327-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing