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A tsunami from 9.0 earthquake would reach B.C. coast in about 20 minutes, research finds

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If a magnitude 9.0 earthquake struck in the Cascadia subduction zone, along the B.C. and U.S. coast, tsunami waves would reach the outer coast of northwest Vancouver Island in about 20 minutes.

The waves would be an estimated 5.9 metres high, followed by runup on the land that may exceed 12 metres of elevation.

This is just part of the findings of a coastal hazard assessment plan for B.C.’s coast in order to help communities and the province plan for emergencies .

Research from Ocean Network Canada (ONC), Strathcona Regional District and Northwest Hydraulic Consultants, as well as Ka:’yu:’k’t’h’/Che:k:tles7et’h’ First Nations and Nuchatlaht First Nation, will help determine the estimated arrival times and flooding extent of earthquake-induced tsunamis on the B.C. coast.

“The objective of our project was to first identify the hazard in the study area, what is the tsunami wave height, current velocities and time of arrival of tsunamis, and also how to communicate those hazards with the public through several hazard and foundation maps that we created for the project,” Soroush Kouhi, an applied scientist specialist at Ocean Network Canada told Global News.

“It’s important to raise the awareness of the public through the information that we have provided in this project.”

Kouhi said one of the biggest takeaways from the project is how quickly the tsunami waves could arrive on parts of B.C.’s coast in less than 30 minutes if the quake strikes in the Cascadia subduction zone.

He added the communities on the west coast of Vancouver Island and others such as Tahsis and Zeballos are at the greatest risk of a dangerous tsunami.

“We regularly work with communities on their tsunami activities preparedness and plans,” Minister of Emergency Management and Climate Readiness Bowinn Ma told Global News Tuesday.

“We have funding provided through the Community Preparedness Fund to support communities in work that they do so as an example, Tofino recently received funding to plan a vertical escape structure for their residents.”

Ma said the province does have staff 24 hours a day, 365 days a year, monitoring seismic activity off the coast and if a tsunami could occur at that time. She added that if a risk of a tsunami is determined, then the provincial government does have the ability to broadcast emergency alerts to communities at risk.

“I was out in Ucluelet just in April and participating in their high-ground hike,” Ma added. “So communities, during tsunami preparedness week, perform or often engage in these high-ground hikes and tsunami preparedness activities where they practice with the community where the high ground is in a community.”

Kouhi said the results of this project can help communities better prepare for tsunamis and update their emergency plans.

Archie Little, a councillor with the Nuchatlaht First Nation, located just west of Zeballos, said he remembers the 1964 tsunami that struck Port Alberni.

That tsunami was caused by a magnitude 9.2 earthquake off the coast of Alaska.

“The tsunami produced by the earthquake swept southward along the British Columbia coast, and into the coastal passages and fiords. It penetrated up rivers, and was even recorded at Pitt Lake, a fresh water tidal lake over 30 miles from the sea,” according to a report from Fisheries and Oceans Canada .

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Little said he was in residential school on Meares Island, at the time, and the tidal waves hit in the afternoon.

“The tide came in about two or three times and came up a couple of hundred feet, just came in and out,” he added.

ONC also helped produce a documentary on the history of tsunamis on the West Coast from an Indigenous perspective .

Little said they showed the documentary in Port Alberni but many young people didn’t attend.

“Those are who we need to focus on to ensure that they know, to ensure that they are ready and not to wait until the last minute to start grabbing stuff because it will be too late.

“It’s coming. It’s just a matter of when and the more we’re prepared, we’ll survive.”

Giant tsunami monitoring, early warning and hazard assessment

Nobuhito Mori ORCID: orcid.org/0000-0001-9082-3235 1 ,
Kenji Satake ORCID: orcid.org/0000-0002-3368-3085 2 ,
Daniel Cox 3 ,
Katsuichiro Goda ORCID: orcid.org/0000-0003-3900-2153 4 ,
Patricio A. Catalan 5 ,
Tung-Cheng Ho ORCID: orcid.org/0000-0002-3678-8288 1 ,
Fumihiko Imamura ORCID: orcid.org/0000-0001-7628-575X 6 ,
Tori Tomiczek ORCID: orcid.org/0000-0003-4116-7547 7 ,
Patrick Lynett ORCID: orcid.org/0000-0002-2856-9405 8 ,
Takuya Miyashita 1 ,
Abdul Muhari 9 ,
Vasily Titov ORCID: orcid.org/0000-0002-1630-3829 10 &
Rick Wilson ORCID: orcid.org/0000-0003-3629-2167 11

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

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Physical oceanography
Solid Earth sciences

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.

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.

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

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

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

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

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 M w 9.0 Tohoku earthquake. Seismol. Res. Lett. 82 , 481–493 (2011).

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).

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

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).

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).

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 .

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 .

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

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

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 .

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

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).

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

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 .

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).

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).

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

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).

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).

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).

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).

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).

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).

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).

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).

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

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

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

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

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).

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

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).

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

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

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).

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

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).

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).

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).

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).

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

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).

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

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).

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).

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

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

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

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

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).

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).

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

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).

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).

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).

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).

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

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).

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

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).

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).

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).

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

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).

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).

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).

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

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

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

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).

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

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

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).

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).

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).

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).

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).

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).

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

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).

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).

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).

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).

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

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

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).

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).

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).

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).

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

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

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).

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).

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).

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).

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).

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).

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

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).

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).

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).

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

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).

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).

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

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).

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

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

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).

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).

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).

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).

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).

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).

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).

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).

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).

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).

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).

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

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

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

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

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).

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).

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).

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).

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).

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

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).

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).

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).

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).

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).

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).

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).

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).

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).

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

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

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).

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

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).

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).

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).

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

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).

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

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).

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).

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).

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).

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).

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

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

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

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

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

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).

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/ .

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).

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).

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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.

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Supplementary information

Supplementary information.

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

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

Combination of hazard, exposure and vulnerability.

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

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

(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.

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

(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.

(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.

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

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

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

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

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.

A measure of an earthquake’s size or strength.

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

Empirical relation used to estimate earthquake frequency.

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

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

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

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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

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Lombok Indonesia Earthquake 2018 Case Study

The causes, effects, and responses to the Lombok earthquake

Lombok is one of the 17508 islands that make up Indonesia. The island is approximately 4,500 sq km (1,700 sq miles) and is located to the east of Bali and west of Sumbawa part of the Lesser Sunda Island chain. It’s known for beaches and surfing spots, particularly at Kuta and Banko Banko (in south Lombok).

In the first in the series, on 29 July, a 6.4 magnitude quake triggered landslides in the mountain region of the island and killed at least 16 people. Following this a shallow, magnitude 6.9 earthquake struck Lombok and Bali on August 5th, 2018, killing over 555 people, injuring 1300 and leaving at least 353000 homeless. The most severe damage was in North Lombok close to the epicentre.

Location of the August 5th 2018 Lombok earthquake

The main quake struck at 19:46 local time (11:46 GMT) on Sunday, August 5th at a fairly shallow depth of 31km (19 miles).

Earthquakes are common in Indonesia because it lies on the Ring of Fire – the line of frequent quakes and volcanic eruptions that circles virtually the entire Pacific Rim.

More than half of the world’s active volcanoes above sea level are part of the ring.

The recent earthquakes have occurred along a specific zone where the Australian tectonic plate meets the Indonesian island plate, Sunda.

Tectonic plates are slabs of the Earth’s crust that move very slowly over our planet’s surface. Indonesia sits along the “Pacific Ring of Fire” where several tectonic plates collide and many volcanic eruptions and earthquakes occur.

The Earth’s tectonic plates

Some of these earthquakes are very large, such as the magnitude 9.1 earthquake off the west coast of Sumatra that generated the 2004 Indian Ocean tsunami . This earthquake occurred along the Java-Sumatra subduction zone , where the Australian tectonic plate moves underneath Indonesia’s Sunda plate.

Both earthquakes occurred along faults in an area where tectonic plates are colliding, with one diving beneath the other.

The Sunda Plate

In this area, there’s subduction, so the Australian plate is moving under the Sunda plate, and the Australian plate is moving to the north underneath the Sunda plate.

The earthquake destroyed tens of thousands of homes, mosques and businesses across Lombok on August 5 2018. More than 1,300 people were injured and nearly 353,000 have been internally displaced.

It is estimated that 80% of the region had been damaged by the earthquake. Lombok suffered more than 5 trillion rupiah ($342 million; £268 million) in damage following the 5 August earthquake, authorities said.

Hundreds of tourists were stranded on the island and hotels were filled to capacity. No tourists were reported killed, but the earthquake was felt as far away as the neighbouring island of Bali, where two people died. The quake was followed by more than a dozen aftershocks, with one registering magnitude 5.4 on the Richter Scale.

According to scientists from NASA and the California Institute of Technology’s rapid-imaging project, the earthquake lifted the island as much as 25 centimetres in some areas. In other places, the ground dropped five-15cm.

Emergency teams in East and North Lombok reported that in some villages 75% of homes were damaged.

More than 500 hikers, most of whom were foreigners, were stranded on Indonesia’s Mt Rinjani when a deadly quake triggered landslides. The earthquake triggered landslides around Mount Rinjani, cutting off escape routes. The volcano , which rises 3,726m (12,224ft) above sea level and is the second-highest one in Indonesia, is a favourite among sightseers.

The region was hit by more than 350 aftershocks. Some measured up to 6.2 on the Richter Scale and brought down some buildings.

The area around Mount Rinjani increasingly relies on tourism , the earthquake and aftershocks led to the closure of mountain to hikers leading to many hotel cancellations by international tourists.

Hundreds of British citizens and European citizens were stuck in Lombok airport before flights could resume.

Aftershocks killed at least a further 13 people as the region recovered from the main event.

The Indonesia Government declared a three-week long state of emergency. “The most important thing is the emergency response, after that rehabilitation and reconstruction,” said Indonesia’s second-in-command, Vice President Jusuf Kalla. The government mobilised the National Disaster Mitigation Agency (BNPB) and the national military, directly deploying personnel in response to the earthquake.

Two helicopters were deployed to assist in emergency operations and the military sent troops and medical personnel, as well as medical supplies and communications equipment. Five planes carrying food, medicine, blankets, field tents and water tankers left the capital, Jakarta, for the island early on Wednesday 8th August.

Supplies for those made homeless were distributed with about 30,000 tents and 100 wheelchairs sent to affected areas.

As hospitals and clinics were affected by the earthquake many of the injured were treated in the open air or in makeshift clinics.

Rescue efforts were hampered by power outages, a lack of phone reception in some areas and limited evacuation options. A lack of heavy lifting equipment also affected the relief effort, with some rescuers forced to dig by hand. Other obstacles in the mountainous north and east of Lombok included collapsed bridges and electricity and communication blackouts. Debris blocked damaged roads.

In Sembalun the community pulled together to repair damaged buildings, including the town’s only health clinic. Electricity and clean water had to be being restored to villages in Sambalia that were cut off.

Emergency workers gradually reached more remote areas of Lombok having focussed their initial efforts in urban areas.

More than 500 hikers who were stranded on a mountain on the Indonesian island of Lombok after the earthquake were safely evacuated. Most of the hikers and guides were able to walk down after a safe route was found for them but some were flown out by helicopter.

The UK Foreign Office worked with the Indonesian authorities to provide assistance to British people caught up in the earthquake. Extra flights were added to help people who want to leave Lombok. Airport authorities requested that additional flights be added on Monday 6th August 2018 , to accommodate the influx of tourists trying to leave the island.

Charity, Plan International, provided counselling for children and supported those who were unable to go to school, by distributing emergency school kits and helping teachers continue educating while schools remain closed. The charity also provided humanitarian assistance to 2,500 families in six villages in Lombok. The organisation dispatched 500 emergency shelter kits, containing 1,000 tarpaulins, 1,000 sleeping mats and 2,000 blankets.

The Salvation Army in Indonesia also provided medical and other assistance to people who were affected by the earthquake on Lombok. The team immediately distributed a small supply of rice, noodles, sugar and bottled water to the affected population.

The Indonesian Red Cross (Palang Merah Indonesia) disaster responders provided first aid and assessed immediate needs in remote villages, arranging for bottled water and rice to be delivered by motorbike.

British-based charity Oxfam said it was providing clean drinking water and tarpaulin shelter sheets to 5,000 people and planned to intensify aid delivery.

A French military transport plane delivered 25 tonnes of humanitarian aid to the earthquake-hit island of Lombok on behalf of the Indonesian government.

On the 14th August 2018, The EU announced a further €500 000 to step up its emergency response to meet the most pressing needs of those affected by the devastating earthquakes that struck the Indonesian island of Lombok in late July and early August. The allocation came in addition to the initial €150 000 delivered earlier in August, thus bringing the EU’s contribution to €650 000. The EU humanitarian funding complemented the Indonesian government response and focussed on the most vulnerable groups and communities in the affected area. The EU aid supported the International Federation of Red Cross and Red Crescent Societies (IFRC) in providing relief assistance and protection to the most vulnerable among the affected population. It is estimated that the aid directly benefited 80 000 vulnerable people in some of the worst hit localities in the northeast and west Lombok districts. The aid was also used to assist the IFRC in reuniting families that were separated by the earthquakes. Aid was also offered by other countries including Australia.

Allegedly, authorities on Lombok were demanding money from tourists before they would let them onto rescue boats. However, around 5000 tourists who wanted to be evacuated from three outlying holiday islands had left by boat.

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Environmental hazards Case study: Indian Ocean Tsunami 2004

Understanding why natural hazards occur can help countries to manage or prevent their consequences. Case studies illustrate the impact of natural hazards in the short and long term.

Part of Geography Global issues

Case study: Indian Ocean Tsunami 2004

Indian ocean tsunami 2004.

A very common case study for earthquakes is the South-East Asian tsunami of 2004. Other case studies include Mexico 1985, San Francisco 1989, Kobe 1995 and Pakistan 2005.

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The causes and effects of the 2004 Asian tsunami

The underlying causes

On 26 December 2004 there was a massive and sudden movement of the Earth’s crust under the Indian Ocean. This earthquake was recorded at magnitude close magnitude The size or severity of something. For example, an earthquake. 9 on the Richter Scale close Richter scale The measure by which the strength of earthquakes is determined. and as it happened under the ocean, caused a devastating sea wave called a tsunami.

The epicentre close epicentre The point on the Earth's surface directly above the focus of an earthquake. of the earthquake occurred 200 kilometres west of the island of Sumatra in the Indian Ocean. The earthquake itself was caused by the subduction close subduction When one crustal plate is forced beneath the other. of the Indo-Australian plate under the Eurasian plate.

As the Indian plate (part of the Indo-Australian plate) moved underneath the Burma plate (part of the Eurasian plate) the crustal rocks stuck as they moved past one another. At 08:00 local time, the pressure build up was too great and the crustal rocks snapped, causing an earthquake.

When this happened the sea floor close sea floor The bottom of the ocean. was pushed upwards displacing a huge volume of water and creating the devastating tsunami waves.

Impact on landscape and population

Some smaller islands in the Indian Ocean were completely destroyed.
Coastal buildings were completely destroyed making people homeless.
Fishing villages close fishing villages A small settlement where the main activity is catching fish. were completely destroyed.
Lines of communication close lines of communication This refers to telephone cables and electricity power lines as well as roads and railways. , including phone lines, were cut off.
Electricity power lines were cut off.
Roads and railways were destroyed.
Fires broke out due to severed water pipes.
Approximately 250,000 people are estimated to have been killed, including many tourists close tourist Someone who travels for recreation or business purposes. on the beaches of Thailand.
There was an outbreak of diseases such as cholera due to a lack of fresh water supplies.
There was a lack of food as many fish died and farms were destroyed.
Thousands of people were made homeless.
Thousands of people lost their jobs as tourist hotels in Thailand were destroyed and fishing vessels were washed ashore.

Case Study on Recent Tsunami

Recent tsunami case study:.

One of the most serious and ruining tsunamis occurred in Japan in 2011 and it is called T?hoku earthquake and tsunami. The cause of the tsunami was the earthquake which occurred in the Pacific Ocean about 130 km from the coast of Japan. The magnitude of the earthquake was 9.0, no wonder that such an earthquake was followed by the enormous tsunami.

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1 introduction, 4 discussion and conclusions, acknowledgements, conflict of interest, data availability.

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The impact of ozone on Earth-like exoplanet climate dynamics: the case of Proxima Centauri b

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P De Luca, M Braam, T D Komacek, A Hochman, The impact of ozone on Earth-like exoplanet climate dynamics: the case of Proxima Centauri b, Monthly Notices of the Royal Astronomical Society , Volume 531, Issue 1, June 2024, Pages 1471–1482, https://doi.org/10.1093/mnras/stae1199

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The emergence of the JWST and the development of other advanced observatories (e.g. ELTs, LIFE, and HWO) marks a pivotal moment in the quest to characterize the atmospheres of Earth-like exoplanets. Motivated by these advancements, we conduct theoretical explorations of exoplanetary atmospheres, focusing on refining our understanding of planetary climate and habitability. Our study investigates the impact of ozone on the atmosphere of Proxima Centauri b in a synchronous orbit, utilizing coupled climate chemistry model simulations and dynamical systems theory. The latter quantifies compound dynamical metrics in phase space through the inverse of co-persistence ( θ ) and co-dimension ( d ), of which low values correspond to stable atmospheric states. Initially, we scrutinized the influence of ozone on temperature and wind speed. Including interactive ozone [i.e. coupled atmospheric (photo)chemistry] reduces the hemispheric difference in temperature from 68 °K to 64 °K, increases (∼+7 °K) atmospheric temperature at an altitude range of ∼20–50 km, and increases variability in the compound dynamics of temperature and wind speed. Moreover, with interactive ozone, wind speed during highly temporally stable states is weaker than for unstable ones, and ozone transport to the nightside gyres during unstable states is enhanced compared to stable ones (∼+800 DU). We conclude that including interactive ozone significantly influences Earth-like exoplanets' chemistry and climate dynamics. This study establishes a novel pathway for comprehending the influence of photochemical species on the climate dynamics of potentially habitable Earth-like exoplanets. We envisage an extension of this framework to other exoplanets.

The recent discovery of many nearby potentially temperate terrestrial exoplanets provides the opportunity to characterize climates of planets that may be Earth-like (Anglada-Escudé et al. 2016 ; Gillon et al. 2017 ; Rodriguez et al. 2020 ; Delrez et al. 2022 ; Kossakowski et al. 2023 ). Importantly, these temperate planets all have close-in orbits around M-dwarf host stars, which is expected to cause their basic-state climate to be impacted by tidal spin-synchronization (Pierrehumbert & Hammond 2019 ). Most notably, the large day-to-night irradiation contrast is expected to drive strong dayside convection (Yang et al. 2013 ; Sergeev et al. 2020 ) and a planetary-scale Matsuno-Gill pattern of equatorial waves (Showman et al. 2013 ). The combination of dayside convection, potential equatorial super-rotation, and off-equatorial Rossby waves are expected to control the spatial distribution of atmospheric tracers, including clouds (Komacek & Abbot 2019 ; Suissa et al. 2020 ) and photochemically produced chemical species such as ozone (Chen et al. 2021 ; Braam et al. 2023 ).

Ozone (O 3 ) forms from the photolysis of molecular oxygen (O 2 ) via the Chapman mechanism (Chapman 1930 ). On Earth, atmospheric O 2 is produced by photosynthesis and thus an indication of life. Therefore, O 2 and its photochemical by-product ozone have been proposed as potential biosignatures (e.g. Des Marais et al. 2002 ; Schwieterman et al. 2018 ). However, a variety of exoplanet scenarios, including the different ratios of near-UV to far-UV fluxes that planets around M-dwarfs receive, may drive abiotic build-up of O 2 and ozone in planetary atmospheres (e.g. Selsis et al. 2002 ; Domagal-Goldman et al. 2014 ).

Ozone is also a radiatively active species and thus impacts a planetary atmosphere's thermal and dynamic structure. Being both (photo)chemically and radiatively active makes ozone one of many potential species that induce climate-chemistry interactions, which have received considerable attention in models of the Earth System (e.g. Ramanathan et al. 1987 ; Isaksen et al. 2009 ), also as part of the Inter-governmental Panel on Climate Change reports (IPCC 2021 ). The radiative impact of ozone consists of two components. First, the stratospheric ozone layer absorbs incoming UV radiation on Earth. This layer protects the surface from harmful UV fluxes, which is also the case when considering the radiation received by exoplanets around M, K, G, and F-type stars (e.g. Segura et al. 2003 , 2005 ) or the 3D impact of stellar flares from M-dwarfs (Ridgway et al. 2023 ). Ozone then emits thermal energy at infrared wavelengths, heating the stratosphere and producing a temperature inversion, which is also predicted for various exoplanets (e.g. Godolt et al. 2015 ). Secondly, ozone in the upper troposphere acts as a greenhouse gas, absorbing the outgoing radiation from a planet. The radiative effect of ozone strongly depends on the 3D (vertical and horizontal) distribution through the atmosphere, which is ultimately determined by the complex interplay between (photo)chemistry and atmospheric dynamics. Studying such climate-chemistry interactions motivates using a 3D coupled climate-chemistry model (CCM).

A growing body of work uses CCM simulations to study Earth-like exoplanets in a synchronized orbit. Generally, significant hemispheric contrasts in ozone exist for planets around M-dwarfs (e.g. Chen et al. 2018 ; Yates et al. 2020 ), and such spatial variations should affect future observations (Cooke et al. 2023 ). Simulations of Proxima Centauri b predict a significant zonal structure in the ozone distribution with accumulation of ozone at the gyre locations (Yates et al. 2020 ), driven by a stratospheric circulation connecting the photochemically active dayside and the gyres on the nightside (Braam et al. 2023 ). Furthermore, Yates et al. ( 2020 ) have shown that steady-state climate conditions differ when ozone is computed interactively, depending on photochemistry, atmospheric circulation, and temperature, compared to the simulations without ozone and with a fixed Earth-like ozone profile (Boutle et al. 2017 ). However, simulations of Proxima Centauri b also show different versions of internal variability in, for example, stratospheric winds (Cohen et al. 2022 ) and planetary-scale waves (Cohen et al. 2023 ), affecting the climate and distributions of atmospheric tracers. At present, a full assessment of the impact of ozone on key atmospheric variables and climate dynamics by also applying a novel dynamical systems theory approach is the logical next step (De Luca et al. 2020a , 2020b ; Faranda et al. 2020 ).

Dynamical systems theory is the discipline that studies the trajectory of a given chaotic dynamical system, such as an atmosphere, by analysing Poincaré recurrences in phase space. Recent developments have seen Poincaré recurrences combined with extreme value theory (Lucarini et al. 2012 ; Faranda et al. 2017 ). This approach allows us to quantify two main metrics, the inverse of local persistence ( θ ) and local dimension ( d ), which are instantaneous in time and computed for 2D latitude-longitude maps. The former indicates the mean residence time of a given number of states around a state of interest. The latter provides information about the active degrees of freedom of the same states in the atmospheric phase space. The lower the θ and d , the more stable (in the sense of atmospheric variability) the atmospheric motion of the variable of interest, whereas the higher the θ and d , the less stable the trajectory of the atmospheric variable. The methodology has been used for univariate atmospheric variables such as temperature, precipitation, and geopotential height, and various studies proved its usefulness in analysing the climate dynamics of Earth (e.g. Hochman et al. 2019 , 2020 ; Vakrat & Hochman, 2023 ; Wedler et al. 2023 ) and terrestrial exoplanets (Hochman et al. 2022 , 2023 ). The method has also more recently been expanded to assess two (or more) atmospheric variables simultaneously (De Luca et al. 2020a , 2020b ; Faranda et al. 2020 ). Moreover, in this compound case, one can obtain two dynamical metrics: the inverse of local co-persistence ( θ T,WS ) and local co-dimension ( d T,WS ). They resemble the univariate metrics, with the only difference being that they are computed from two atmospheric variables of interest, temperature (T) and wind speed (WS), in two different phase spaces. Therefore, their values are obtained from joint recurrences, and we define them here as compound dynamical metrics.

Our main aim is to leverage climate model simulations of Proxima Centauri b, traditional atmospheric analysis, and dynamical systems theory to understand how ozone influences key atmospheric variables and climate dynamics. Section 2 describes the climate model setup and data, the computation of the compound dynamical systems metrics, and statistical inference. In Section 3 , we present our results regarding the direct effects of ozone on atmospheric variables and dynamics. We discuss our findings for Proxima Centauri b and provide conclusions extended to the broad range of Earth-like exoplanets in Section 4 .

2.1 Coupled climate-chemistry model

We use a 3D coupled Climate-Chemistry Model (CCM) consisting of the Met Office Unified Model (UM) and the UK Chemistry and Aerosol framework (UKCA). Braam et al. ( 2022 ) described the CCM in extensive detail. Here, we limit ourselves to a brief description of the essential components relevant to this study. A 3D CCM simulates the coupled evolution of radiative transfer, dynamics, and chemistry in a planetary atmosphere, comprehensively assessing the planetary climate and habitability.

The UM is a versatile General Circulation Model, using the ENDGame dynamical core to solve the equations of motion (Wood et al. 2014 ) and the Suite of Community Radiative Transfer codes based on the Edwards and Slingo scheme to compute radiative transfer (SOCRATES; Edwards & Slingo 1996 ). The incoming stellar radiation from Proxima Centauri (M5.5 V star) is based on the v2.2 composite spectrum from the MUSCLES spectral survey (France et al. 2016 ; P. Loyd et al. 2016 ; Youngblood et al. 2016 ). Sub-grid scale processes like convection (Gregory & Rowntree, 1990 ), water cloud physics (Wilson et al. 2008 ), and turbulent mixing (Lock et al. 2000 ; Brown et al. 2008 ) are parametrized. Besides the common use of the UM in predicting the Earth's weather and climate, it has been adapted to Mars (McCulloch et al. 2023 ) and exoplanets ranging from terrestrial planets (e.g. Mayne, Baraffe, Acreman, Smith, Wood, et al. 2014 ; Boutle et al. 2017 ) to hot Jupiters (e.g. Mayne, Baraffe, Acreman, Smith, Browning, et al. 2014 ; Amundsen et al. 2016 ).

UKCA is a framework to simulate 3D kinetic and photochemistry (Morgenstern et al. 2009 ; O'Connor et al. 2014 ; Archibald et al. 2020 ; Braam et al. 2022 ). The atmospheric transport of chemical tracers is fully coupled to the UM's large-scale advection, convection, and boundary layer mixing. Here, we limit ourselves to UKCA's gas-phase (photo)chemistry description. The chemical network consists of 21 chemical species connected by 71 reactions (as shown in the appendix of Braam et al. 2022 ). The network describes the Chapman mechanism of ozone formation as well as the catalytic destruction of ozone by the hydrogen oxide and nitrogen oxide catalytic cycles, representing interactive ozone chemistry in the 3D model.

We simulate Proxima Centauri b (Anglada-Escudé et al. 2016 ) as an aqua planet orbiting in a 1:1 spin-orbit resonance around its M-type host star, using the orbital parameters described in Table 1 . Anglada-Escudé et al. ( 2016 ) well-constrained the semimajor axis and orbital period. Given the close-in orbit at 0.0485 AU, a 1:1 spin-orbit resonance or synchronous orbit is a probable scenario (Barnes 2017 ) and results in a rotation rate of 6.501 × 10 −6 rad s −1 . The stellar irradiance follows from Boutle et al. ( 2017 ). Since Proxima Centauri b is non-transiting, we only have a lower limit on its mass from the detection by the radial velocity method of M p sin (i) = |$1.07\ \pm \ 0.06$| M ⨁ (Anglada-Escudé et al. 2016 ; Faria et al. 2022 ), where i is the orbital inclination. Given the unknown actual mass of Proxima Centauri b and to ensure consistency with previous GCM simulations, we follow Turbet et al. ( 2016 ) and assume an actual planet mass of 1.4 M ⨁ . Assuming that Proxima Centauri b has Earth's density (5.5 g cm −3 ), we estimate a corresponding planetary radius of 1.1 R ⨁ and a surface gravity of 10.9 m s −2 . While these configurations are based on the known parameters of Proxima Centauri b, the simulation results can apply more generally to planets with similar sizes and rotation periods in spin-synchronous orbits around M-dwarf stars. We use a horizontal resolution of 2° × 2.5° in latitude and longitude, respectively, with the substellar point at 0° latitude and longitude. We assume the entire surface is covered by a 2.4 m slab ocean mixed layer with a total heat capacity of 10 7 J K −1 m −2 . We simulate an atmosphere extending up to 85 km altitude, divided over 60 vertical levels that are quadratically stretched for enhanced near-surface resolution (Yates et al. 2020 ). Abundances of N 2 , O 2 , and CO 2 (Table 1 ) correspond to pre-industrial Earth levels, and water vapour profiles are determined interactively following evaporation from the slab ocean.

Orbital, planetary, and atmospheric parameters used to configure Proxima Centauri b following Boutle et al. ( 2017 ). Note that the mass fractions of H 2 O and O 3 are interactively calculated; therefore, they are given as a range indicating minimum and maximum values. H 2 O follows from the balance of evaporation, condensation, (photo)chemistry, and O 3 from (photo)chemistry.

We initialize the No-Chemistry setup with the uniform mass mixing ratios of N 2 , CO 2 , and H 2 O from surface evaporation. CO 2 and H 2 O, as radiatively active species, are important factors in the thermodynamic and dynamic state of the atmosphere. For the Chemistry simulation, we also specified a uniform mass mixing ratio for O 2 and included the interactive calculation of ozone chemistry. Ozone is another radiatively active species, and its interactively determined and varying mixing ratios are used in the radiative transfer calculations, potentially affecting the thermodynamic and dynamic state of the atmosphere. We spin up both simulations for ∼20 Earth years to ensure a steady state, diagnosed by radiative balance, surface temperatures, and ozone abundances. After that, we run the simulations for another 30 yr with daily output to analyse Proxima Centauri b's atmospheric dynamics.

We extract two atmospheric variables from the 30-yr Chemistry and No-Chemistry simulations: temperature (T in °K), linked with thermodynamic processes, and wind speed (WS in m s −1 ), related to dynamic processes. In addition, we obtain the vertically integrated ozone column density in DU (1 DU = 2.69 × 10 20 molecules m −2 ) and ozone mass fraction (kg kg −1 ) over the same levels, hereafter OzCol and OzFr, respectively. From these four variables, we use all 60 vertical levels when assessing their vertical profiles and choose one level for computing composite and difference maps. This level corresponds to ∼22 km for T, WS, and OzFr since we find the largest concentration of ozone particles at this level. For OzCol, we use the surface level representing the vertically integrated amount of overhead ozone molecules in the vertical column. We then provide vertical profiles for these four atmospheric variables and compute them for the global, northern, and southern gyre regions. For each variable, simulation, and atmospheric vertical level, we take the temporal median over the entire 30-yr period and then compute the field median. This leaves us with 60 data points for each variable and region representing the atmosphere.

2.2 Dynamical systems metrics

We used a novel dynamical systems method to compute two compound dynamical metrics: the inverse of local co-persistence and local co-dimension, which we refer to as θ T,WS , and d T,WS , respectively. The metric θ T,WS is intuitively a measure of the joint average residence time of two trajectories around two respective states of interest. The lower the value of θ T,WS , the more likely it is that the preceding and future states of the systems will resemble the current states. The metric d T,WS describes the joint evolution of the systems around two respective states of interest and can be interpreted as a proxy for the joint number of degrees of freedom active around the same states (De Luca et al. 2020a , 2020b ; Faranda et al. 2020 ).

Calculating the compound dynamical systems metrics combines Poincaré recurrences with extreme value theory (Freitas et al. 2010 ; Lucarini et al. 2012 ; Faranda et al. 2017 , 2020 ). We referred to the system returning n times close to a previously visited state in the phase space as recurrences . We considered an atmospheric variable, such as T, and a given state of interest, namely a 2D map of a day within the time series of T. Our approach uses the Euclidean distance to quantify how close the state of interest and the recurrences are to one another in the atmospheric phase space (Faranda et al. 2017 ).

We considered a second phase space for the latter variable to extend the dynamical systems analysis to two variables, T and WS. Eventually, we computed joint recurrences around a common state of interest, corresponding to two instantaneous latitude–longitude maps: one for T and one for WS. Once we defined the joint recurrences, we computed θ T,WS , and d T,WS compound metrics for the 60 vertical levels in the Proxima Centauri b's climate model simulations. The final output of such analysis is a value for each metric, daily time-step, and vertical level. This allows us to relate specific metric values to the corresponding geographical patterns of selected atmospheric variables. We further computed the compound dynamical metrics for particular regions of interest: the northern gyre, where atmospheric ozone tends to accumulate, and equatorial western and eastern terminators, which are highly variable (Fig. 1e ). Finally, we defined ‘High’ and ‘Low’ dynamically stable days as the lower and upper 5 per cent of θ T,WS , and d T,WS compound metrics, respectively. For a complete derivation of θ T,WS , and d T,WS we refer the reader to Faranda et al. ( 2020 ).

$Climatology for key variables in the Chemistry and No-chemistry model simulations. (a–b) Temperature (T in °K). (c–d) Wind speed (WS in m s−1). (e) Ozone column density (OzCol in DU). (f) Ozone fraction (OzFr in kg kg−1). Median composites were computed over the 30 yr for Chemistry (a, c, e, f) and No-Chemistry (b, d) simulations. Climatology of (a-d, f) was computed from the ∼22 km level, whereas for (e) from the surface level. In (e) we mark the geographical regions used with rectangles: northern gyre (Lon 146.25°, -93.25°, Lat 39°, 83°); southern gyre (Lon 146.25°, -93.25°, Lat -39°, -83°); equatorial western terminator (Lon -105°, -75°, Lat -15°, 15°); and equatorial eastern terminator (Lon 75°, 105°, Lat -15°, 15°).$

Climatology for key variables in the Chemistry and No-chemistry model simulations. (a–b) Temperature (T in °K). (c–d) Wind speed (WS in m s −1 ). (e) Ozone column density (OzCol in DU). (f) Ozone fraction (OzFr in kg kg −1 ). Median composites were computed over the 30 yr for Chemistry (a, c, e, f) and No-Chemistry (b, d) simulations. Climatology of (a-d, f) was computed from the ∼22 km level, whereas for (e) from the surface level. In (e) we mark the geographical regions used with rectangles: northern gyre (Lon 146.25°, -93.25°, Lat 39°, 83°); southern gyre (Lon 146.25°, -93.25°, Lat -39°, -83°); equatorial western terminator (Lon -105°, -75°, Lat -15°, 15°); and equatorial eastern terminator (Lon 75°, 105°, Lat -15°, 15°).

2.3 Statistical inference

We perform a two-tailed Wilcoxon rank-sum test to assess the statistical significance of the median vertical profiles and difference maps (Mann & Whitney, 1947 ). The test was performed between the Chemistry and No-Chemistry medians, composite data sets, and between compound stable and unstable atmospheric states under the null hypothesis that the medians of the data sets are equal. We then compute the composite maps' corresponding p -values at the grid-point level. To account for Type I errors (or false positives), we apply the Bonferroni correction of p -values (Bonferroni, 1936 ), which divides the original p -values by the total number of statistical tests performed. Lastly, to assess the statistical significance of the standard deviation's vertical profiles, we perform the F-test under the null hypothesis that the variances of the two populations are equal (Snedecor & Cochran, 1989 ). We provide all statistical tests at the 1 percent significance level.

3.1 How does interactive ozone influence the climatology of atmospheric variables?

Fig. 1 shows the 30-yr climatology of four atmospheric variables, i.e. T, WS, OzCol, and OzFr, for both Chemistry and No-Chemistry simulations. We note that the stratospheric temperature for the Proxima Centauri b Chemistry simulation increases globally compared to the No-Chemistry due to the different chemical compositions (Figs. 1a and b ). The ozone layer (Fig. 1e ) and particularly stratospheric ozone (Fig. 1f ) in the Chemistry simulation absorbs incoming ultraviolet radiation and reemits this at infrared wavelengths, leading to warming in the stratosphere. On the other hand, the temperature gradient between the two simulations is kept, meaning that T at higher (northern and southern) latitudes reaches its minimum and gradually increases meridionally towards the equatorial region (Figs. 1a and b ). The wind speed shows a similar spatial distribution for the Chemistry and No-Chemistry climatology (Figs. 1c and d ), with lower wind speed at higher latitudes and an increase towards the equatorial jet. Nevertheless, the jet's intensity changes with weaker eastward winds at the equator for the Chemistry simulation. Taking the surface temperature as a proxy for day-to-night contrasts, we determine a hemispheric contrast (dayside average minus nightside average) of 64 °K and 68 °K for the Chemistry and No-Chemistry climatology, respectively. The smaller day-to-night temperature contrast for the Chemistry climatology may be the reason for a weaker jet considering thermal wind balance. Lastly, both ozone climatologies show similar patterns with the higher concentration of ozone in the northern and southern gyre regions (Figs. 1e and f ). In contrast, for OzFr, we still observe higher ozone values in the proximity of the gyres and across the mid and higher latitudes with a meridional gradient of lower values towards the equator (Fig. 1f ). The difference between OzCol and OzFr climatology is due to the former representing ozone integrated over the entire vertical column, whereas the latter is strictly confined to stratospheric ozone fraction. Spatial variations in the distribution of ozone are driven by stratospheric circulation mechanisms, including an analogue of the Brewer-Dobson circulation that controls the ozone distribution on Earth and the stratospheric dayside-to-nightside circulation for synchronously rotating planets (Braam et al. 2023 ).

The temperature vertical profiles look similar between the global and gyre regions (Figs. 2a–c ). However, both gyres exhibit lower temperatures (∼175 °K) than the global median (∼211 °K) at the surface. Such a temperature difference is noticeable within the first 5–6 km from the surface because these gyres trap air, subject to extensive radiative cooling due to the nightside location. For both Chemistry and No-Chemistry and all three regions, we observe an increase in temperature, then an inversion, and a slight increase up to the top of the atmosphere. A significant difference is that the temperature for the Chemistry simulation is significantly higher (∼+7 °K) than the No-Chemistry one from ∼17 km to ∼50 km in all three regions, probably because at this altitude range, we find the highest ozone mass fraction (Figs. 2j–l ). The vertical profiles for wind speed are also very similar between all the regions in the Chemistry and No-Chemistry simulations (Figs. 2d–f ). However, the wind speed in the Chemistry simulation in the gyres is significantly higher (∼+5 m s −1 ) than the No-Chemistry one. This may be due to multiple complex factors since the location of the gyres and strength of the rotating winds will depend on the radiative forcing and heat transport (e.g. Showman et al. 2013 ; Pierrehumbert & Hammond 2019 ), which slightly change due to the inclusion of ozone. The values of OzCol are higher from ∼0 to ∼20 km over the gyres compared to the global region (Figs. 2g–i ; Braam et al. 2023 ). The OzCol = 0 DU from ∼40 km upwards for all three regions. However, OzFr in all three regions increases from the surface to ∼48 km when it reaches its maximum and then decreases close to 1e -08 kg kg −1 at ∼76 km (Figs. 2j–l ). Between the global and the gyre profiles, we notice a difference close to the peak in OzFr: the vertical distribution of ozone over the gyre regions shows a saddle from ∼48 to ∼60 km due to the formation of a secondary ozone layer on the nightside hemisphere in the absence of ozone photolysis (e.g. Smith & Marsh 2005 ).

$Vertical profiles of four atmospheric variables. (a–c) Temperature – T, (d–f) Wind speed – WS, (g–i) Ozone column – OzCol and (j–l) Ozone fraction – OzFr. The first column represents vertical profiles computed from global medians over the 30 yr. In contrast, the second and third columns represent the same, but for the northern and southern gyres, where the concentration of ozone is higher. (a–f) Profiles of the Chemistry and No-Chemistry simulations. (g–l) Profiles computed from the Chemistry simulation. Circles in (a–f) represent Chemistry and No-chemistry medians that are not significantly different at the 1 percent level. OzFr in (j–l) is plotted on a log10 scale.$

Vertical profiles of four atmospheric variables. (a–c) Temperature – T, (d–f) Wind speed – WS, (g–i) Ozone column – OzCol and (j–l) Ozone fraction – OzFr. The first column represents vertical profiles computed from global medians over the 30 yr. In contrast, the second and third columns represent the same, but for the northern and southern gyres, where the concentration of ozone is higher. (a–f) Profiles of the Chemistry and No-Chemistry simulations. (g–l) Profiles computed from the Chemistry simulation. Circles in (a–f) represent Chemistry and No-chemistry medians that are not significantly different at the 1 percent level. OzFr in (j–l) is plotted on a log 10 scale.

3.2 How does interactive ozone influence the atmospheric dynamics?

Here, we analyse the influence that interactive ozone has on the dynamics of Proxima Centauri b's atmosphere. In this respect, we show the median vertical profiles of the compound dynamical systems metrics (Fig. 3 ). Globally, although relatively small, we find significant differences between the Chemistry and No-Chemistry simulations. A substantial decrease in θ T,WS , and d T,WS is observed below the levels of maximum OzFr and an increase above these levels (compare Figs. 3a , e with Fig. 2j ). Some levels, especially for d T,WS , do not show significant differences. These findings are also apparent in the north gyre (Figs. 3b , f ) and west and east terminators (Figs 3c–d , g–h ). However, for the terminators, we note that Chemistry's and No-Chemistry's co-dimension shows lower values over the entire vertical profile when compared to the global and north gyre regions. We further display the standard deviations vertical profiles of θ T,WS , and d T,WS (Figs. 3i–p ). We provide evidence for significantly larger variability in the atmospheric time series dynamics of Proxima Centauri b when including interactive ozone than not having it. This finding is particularly evident just below the level of maximum ozone accumulation.

Vertical profiles of compound dynamical systems metrics. (a–d) The inverse of local co-persistence ( θ T,WS ). (e–h) Local co-dimension ( d T,WS ) medians for Chemistry and No-Chemistry simulations. (i–p) the same but for the standard deviations. The first column shows the global vertical level values over the 30 yr, the same for the second column but for the northern gyre. The third and fourth columns are for the western and eastern equatorial terminators. The dynamical system metrics are computed from temperature (T) and Wind Speed (WS). Circles represent (a–h) medians and (i–p) standard deviations that are not significantly different at the 1 percent level.

Next, we selected ‘High’ (upper 5 percent) and ‘Low’ (lower 5 percent) θ T,WS , and d T,WS days in the Chemistry and No-Chemistry simulations (Fig. 4 ). Joint low values reflect more stable (in the sense of atmospheric time series variability) atmospheric states, whereas joint higher values are relatively unstable atmospheric configurations. Also, from Fig. 4 , it is possible to notice an increased variability for the Chemistry simulation compared to the No-Chemistry since the data points in the former tend to spread more over the x and y axis compared to the latter.

Scatter plots of θ T,WS (x-axis) and d T,WS (y-axis) for (a) Chemistry and (b) No-Chemistry simulations. The compound dynamical systems metrics are computed from temperature and wind speed at the ∼22 km level. Bottom left and top right shaded areas contain states with θ T,WS and d T,WS < 5th and > 95th percentiles, ‘Low’ and ‘High’, respectively. On the top left of each panel, we show the Spearman's correlation coefficient and p -value.

We calculate field medians or composite maps for the ‘High’ and ‘Low’ atmospheric states from Fig. 4 . Next, we analysed the differences between the median composite maps of temperature and wind speed for ‘High’ and ‘Low’ atmospheric states in the Chemistry and No-chemistry simulations at the vertical level corresponding to ∼22 km altitude (Figs. 5 and 6 ). Fig. 5 shows the composite and difference maps for temperature. Here, the Chemistry simulation has higher temperatures in both ‘High’ and ‘Low’ states than the No-Chemistry simulation, thanks to the large abundance of OzFr at this level leading to radiative heating. In addition, temperature patterns are symmetrical between the northern and southern regions of Proxima Centauri b, with lower values at higher latitudes and higher values across the tropical region (Figs. 5a–d ). Difference maps of High–Low states point towards enhanced temperatures for the ‘High’ states over the planet, with both Chemistry and No-Chemistry simulations also showing slightly higher values in the gyre regions (up to + 6 °K). This pattern is more pronounced in the former simulation (Figs. 5e–f ), and this is due to a higher accumulation of ozone in the ‘High’ states compared to ‘Low’ ones. Difference maps for Chemistry–No-Chemistry shows positive and significant temperature differences over the entire planet for both ‘High’ and ‘Low’ states (up to +15 °K) again due to ozone's radiative heating. Nevertheless, we note that ‘High’ states' positive temperature differences are observed over the mid-latitudes, whereas the ‘Low’ states positive temperature differences occur over the tropics (Figs. 5g–h ).

Composite and difference maps for temperature (T) at the ∼22 km level. (a–b) Composite maps are field medians computed from Chemistry and No-Chemistry joint co-persistence and co-dimension states (defined in Fig. 4 ), which are > 95th percentile – ‘High,’ and (c–d) the same but for states < 5th percentile – ‘Low.’ (e–f) Difference maps were computed by subtracting the ‘Low’ from the ‘High’ composites for Chemistry and No-Chemistry simulations. (g–h) Difference maps were calculated by subtracting the No-Chemistry from the Chemistry composites for both ‘High’ and ‘Low’ states. In (e–h), stippling represents areas that are not significantly different at the 1 percent level.

Same as Fig. 5 but for Wind Speed (WS).

Composite maps for wind speed show very similar and symmetrical patterns of high wind speed over the tropics (corresponding to the equatorial jet) and lower wind speed in the higher latitudes, with the only difference being the Chemistry ‘Low’ states simulation that has weaker wind speed values in the tropics (Figs. 6a–d ). This shows that the addition of interactive ozone substantially affects the persistent atmospheric states by weakening the equatorial jet (Fig. 6c ). This may imply that the mechanism that drives the equatorial jet on synchronously rotating exoplanets (Showman et al. 2013 ) diminishes in strength with the inclusion of ozone. This causes less pronounced gyres, which we will show with the composite maps of the OzCol below. In Section 4 , we will put these findings on the equatorial jet and gyres into the context of the large-scale atmospheric circulation. Difference maps between High–Low show positive and high wind speed differences (up to +35 m s −1 ) from ∼50 |$^\circ $| S to ∼50 |$^\circ $| N, with higher values for the Chemistry simulation than the No-Chemistry one. In the Chemistry simulation, we also provide evidence for negative wind speed differences (up to −10 m s −1 ) over the gyres (Figs. 6e–f ). Difference maps for Chemistry–No-Chemistry ‘High’ states show positive wind speed values (up to +15 m s −1 ) over most of the planet and negative differences in the gyres (up to −5 m s −1 ). The same maps for ‘Low’ states show stronger negative wind speed differences (up to −25 m s −1 ) from ∼50 |$^\circ $| S to ∼50 |$^\circ $| N, and weak positive differences for most of the remaining planet (Figs. 6g and h ).

Similar to temperature and wind speed, we also computed the composite and difference maps for OzCol and OzFr, with the only difference being that here, we only use the Chemistry simulation. When looking at the Chemistry composite maps of both OzCol and OzFr during ‘High’ and ‘Low’ atmospheric states, we observe that higher states lead to higher accumulation of ozone over both the northern and southern gyres (Figs. 7a–b , d–e ), driven by a stratospheric dayside-to-nightside circulation (Braam et al. 2023 ). However, for OzFr, higher values of ozone are also found from 50 |$^\circ $| S to 90 |$^\circ $| S and from 50 |$^\circ $| N to 90 |$^\circ $| N, caused by the combined effect of atmospheric circulation and weaker chemical loss processes of ozone the further we move from the substellar point (Fig. 7d ) (e.g. Chen et al. 2018 ; Yates et al. 2020 ). Such spatial patterns are, therefore, reflected when looking at the difference maps of High–Low states. Indeed, we find positive differences for the ‘High’ states of OzCol over the northern and southern gyres, and the same is true for OzFr, but with more prominent enhanced values at higher latitudes. Moreover, the former variable shows negative differences over most of the remaining planet, and the latter shows negative and non-significant differences from ∼10 |$^\circ $| S to ∼10 |$^\circ $| N (Figs. 7c and f ). Hence, the composite maps of temperature, wind speed, and ozone together illustrate that the stratospheric dayside-to-nightside circulation that drives ozone accumulation over the gyres is most prevalent during the ‘High’ states. On the other hand, the ‘Low’ states represent a relatively weak dayside-to-nightside circulation, illustrated by less pronounced gyres and a more homogeneous ozone distribution.

Same as Fig. 5 but for OzCol (a–c) and OzFr (d–f). Note that the figure shows the Chemistry simulation. In (c, f), stippling represents areas that are not significantly different at the 1 percent level.

The atmospheres of tidally locked terrestrial (or Earth-like) exoplanets are close to being characterized by the advent of the JWST . This opens new frontiers in searching for habitable planets outside our solar system and incentivizes new ground-breaking investigations from the astrophysical, astronomical, and Earth sciences communities. With this work, we showed that adding an interactive ozone module to climate model simulations of Proxima Centauri b globally increases the stratospheric temperature and induces regionally varying effects on the surface temperature, including increased surface temperature in the gyre regions and a decrease of the dayside-to-nightside temperature contrast by 4 |${}^\circ $| K. Adding ozone in the simulations resulted in similar albeit significantly different median vertical compound dynamics of temperature and wind speed compared to not having interactive ozone, with more differences observed when assessing their standard deviations, indicating enhanced variability. We found that highly dynamically stable states have warmer gyre regions, stronger wind speeds, and enhanced ozone accumulation in the gyres.

Our findings can be summarized and contextualized as follows:

Chemistry model simulations show higher average stratospheric temperatures compared to No-Chemistry ones. We attribute this difference to the ozone's radiative heating, a similar mechanism that we observe in the Earth's stratosphere due to the absorption and emission of terrestrial IR radiation and absorption of solar radiation in the UV and visible spectrums (Dopplick, 1972 ; Park & London 1974 ; Fishman et al. 1979 ). The temperature difference was in the order of + 7 |${}^\circ $| K and can be observed from ∼17 to ∼50 km above the surface. The altitude range where the highest ozone mass fractions are found and where ozone is most likely to interact with the incoming stellar radiation. This agrees qualitatively with previous studies on the effect of ozone on vertical temperature structures, with Boutle et al. ( 2017 ) also reporting warming of the stratosphere for Proxima Centauri b. However, the quantitative warming effect of ozone varies. Godolt et al. ( 2015 ) have shown that ozone heats the stratosphere of planets around F-type stars but does not significantly affect the vertical temperature structure of planets around K-type stars. The spectral dependence is further illustrated by Kozakis et al. ( 2022 ), showing that ozone abundances and the amount of stratospheric heating depend on the total amount of UV flux received from the host star and the distribution over wavelengths. The amount of incoming near-ultraviolet (NUV: 200<λ<400 nm) radiation determines ozone production, while the far-ultraviolet (FUV: 91<λ<200 nm) radiation determines the amount of oxygen photolysis and, thus, ozone production. Hence, the total UV flux and the FUV/NUV flux ratio are important metrics of ozone photochemistry and its impact on climate dynamics. Even if we only consider the photolysis wavelengths in UM-UKCA (λ>177 nm), the MUSCLES spectrum used in this study had an FUV/NUV flux ratio of 0.012, a higher ratio than any of the host stars from Kozakis et al. ( 2022 ) and sufficient to drive mild stratospheric heating. The altitude that corresponds to the stratospheric ozone layer and its effect on the vertical temperature structure depends on the initial amount of O 2 present (Cooke et al. 2022 , 2023 ).

The gyres of Proxima Centauri b show a ‘saddle’ in the OzFr vertical profile, which the global ones do not. Since the nightside is devoid of incoming radiation, the balance of ozone formation (three-body reaction O + O 2 + M → O 3 + M) and ozone destruction (driven by photolysis and reaction with atomic O) in the Chapman mechanism shifts to more ozone production. This leads to a secondary nightside ozone layer at high altitudes. A mesospheric secondary ozone layer is also present during night-time on Earth (e.g. Smith & Marsh 2005 ). Its permanent presence on the nightside of synchronously rotating planets was also found in simulations of Earth in a synchronous orbit (Proedrou & Hocke 2016 ).

Adding interactive ozone to the simulations affects the compound dynamics of temperature and wind speed. Both Chemistry and No-Chemistry simulations showed similar patterns of θ T, WS , and d T, WS vertical profiles when taking the medians over the 30 yr in the entire planet, northern gyre, and western and eastern terminators. However, most of the vertical profiles showed statistically significant differences. We noticed more variability for the Chemistry simulation than the No-Chemistry one when considering the standard deviations. We therefore suggest that including interactive ozone improves the simulation of Proxima Centauri b's atmospheric time series dynamics, since increased variability broadly means a more realistic simulation of the atmospheric dynamics. Ozone, as a single interactive species, can significantly impact the compound dynamical metrics on both global and regional scales, depending on the stellar flux distribution and the initial oxygen content. From Fig. 7 of Kozakis et al. ( 2022 ), ozone heats the atmosphere more for planets orbiting earlier stellar types. This relates to the FUV/NUV ratio, but perhaps even more so simply to the magnitude of the UV radiation, as shown in their Fig. 2.

Highly dynamically stable states show larger ozone column and fraction over the gyres than ‘Low’ states. ‘Low’ states reflect stalling (or more persistent) weather patterns compared to the more variable ‘High’ states. The Chemistry simulation showed a significant increase in OzCol in ‘High’ states over the northern and southern gyres’ regions on the nightside, along with increased OzFr. On synchronously rotating planets, the day-nightside heating contrast generates an overturning circulation with rising air on the dayside and subsiding air on the nightside (Showman et al. 2013 ; Hammond & Lewis, 2021 ). The overturning circulation has a strong tropospheric component but extends into the stratosphere, with implications for photochemically generated ozone at these altitudes (Braam et al. 2023 ). These vertical motions, in turn, contribute to forming standing, planetary-scale Kelvin and Rossby waves, the latter of which manifest as the nightside gyres in our simulations (Showman et al. 2013 ). Photochemistry is not active in these non-irradiated gyre regions, and the enhanced OzCol is regulated by the stratospheric overturning circulation (Braam et al. 2023 ). This picture implies that the large-scale circulation, atmospheric variability, and OzCol variations are linked. We postulate that ‘High’ θ T, WS , and d T, WS states may represent migration in time for the gyres (see also Cohen et al. 2023 ), oscillating in their central longitude so that naturally, these states show enhanced variability. Furthermore, we postulate that these ‘High’ states have a particularly strong overturning circulation, enhancing the amount of ozone trapped in the gyres.

Wind speed for Chemistry ‘Low’ states is much lower than for ‘High’ states.

The existence of the gyres is related to the mechanism to form equatorial super-rotating jets on synchronous exoplanets since the planetary-scale waves (including the Rossby waves, which the gyres are lows of) pump eastward momentum equator-wards (Showman & Polvani 2011 ). Given this mechanism to generate the jets, we suggest that the higher wind speed for ‘High’ states in our Chemistry simulation fits this complete dynamical picture and the finding of enhanced OzCol in the gyres for ‘High’ states. The simulations show pronounced and vigorous gyres and a strong equatorial jet for ‘High’ states. In contrast, the gyres are less pronounced in the ‘Low’ states, corresponding to less eastward momentum flowing equatorwards, resulting in a substantially weaker jet.

Our results demonstrated the value of compound dynamical systems metrics to elucidate variability in the atmospheres of exoplanets. They can be extended beyond Proxima Centauri b to other Earth-like exoplanets. Our framework also has potential applications with future exoplanet observations, obtained, for example, by the JWST , the Habitable Worlds Observatory, and the Large Interferometer for Exoplanets, since they will contribute to constraining the climate state, dynamics, and potential habitability of Earth-like exoplanets (Hochman et al. 2022 , 2023 ; Quanz et al. 2022 ). Indeed, understanding how ozone impacts climate dynamics and its observations on exoplanets is crucial for grasping the potential habitability of distant worlds. Ozone plays a vital role in shielding an exoplanet from harmful UV radiation. The presence or absence of ozone can provide valuable insights into the composition and stability of exoplanets’ atmospheres. By studying ozone and its interactions within different atmospheric environments, we can interpret atmospheric signatures observed in exoplanet atmospheres, helping us to identify conditions conducive to life as we know it (Cole et al. 2020 ; Ben-Israel et al. 2024 ). Furthermore, understanding ozone dynamics aids in predicting how atmospheric changes, both natural and anthropogenic, may impact habitability on Earth and beyond, guiding our search for potentially habitable exoplanets in the vast Universe. As a caveat, we acknowledge the fact that the actual radius of Proxima Centauri b is unknown because no transit has been detected so far (Kipping et al. 2017 ). Therefore, the planet may not be an Earth-like exoplanet (Brugger et al. 2017 ). We envisage future works on the impact of an entire interactive chemistry module on the climate dynamics of Earth-like exoplanets, with the case study being, for instance, Proxima Centauri b and TRAPPIST-1e. In addition, future work should also include a variety of host stars and FUV/NUV ratios and the effect of varying initial O 2 abundances.

PDL was funded by the European Union's Horizon Europe Research and Innovation Program under Grant Agreement 101059659. MB is part of the CHAMELEON MC ITN EJD, which received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 860470. The Israel Science Foundation (grant #978/23) funds the contribution of AH. We gratefully acknowledge using the MONSooN2 system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, as a strategic partnership between the Met Office and the Natural Environment Research Council. The simulations were performed as part of the project space ‘Using UKCA to investigate atmospheric composition on extra-solar planets (ExoChem)’ with Principal Investigator Paul Palmer.

The authors declare no competing interests.

The time series of the compound dynamical systems’ metrics are available upon reasonable request to the corresponding author.

Anglada-Escudé G. et al. 2016 , Nature , 536 , 437 10.1038/nature19106

Archibald A. T. et al. 2020 , Geosci. Model. Dev. , 13 , 1223 10.5194/gmd-13-1223-2020

Barnes R. 2017 , Celest. Mech. Dyn. Astron. , 129 , 509 10.1007/s10569-017-9783-7

Ben-Israel M. , Holder R. M. , Nelson L. L. , Smith E. F. , Kylander-Clark A. R. C. , Ryb U. 2024 , Nat. Commun. , 15 , 2892 10.1038/s41467-024-46660-7

Bonferroni C. 1936 . Pubblicazioni Del R. Istituto Superiore Di Scienze Economiche e Commerciali Di Firenze , 8 , 3

Boutle I. A. et al. 2017 , A&A , 601 , A120

Braam M. , Palmer P. I. , Decin L. , Cohen M. , Mayne N. J. 2023 , MNRAS , 526 , 263 10.1093/mnras/stad2704

Braam M. et al. 2022 , MNRAS , 517 , 2383 10.1093/mnras/stac2722

Brown A. R. , Beare R. J. , Edwards J. M. , Lock A. P. , Keogh S. J. , Milton S. F. , Walters D. N. 2008 , Boundary Layer Meteorol. , 128 , 117 10.1007/s10546-008-9275-0

Brugger B. , Mousis O. , Deleuil M. , Deschamps F. 2017 , ApJ , 850 , 93 10.3847/1538-4357/aa965a

Chapman A. 1930 . London Edinburgh Dublin Phil. Magaz. J. Sci. , 10 , 369

Chen H. , Wolf E. T. , Kopparapu R. , Domagal-Goldman S. , Horton D. E 2018 , ApJ , 868 , L6 10.3847/2041-8213/aaedb2

Chen H. , Zhan Z. , Youngblood A. , Wolf E. T. , Feinstein A. D. , Horton D. E. 2021 , Nat. Astron. , 5 , 298 10.1038/s41550-020-01264-1

Cohen M. , Bollasina M. A. , Palmer P. I. , Sergeev D. E. , Boutle I. A. , Mayne N. J. , Manners J. 2022 , ApJ , 930 , 152 10.3847/1538-4357/ac625d

Cohen M. , Bollasina M. A. , Sergeev D. E. , Palmer P. I. , Mayne N. J. 2023 , Planet. Sci. J. , 4 , 68 10.3847/PSJ/acc9c4

Cole D. B. , Mills D. B. , Erwin D. H. , Sperling E. A. , Porter S. M. , Reinhard C. T. , Planavsky N. J. 2020 , Geobiology , 18 , 260 10.1111/gbi.12382

Cooke G. J. , Marsh D. R. , Walsh C. , Black B. , Lamarque J.-F. 2022 , R. Soc. Open Sci. , 9 , 211165

Cooke G. J. , Marsh D. R. , Walsh C. , Rugheimer S. , Villanueva G. L. 2023 , MNRAS , 518 , 206 10.1093/mnras/stac2604

Delrez L. et al. 2022 , A&A , 667 , A59

De Luca P. , Messori G. , Faranda D. , Ward P. J. , Coumou D. 2020a . Earth Syst. Dyn. , 11 , 793 10.5194/esd-11-793-2020

De Luca P. , Messori G. , Pons F. M. E. , Faranda D. 2020b . Q. J. R. Meteorolog. Soc. , 146 , 1636

Des Marais D. J. et al. 2002 , Astrobiology , 2 , 153 10.1089/15311070260192246

Domagal-Goldman S. D. , Segura A. , Claire M. W. , Robinson T. D. , Meadows V. S. 2014 , ApJ , 792 , 90 10.1088/0004-637X/792/2/90

Dopplick T. G. 1972 , J. Atmos. Sci. , 29 , 1278 10.1175/1520-0469(1972)029<1278:RHOTGA>2.0.CO;2

Edwards J. M. , Slingo A. 1996 , Q. J. R. Meteorolog. Soc. , 122 , 689 10.1002/qj.49712253107

Faranda D. , Messori G. , Yiou P. 2017 , Sci. Rep. , 7 , 41278

Faranda D. , Messori G. , Yiou P. 2020 , Clim. Dyn. , 54 , 2187 10.1007/s00382-019-05106-3

Faria J. P. et al. 2022 , A&A , 658 , A115

Fishman J. , Ramanathan V. , Crutzen P. J. , Liu S. C. 1979 , Nature , 282 , 818 10.1038/282818a0

France K. et al. 2016 , ApJ , 820 , 89 10.3847/0004-637X/820/2/89

Freitas A. C. M. , Freitas J. M. , Todd M. 2010 , Probab. Theory Relat. Fields , 147 , 675 10.1007/s00440-009-0221-y

Gillon M. et al. 2017 , Nature , 542 , 456 10.1038/nature21360

Godolt M. et al. 2015 , Planet. Space Sci. , 111 , 62 10.1016/j.pss.2015.03.010

Gregory D. , Rowntree P. R. 1990 , Mon. Weather Rev. , 118 , 1483 10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2

Hammond M. , Lewis N. T. 2021 , Proc. Natl. Acad. Sci. , 118 , e2022705118 10.1073/pnas.2022705118

Hochman A. , Alpert P. , Harpaz T. , Saaroni H. , Messori G. 2019 . Sci. Adv. , 5 , eaau0936 10.1126/sciadv.aau0936

Hochman A. , Alpert P. , Kunin P. , Rostkier-Edelstein D. , Harpaz T. , Saaroni H. , Messori G. 2020 , Clim. Dyn. , 54 , 561 10.1007/s00382-019-05017-3

Hochman A. , De Luca P. , Komacek T. D. 2022 , ApJ , 938 , 114 10.3847/1538-4357/ac866f

Hochman A. , Komacek T. D. , De Luca P. 2023 , Sci. Rep. , 13 , 11123 10.1038/s41598-023-38026-8

Isaksen I. S. A. et al. 2009 , Atmos. Environ. , 43 , 5138 10.1016/j.atmosenv.2009.08.003

Kipping D. M. et al. 2017 , AJ , 153 , 93 10.3847/1538-3881/153/3/93

Komacek T. D. , Abbot D. S. 2019 , ApJ , 871 , 245 10.3847/1538-4357/aafb33

Kossakowski D. et al. 2023 , A&A , 670 , A84

Kozakis T. , Mendonça J. M. , Buchhave L. A. 2022 , A&A , 665 , A156

Lock A. P. , Brown A. R. , Bush M. R. , Martin G. M. , Smith R. N. B. 2000 , Mon. Weather Rev. , 128 , 3187 10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2

Loyd P. et al. 2016 , ApJ , 824 , 102 10.3847/0004-637X/824/2/102

Lucarini V. , Faranda D. , Wouters J. 2012 , J. Stat. Phys. , 147 , 63 10.1007/s10955-012-0468-z

Mann H. B. , Whitney D. R. 1947 , Ann. Math. Stat. , 18 , 50 10.1214/aoms/1177730491

Masson-Delmotte V. et al. 2021 , IPCC, 2021: Summary for Policymakers: Climate Change 2021–The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change . Cambridge University Press , Cambridge, United Kingdom and New York, NY, USA

Google Scholar

Google Preview

Mayne N. J. et al. 2014 , A&A , 561 , A1

Mayne N. J. et al. 2014 , Geosci. Model. Dev. , 7 , 3059 10.5194/gmd-7-3059-2014

McCulloch D. et al. 2023 , Geosci. Model. Dev. , 16 , 621 10.5194/gmd-16-621-2023

Morgenstern O. , Braesicke P. , O'Connor F. M. , Bushell A. C. , Johnson C. E. , Osprey S. M. , Pyle J. A. 2009 , Geosci. Model. Dev. , 2 , 43 10.5194/gmd-2-43-2009

O'Connor F. M. et al. 2014 , Geosci. Model. Dev. , 7 , 41 10.5194/gmd-7-41-2014

Park J. H. , London J. 1974 , J. Atmos. Sci. , 31 , 1898 10.1175/1520-0469(1974)031<1898:OPARHO>2.0.CO;2

Pierrehumbert R. T. , Hammond M. 2019 , Annu. Rev. Fluid Mech. , 51 , 275 10.1146/annurev-fluid-010518-040516

Proedrou E. , Hocke K. 2016 , Earth Planets Space , 68 , 96 10.1186/s40623-016-0461-x

Quanz S. P. et al. 2022 , A&A , 664 , A21

Ramanathan V. et al. 1987 , Rev. Geophys. , 25 , 1441 10.1029/RG025i007p01441

Ridgway R. J. et al. 2023 , MNRAS , 518 , 2472 10.1093/mnras/stac3105

Rodriguez J. E. et al. 2020 , AJ , 160 , 117 10.3847/1538-3881/aba4b3

Schwieterman E. W. et al. 2018 , Astrobiology , 18 , 663 10.1089/ast.2017.1729

Segura A. et al. 2005 , Astrobiology , 5 , 706 10.1089/ast.2005.5.706

Segura A. et al. 2003 , Astrobiology , 3 , 689 10.1089/153110703322736024

Selsis F. , Despois D. , Parisot J.-P. 2002 , A&A , 388 , 985

Sergeev D. E. , Lambert F. H. , Mayne N. J. , Boutle I. A. , Manners J. , Kohary K. 2020 , ApJ , 894 , 84 10.3847/1538-4357/ab8882

Showman A. P. , Polvani L. M. , 2011 , Astrophys. J. , 738 , 71

Showman A. P. , Wordsworth R. D. , Merlis T. M. , Kaspi Y. , Dotson R. 2013 , in Mackwell S. J. , Simon-Miller A. A. , Harder J. W. , Bullock M. A. , eds, Comparative Climatology of Terrestrial Planets . University of Arizona Press , Tucson , p. 277 . Retrieved from http://www.jstor.org/stable/j.ctt183gz90.17

Smith A. K. , Marsh D. R. , 2005 , J. Geophys. Res.: Atmos. , 110 , D23305 10.1029/2005JD006298

Snedecor G. W. , Cochran W. G. 1989 , Statistical Methods (8th ed.). Iowa State University Press , Ames, IA

Suissa G. , Mandell A. M. , Wolf E. T. , Villanueva G. L. , Fauchez T. , Kopparapu R. k. 2020 , ApJ , 891 , 58 10.3847/1538-4357/ab72f9

Turbet M. et al. 2016 , A&A , 596 . A112

Vakrat E. , Hochman A. 2023 , Q. J. R. Meteorolog. Soc. , 149 , 3593 10.1002/qj.4575

Wedler M. , Pinto J. G. , Hochman A. 2023 , Sci. Total Environ. , 870 , 161883 10.1016/j.scitotenv.2023.161883

Wilson D. R. , Bushell A. C. , Kerr-Munslow A. M. , Price J. D. , Morcrette C. J. 2008 , Q. J. R. Meteorolog. Soc. , 134 , 2093 10.1002/qj.333

Wood N. et al. 2014 , Q. J. R. Meteorolog. Soc. , 140 , 1505 10.1002/qj.2235

Yang J. , Cowan N. B. , Abbot D. S. 2013 , ApJ , 771 , L45 10.1088/2041-8205/771/2/L45

Yates J. S. , Palmer P. I. , Manners J. , Boutle I. , Kohary K. , Mayne N. , Abraham L. 2020 , MNRAS , 492 , 1691 10.1093/mnras/stz3520

Youngblood A. et al. 2016 , ApJ , 824 , 101 10.3847/0004-637X/824/2/101

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Recent Advances in Agent-Based Tsunami Evacuation Simulations: Case Studies in Indonesia, Thailand, Japan and Peru

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Published: 20 May 2015
Volume 172 , pages 3409–3424, ( 2015 )

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Erick Mas 1 ,
Shunichi Koshimura 1 ,
Fumihiko Imamura 1 ,
Anawat Suppasri 1 ,
Abdul Muhari 2 &
Bruno Adriano 3

69 Citations

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As confirmed by the extreme tsunami events over the last decade (the 2004 Indian Ocean, 2010 Chile and 2011 Japan tsunami events), mitigation measures and effective evacuation planning are needed to reduce disaster risks. Modeling tsunami evacuations is an alternative means to analyze evacuation plans and possible scenarios of evacuees’ behaviors. In this paper, practical applications of an agent-based tsunami evacuation model are presented to demonstrate the contributions that agent-based modeling has added to tsunami evacuation simulations and tsunami mitigation efforts. A brief review of previous agent-based evacuation models in the literature is given to highlight recent progress in agent-based methods. Finally, challenges are noted for bridging gaps between geoscience and social science within the agent-based approach for modeling tsunami evacuations.

Defining Disaster: An Evolving Concept

A review on applications of urban flood models in flood mitigation strategies

A systematic scoping review of the Social Vulnerability Index as applied to natural hazards

Avoid common mistakes on your manuscript.

1 Introduction

The 2004 Indian Ocean tsunami (IOT) was one of the deadliest disasters in recent history due to the extreme tsunami heights combined with insufficient warning, awareness, and early evacuation responses. Since its occurrence, the event has been used as a learning tool. This event likely marked the first time that people watched videos or pictures of a devastating tsunami event. Among those pictures, Synolakis and Bernard ( 2006 ) noted the surprising images of tourists in Phuket, Thailand, watching the approaching tsunami without taking any protective action. Unfortunately, the Indian Ocean did not have a tsunami warning system at that time, so no warnings could reach the people in the affected areas. However, would these people have reacted to warning information? This is an important question because tsunami risk involves not only a hazard assessment but also a social component of human behavior because evacuation is the best option for saving lives during a tsunami ( Shuto 2009 ).

Similarly, the 2011 Great East Japan tsunami (GEJT) was one of the most destructive tsunami events in modern history, although it provided lessons for preparing for future events. The large inundation and tsunami heights ( Suppasri et al. 2012a ) destroyed several towns and villages along the Japanese coast. However, the survival rate of the people living in the inundated areas was 96 % ( Suppasri et al. 2012b ; Fraser et al. 2012 ).

Fatality rates were lower for the 2011 GEJT event, compared with the 2004 IOT in Indonesia ( Suppasri et al. 2014 ; Mas et al. 2013b ), despite the numerous towns struck by larger tsunami heights. What caused these different outcomes? A plausible answer could be that not only structural countermeasures but also rapid dissemination of warning information, disaster education, tsunami awareness, and, in particular, evacuation responses contributed to strongly reducing the casualties in the 2011 GEJT. Both tsunami events confirmed the importance of early evacuation and tsunami awareness and the need to develop much more resilient communities with effective evacuation plans.

Therefore, following these lessons provided by the most destructive tsunami events in recent times, we aim to highlight the importance of tsunami evacuation planning and to present a tool to support such an activity. Four international case studies were chosen to describe practical applications of casualty and bottleneck estimation and analyses of vehicles in evacuation, human behavior and shelter demand, all of which contribute to tsunami mitigation and evacuation planning. The use of tsunami evacuation simulations attempts to support reconstruction activities in Japan and efforts to develop resilient communities in at-risk areas in Indonesia, Thailand and Peru.

2 Tsunami Evacuation Modeling

The second part of this section presents a comprehensive summary of agent-based models for tsunami evacuation, although the section does not provide an extensive review of all the various models available. We wish to clarify our motivation for focusing on the agent-based approach instead of other methods. First, what are agent-based models?

Agent-based model (ABM): it is a bottom–up approach in which each agent or individual part of a system is modeled as an autonomous decision-making entity. Each agent follows particular rules according for their role in the system; thus, they are able to execute various behaviors. The interaction of these parts and their behaviors develops into a macro description of the system based on an emergent phenomenon.

ABMs are flexible and capture the emergent phenomena from a natural description of a system such as a community and its individual members. Therefore, an ABM is ideal for simulating disaster emergency evacuations ( Munadi et al . 2012 ) because it provides valuable insight into the mechanisms and behavior that result in jamming or casualties.

There are several other approaches used in tsunami evacuation models that can be found in the literature, e.g., genetic algorithms ( Park et al. 2012 ), Geographic Information System (GIS) ( Sugimoto et al . 2003 ; Clerveaux et al. 2008 ; Wood and Schmidtlein 2012 ; Dewi 2012 ; Freire et al. 2012 ; Gonzalez - Riancho et al. 2013 ) distinct or discrete element methods (DEMs) ( Abustan et al. 2012 ), and system dynamic approaches ( Simonovic and Ahmad 2005 ; Kietpawpan 2008 ). However, GIS approaches are traditionally top–down methods that use aggregate descriptions of a system. In evacuation, the complexity and diversity of behaviors that are interrelated produce dynamic changes that GIS models are not well suited to tracking ( Castle and Crooks 2006 ) unless they incorporate micro-scale components provided by ABMs ( Johnston 2013 ). Similarly, system dynamic approaches might be able to track system changes throughout a simulation; however, they lack spatial complexity and require many assumptions about the system if applied to simulating evacuation procedures. In addition, DEMs apply physical laws, such as fluid dynamics, to the evacuees. Such a representation may provide a good description of large crowd behavior during an evacuation ( Helbing et al. 2005 ); however, not all phenomena follow Newtonian motion because psychological forces or sudden changes in motion are likely to occur.

In summary, we choose to focus on ABM due to its benefits over other model techniques for capturing emergent phenomena and providing a natural description of a system. ABM is also flexible for scaling, tuning agent complexity and behavior, and has the capability to use modern data with a higher level of detail.

2.1 Agent-Based Tsunami Evacuation Modeling

One of the first tsunami evacuation models published ( Usuzawa et al. 1997 ) can be found in the Japanese literature. The model simulates the evacuation of Aonae on Okushiri Island, which was affected by the 1993 Hokkaido earthquake. In this initial model, a network modeling approach, which is commonly used to simulate evacuations from hurricanes, floods, nuclear disasters and fires in buildings ( Watts 1987 ), was used as the modeling method. Following the research of Usuzawa et al. ( 1997 ), Imamura et al. ( 2001 ) described another network model for the same area, but this study included a different start time for each agent in the evacuation and control parameters to distinguish pedestrians from vehicles in the calculation. The computational method uses sequential programming, so all of the evacuees move at the same time and decisions are scheduled only at intersections or the nodes of the network. In addition, in this model, agents jump from node to node in the step that corresponds to the relationship between their speed and road length, limiting the analysis of crowding or the dynamics of pedestrians during the simulation. Later, Fujioka et al. ( 2002 ) proposed a much more complex representation of evacuees by formally introducing multi-agent systems to tsunami evacuation simulations. The model represented evacuees and guides as agents with different objectives and communication capabilities. This study was one of the most advanced representations of human behavior in the tsunami evacuation field at the time. Although the model uses a combination of network- and grid-based roads, the agent collision avoidance dynamics were limited because the speed was fixed for all of the agents throughout the simulation. In addition, the model returned to the total compliance approach for the evacuation start time, which is less realistic than the different timing approach used by Imamura et al. ( 2001 ). Saito and Kagami ( 2004 ) presented an agent-based model similar to the model of Imamura et al. ( 2001 ); they modeled the movement of agents using results from a questionnaire about residents’ preferences for the start time of evacuations. However, preference surveys alone are not sufficient to describe evacuees’ behavior because post-event surveys have demonstrated that actual behavior may differ from the expected behavior. The importance of human behavior in tsunami evacuation simulations gained the attention of researchers in the field ( Suzuki and Imamura 2005 ). Nonetheless, from the point of view of pedestrian dynamics, tsunami evacuation models lacked a clear approach for path finding because the main rule given to agents was to proceed to the next highest node in the network instead of searching for the shortest path or a specific goal. The model developed by Katada et al. ( 2004 ) incorporated a routing method to find the shortest path in the network. Moreover, while previous studies tried to answer specific research questions, Katada’s model moved tsunami evacuation simulations from scientific research to practical applications for tsunami mitigation, particularly for disaster education and outreach. The Tsunami Scenario Simulator ( Katada et al. 2000 ) was developed as a GIS model to investigate information dissemination during disasters; it was later modified ( Katada and Kuwasawa 2006 ) into the Tsunami Dynamic Hazard Map for disaster education purposes. This tool allowed citizens to dynamically observe the consequences of many of their potential evacuation decisions. Since this modification, the literature on tsunami evacuation models using agent-based techniques has increased considerably ( Meguro and Oda 2005 ; Nozawa et al. 2006 ; Watanabe and Kondo 2009 ; Goto et al. 2012 ).

Increases in computational power have enabled the analysis of large amounts of data and have made it possible to shift the modeling approach from network-based ( Imamura et al. 2001 ; Lämmel et al. 2010 ) to grid-based ( Mas et al. 2012 ), potential fields ( Meguro and Oda 2005 ), or hybrid modeling approaches to improve realism in pedestrian dynamics and collision avoidance behaviors ( Fujioka et al. 2002 ; Kato et al. 2009 ; Nguyen et al. 2012a ). Gradually, the research methodology has moved toward using much more data with finer levels of detail through agent-based modeling and high-performance computing ( Wijerathne et al. 2013 ). In addition, the importance of human behavior in evacuations is increasingly being considered in models ( Suzuki and Imamura 2005 ; Mas et al. 2012 ; Fujioka et al. 2002 ). Following the 2004 IOT and 2011 GEJT, tsunami evacuation modelers have focused on providing practical applications of simulations to solve the particular problems that were observed in these events, such as evacuation timing, bottlenecks and traffic congestion from vehicle evacuations, shelter locations, evacuee behavior, and risk communication, among other factors. In addition, reconstruction in tsunami-affected areas requires new evacuation plans that follow new urban layouts. Effective evacuation plans to be executed under new urban spatial conditions can initially be analyzed and evaluated using evacuation models. In the next section, we describe applications of an ABM tsunami evacuation model that integrate tsunami inundation features and human behavior during evacuations.

3 Case Studies of the Practical Applications of Tsunami Evacuation Simulations

The simulation of tsunami evacuations is becoming important to investigate potential responses to warnings, estimate potential casualties, evaluate evacuation plans and explore options for tsunami mitigation. These experiments are guiding the development of more effective educational and mitigation programs in many countries ( Bernard et al. 2006 ). Here, we demonstrate several examples of case studies of ABM tsunami evacuation modeling applied to verify, analyze and evaluate actual or predicted tsunami scenarios for evacuations. A large body of literature on agent-based tsunami evacuation models is available for several areas ( Wijerathne et al. 2013 ; Goto et al. 2012 ; Nguyen et al. 2012b ; Abustan et al. 2012 ; Lämmel et al. 2010 ; Meguro and Oda 2005 ; Katada et al. 2004 ); however, for brevity and consistency, we will introduce case studies and mitigation-related results from one agent-based model.

3.1 Casualty and Bottleneck Estimation: Cases of Arahama, Japan and Padang, Indonesia

The agent-based model described in this paper incorporates tsunami inundation modeling outputs and pedestrian and vehicle agent simulation ( Mas et al. 2012 ). The model was verified using the 2011 GEJT data from the evacuation in Arahama, Sendai, Japan (Fig. 1 ).

Google Earth images of the Arahama area before (April 4, 2010) and after (March 14, 2011) the tsunami. The yellow circle shows the location of Arahama Elementary School

One thousand iterations of a stochastic simulation were conducted; each simulation provided the number of evacuees at a shelter, the number of evacuees who passed one of the exits (“safe”) and the number of evacuees trapped by the tsunami (the probability of becoming a casualty exceeded 50 %) (Fig. 2 ). This information at such a high level of detail is one of the greatest advantages of agent-based modeling. In addition to emergent behavior, the behavioral details of each agent and local issues (e.g., traffic and crowd congestion due to the number of agents or the presence of slow-speed agents in front of fast agents) can be identified. The exact values from a real situation are difficult to obtain with stochastic simulations; however, the average number of estimated survivors at the tsunami evacuation building (TEB) showed that the model realistically represents some evacuation decisions and outcomes in the area.

Left A snapshot of the model applied to the Arahama evacuation when the tsunami inundates the study area. The black dots are active pedestrian evacuees; the purple dots are the evacuees in vehicles, and the red dots are fatalities caused by the tsunami. Right The last snapshot of the simulation shows bottleneck areas during the evacuation as white dots . These bottlenecks mainly occurred on the bridge and in front of Arahama Elementary School, which is the evacuation building

Previous models that did not employ an agent-based approach have also been able to provide casualty estimations and sometimes identify bottleneck areas; however, because those methods (i.e., GIS and DEM) are static or aggregated, information on possible timing and details of interactions, e.g., vehicle–pedestrian interactions, cannot be determined.

The tsunami evacuation simulation demonstrated the capability of the model to identify bottlenecks and to verify the evacuation process with several behavioral conditions within a dynamic framework. The stochastic simulation and the individual level of representation in the model provide the modeler with a reasonable amount of data to analyze and identify issues at both large and small scales, where agent behavior might not contribute to the safe evacuation of other individuals. For example, agents that are slow to begin evacuating increase their own risk of fatality and reduce the flow of evacuation in the network by creating bottlenecks, especially when their speed is also comparatively low.

In addition to verification of past events, ABM models can be used to assess hypothetical events such as a large tsunami affecting Padang City, Indonesia. Imamura et al. ( 2012 ) described the tsunami hazard in this area based on a mega-thrust earthquake scenario for tsunami simulation using high-resolution bathymetry and topography data. The resulting inundated area was approximately 25 km 2 , threatening at least 235,000 people with tsunami depths from 3 m to approximately 8 m ( Muhari et al. 2011 ). Padang city lacks vertical evacuation facilities within the predicted inundation area and based on reports from tsunami evacuations during the 2007, 2009 and 2010 events ( Hoppe and Marhadiko 2009 ), residents mainly use vehicles and motorcycles for evacuation, despite the experiences of traffic congestion. Consequently, estimating the time needed by evacuees to leave the tsunami inundation area and determining possible congested routes during evacuation are necessary.

A total of 104,352 agents were modeled in a 15 km 2 area in southern Padang using agent-based modeling ( Mas et al. 2012 ). Based on the size of the modeled population and the evacuation behavior that considers individual departure times, the model calculated casualty estimates, the time needed for evacuation, and bottleneck points (Fig. 3 ). In the simulation, the tsunami resulted in the fatalities of approximately 37.7 % of the population. The authors identified several congested streets in the northern study area due to the popular use of the exit points. In addition, the shopping center and traditional market center areas were highly congested due to the high local population density. In the south of the evacuation simulation domain, the agents evacuated to high ground by crossing the river; however, based on the tsunami simulation results, those areas are expected to be inundated due to overtopped river embankments.

Simulation of evacuation areas, with the bottleneck results shown in purple . Details of other results are reported by Imamura et al. ( 2012 )

The application of the tsunami evacuation simulation in these case studies clarifies the importance of evacuation start times, which cannot be explored with aggregated modeling approaches, the need for high evacuation areas, and the sensitivity of streets to congestion in highly populated urban cities, such as Padang. The same area was evaluated as a case study by Lämmel et al. ( 2010 ) a multi-agent traffic simulator (MATSim) to represent pedestrian agents instead of vehicles. While the approach used in the MATSim simulation differs from the model presented here, similar results were obtained. The models exhibited similar results because the agent behavior in path-finding or route-planning for evacuation was primarily set to use the shortest path. Lämmel et al. ( 2010 ) noted that a risk–cost function should be added to the route-planning algorithm in MATSim; similarly, in our model, a risk–cost value for the cells near the tsunami flooding areas might be included. However, before such modifications, it is necessary to investigate the residents’ preferences in an evacuation to adequately represent the evacuees. It is possible that some people prefer short routes with earlier starting times, while others prefer longer routes with lower risk. It is recommended to pursue an early starting time and low-risk routes during tsunami evacuations. Padang city is a highly populated area with narrow streets that contribute to immediate crowding. These characteristics make this scenario suitable for modeling using the agent-based approach. However, areas with wide roads, where crowding might not be expected, may use other approaches, such as the least-cost distance (LCD) in GIS ( Wood and Schmidtlein 2012 ), to obtain evacuation timings. In summary, correctly applying tsunami evacuation models depends on the purpose of the simulation and the assumed agent behavior.

3.2 Vehicles in Evacuation: Case of Pakarang Cape, Thailand

Another concern for evacuation planning is the use of vehicles during evacuation. This option must be evaluated according to the characteristics of the environment and the population involved in the evacuation. Mas et al. ( 2013b ) conducted an evacuation simulation for the Pakarang Cape in Thailand, located in the Khao Lak beach resort area of Phang Nga Province on the Andaman Sea, an area devastated by the 2004 IOT (Fig. 4 ).

Phang Nga devastation from the 2004 Indian Ocean tsunami. Left January 13, 2003. Right December 29, 2004. Source: Space Imaging/CRISP-Singapore. The yellow inset shows the simulated area

A total of 2649 residents were modeled based on a nighttime population scenario. The objective in this case study was to explore the influence of vehicles on the evacuation, combined with different reaction times from the residents. A set of percentages of evacuees in vehicles (passengers and drivers) was assumed for developing several scenarios for the simulation. The evacuation rate as a function of time followed the results of a questionnaire survey ( Suppasri 2010 ). In addition to this distribution, three other scenarios were considered: a late evacuation and two intermediate scenarios between the former two distributions. The results of the 20 simulated cases are shown in Fig. 5 . Due to differences in reaction time and the long evacuation distance, the use or non-use of vehicles was found to contribute to fatality rates, which ranged from 6 to 34 % of the total population at risk.

Fatality ratios for different evacuation timing scenarios and the percentages of the population using vehicles. Note the advantage of a fast evacuation decision and the advantage of using cars in this specific case

The application of the tsunami evacuation simulation in this case study showed the capability of ABM to evaluate the feasibility of evacuations and on-road vehicle–pedestrian interactions. In this case, 20 scenarios with different evacuation starting times and percentages of vehicles used in the evacuation were compared. The results suggest that because of the distance to the shelters, vehicle evacuation might be necessary. It is possible that the use of vehicles in this area might not result in significant traffic congestion due to the small population and sufficient road capacity. Note that with a larger population than evaluated here, traffic congestion is possible, as shown in the Arahama case study. This suggests that conclusions from tsunami evacuation simulations in one area should not be arbitrarily applied to another area, particularly with regard to restrictions on vehicles for evacuation.

3.3 Evacuation Behavior: Case of Natori, Japan

This is the second application of a tsunami evacuation model to the 2011 GEJT (Fig. 6 ). Takagi et al. ( 2014 ) simulated the evacuation behavior reported in Yuriage, Natori, to replicate the evacuation process and investigate the reasons for the large number of fatalities in the area. Yuriage is a small town near the Natori River located on the plains of the Miyagi Prefecture. Before the earthquake on March 11, 2011, approximately 5612 residents were living in the area. After the earthquake, 752 people were killed by the resulting tsunami, and 41 are still missing; this event resulted in one of the highest fatality rates in the plains area of Miyagi.

Left The study area of Yuriage in Natori, Miyagi Prefecture, Japan. From right to left (landwards): the green signs indicate the locations of the Yuriage Community Center, Yuriage Junior High School, pedestrian bridge (not official) and Yuriage Elementary School, respectively. The green arrows are the exit routes for vehicle evacuations. Right A snapshot of the model applied in Yuriage, Natori. The study area is inundated by the tsunami based on the numerical simulation results. The black dots are the active pedestrian evacuees; the purple dots are evacuees in vehicles; the red dots are fatalities due to the tsunami; and the yellow dots show the bottleneck points in the simulation

Reports indicate that the residents in the area evacuated to nearby shelters; however, before the tsunami arrived, the tsunami warning was elevated to reflect a larger estimated tsunami height (JMA 2013 ). Therefore, over the next few minutes, some of the evacuees decided to conduct a secondary evacuation to a more inland shelter. Evacuees in the community center, which is a 2-story building, moved to Yuriage Junior High School, which is a 3-story building located approximately 500 m inland (Fig. 6 ). During this second evacuation, the arrival of the tsunami ( Muhari et al. 2012 ) resulted in pedestrian fatalities. In this case study, the model was applied to two scenarios: (1) Case A, a scenario as close as possible to the real evacuation based on the data reported by local authorities and survivors and (2) Case B, a what-if scenario in which the second evacuation was not performed. The actual reported number of fatalities during the event and the results from the simulation are shown in Table 1 and Fig. 7 . From the survey reports, the community center safely sheltered 43 residents ( Murakami et al. 2012 ).

The simulated timeline of the evacuees in the shelters. Note the arrival at the community center ( blue line ). Approximately 25 min was necessary to fill the shelter to its full capacity; no additional evacuees arrived or left until 45 min after the event. Over the next 20 min, people relocated to the junior high school ( red line ), and the tsunami’s arrival resulted in the deaths of evacuees en route

Figure 7 shows the evacuation sequence at each shelter in Case A; note that the community center was filled to its capacity—300 people (Table 1 )—approximately 25 min after the earthquake (~15:10 JST). At 15:14 JST, the Japan Meteorological Agency (JMA) issued its first tsunami warning upgrade for the Miyagi coast from 6 m to over 10 m (JMA 2013 ), which might explain why some people decided to relocate to a more inland shelter; the community center is only a two-story building ( Kahoku Shimpo 2011 ), while Yuriage Junior High School is a three-floor building. In addition, information on the damage to the areas in the north where tsunami waves had already struck might also have contributed to their decision. Based on the information provided by survivors, evacuees started moving from the community center to Yuriage Junior High School at approximately 15:30 JST, which agrees with the second tsunami warning information issued by JMA ( 2013 ). Therefore, by setting 15:30 JST as the time for the second evacuation in the model, the results showed that a total of 257 evacuees were able to leave the community center before the tsunami arrived. Of the 257 people, only 82 were able to reach the junior high school in time. The reasons for the fatalities and for the evacuees reaching the high school or not are further explored in the simulations. We summarize some of the reasons as follows. (1) The timing of the secondary evacuation: each evacuee conducted a second evacuation between 15:30 JST and approximately 15:50 JST, when the tsunami arrived at the community center. During this 20-min period, the first people to evacuate might have arrived on time, depending on their means of transportation. (2) The means of transportation: based on the survivor accounts and the simulation results, traffic was congested on the road in front of the community center, and people who attempted to evacuate by car might have been delayed because of this situation. However, in Case B, in which agents did not conduct a secondary evacuation and remained at the community center, the total number of fatalities was approximately 44 % less than in Case A, provided that the community center was filled to its capacity.

This case study shows the advantage of agent-based models. Unlike aggregate and static approaches, modeling low-level component behavior and event scheduling is possible in agent-based models. The immense amount of available data related to the GEJT makes it possible to utilize agent-based approaches to examine an evacuation process defined by more than the sum of its parts instead of a global picture with several assumptions. In addition, agent-based models are powerful tools that can be applied to verify and analyze the effects of evacuees’ decisions on the outcomes of the evacuation process. Future evacuation plans and activities for the reconstruction process and urban planning can be supported by the results from tsunami evacuation agent-based models. For example, in the Natori area, a new urban layout has been proposed in which new structural countermeasures and resident relocations would be considered. With a more efficient population distribution, improved road networks, shelter availability, and shorter distances to high ground, a lower tsunami risk is expected. However, a tsunami warning can still trigger a massive evacuation in this area, and the characteristics of such a potential evacuation need to be investigated to avoid accidents or fatalities. Plans for a layout suitable for tsunami protection and a massive evacuation can be explored using agent-based tsunami evacuation simulations.

3.4 Shelter Demand: Case of La Punta, Peru

La Punta is a peninsula in the western part of Callao Province. The area is entirely surrounded by the Pacific Ocean, except on its northeastern side where it is bordered by downtown Callao. This district is one of the smallest in Peru, with 4370 inhabitants and a total land area of 0.75 km 2 . Historically, earthquakes and tsunamis have struck the area of La Punta in 1586, 1687 and 1746. More than 250 years of seismic inactivity in this region suggests the seismic gap is large enough to trigger earthquakes with an 8.9 magnitude ( Pulido et al. 2013 ) (Fig. 8 ).

The location of La Punta in Peru and the tectonic settings of the earthquakes in the surrounding regions (modified from Yamazaki and Zavala 2013 ; Mas et al. 2013a )

Tsunami mitigation and preparedness activities have been conducted in La Punta; however, difficulties in conducting frequent evacuation drills with wide population participation suggested it was prudent to apply tsunami evacuation simulations to evaluate the actual conditions of the shelters and the evacuation timing in the area. According to local authorities, 20 official TEBs exist, with enough space to accommodate the entire population (Fig. 9 ). The authors used the agent-based tsunami evacuation model ( Mas et al. 2013a ) to investigate a tsunami inundation and the resident evacuation behavior.

Tsunami evacuation buildings in La Punta. The 20 buildings can accommodate a total of 7930 evacuees in the district of 4370 residents. The bottom-left inset shows the locations of these buildings

A detailed description of the assumptions and constraints for each simulated case can be found in Mas et al. ( 2013a ). In addition to the casualty estimate, more detailed and interesting information is found for the TEBs regarding their capacities and the numbers of evacuees that arrived. At 13 of the 20 evacuation buildings, demand exceeded the available capacity; at the other seven buildings, capacity exceeded the demand, and available space remained. As a consequence, for a total of 4370 residents and buildings with a total capacity for 7930 people, the spatial characteristics of each shelter location produced an imbalance in the preference and number of evacuees during the simulated event. This situation may raise new issues during evacuations, such as conducting a secondary evacuation—as in the case of Natori in Japan discussed previously—that delays evacuation and may cause a higher number of fatalities. To inform the local authorities and stakeholders of these results, the capacity–demand rate of the shelters was mapped. The capacity demand index was constructed to represent and easily communicate the spatial issues of the availability of shelters in La Punta (Fig. 10 ).

The top-left inset shows the evacuee demand for shelter, while the bottom-left inset shows the capacity of shelters. The black square marks the zoomed-in area with the high/low demand and low/high capacity example of over/under demand evaluations. The bottom-right picture is the CDI mapping result for outreach purposes

The tsunami evacuation ABM was used to reveal the necessity of vertically directed evacuations, particularly in low-lying areas such as La Punta. The outcomes of this study contributed to the identification of risks that could not be identified using static approaches (e.g., GIS least-cost distance analysis, shelter location–allocation solutions, or a direct comparison of the available space versus the number of residents without considering the spatiotemporal issues). The dynamics of a tsunami evacuation simulation are valuable characteristics that should be explored and applied in tsunami mitigation and evacuation planning.

4 Challenges and Future Perspectives

Each time a tsunami occurs, lessons are gathered and shared; unfortunately, not all lessons are fully learned. Similarly, tsunami evacuation research has substantially improved since the 2004 IOT. The future of tsunami evacuation research, as seen by the authors, concerns the comprehensive geophysics of tsunamis and the physical and psychological traits of the evacuees, all of which would be built into an integrated modeling technique. Efforts to integrate tsunami simulations and social simulations using agent-based modeling ( Mas et al. 2012 , 2013a ) and to use supercomputers ( Wijerathne et al. 2013 ) to bridge gaps between geoscience and social science connect risk assessments with risk management. With these tools, evacuation issues can be understood in a more comprehensive manner. Challenges and emerging issues worth our attention are as follows:

Human behavior: Modeling human behavior is not an easy task, but it is not impossible because humans do not behave randomly ( Kennedy 2012 ). Psychological parameters ( Vorst 2010 ), such as evacuees’ cognitive and emotional behaviors that affect decision-making or levels of stress and panic should be included. However, capturing individual characteristics in mathematical equations is difficult ( Pan et al. 2007 ). Human behavior in tsunami evacuations has been studied using questionnaires. Survey responses provide insight into people’s risk perceptions ( Charknol and Tanaboriboon 2006 ; Bird 2009 ; Gierlach et al. 2010 ) and their experiences in real evacuations in post-tsunami events ( Lachman et al. 1961 ; Katada et al. 2005 ; Saito 1990 ; Mas et al. 2011 ). The statistical outcomes of preference and revealed surveys are incorporated into the agent-based model presented here to stochastically assess uncertainties in human behavior. In the future, we should explore artificial intelligence and cognitive science models in conjunction with agent-based approaches for tsunami evacuation modeling. Agent-based modeling practitioners use these fields quite often to model human behavior ( Wray and Laird 2003 ; Shendarkar and Vasudevan 2006 ; Dunin - Keplicz and Verbrugge 2010 ).

Verification and validation (V&V) of models: Because of the nature of ABMs, which are based on simulations rather than equations that can be tested in the laboratory or analytically solved, the process of V&V is difficult ( Ormerod and Rosewell 2009 ). However, replication may aid the model verification process. Specifically, if two distinct implementations of a conceptual model are able to produce the same results, then that outcome supports the hypothesis that the original model correctly implemented the conceptual model ( Rand and Wilensky 2006 ). Another way to confirm the “inner validity” of the model is by “alignment”, which refers to using a different programming language and ABM toolkit to re-implement the model ( Castle and Crooks 2006 ). For validation, real-world data must be collected for comparison with model output; this can be done through controlled evacuation drills. Finally, aggregated data on tsunami evacuations or evacuation drills, such as the number of fatalities in an area or the number of evacuees in shelters, are available, but the validity of the dynamics of the evacuees from the starting point to their shelter and the accuracies of the behavioral models used for agents are uncertain.

Use of earthquake disaster “big data”: To address the challenges in the two items above, human behavioral models and real-world data can be collected from mobile technologies, which record user locations, speeds, directions, etc. This information was tracked during the 2011 GEJT from mobile phones, car navigation systems and social media. This massive amount of data is known as “Shinsai big data” or earthquake disaster big data. These data can elucidate what occurred on the ground on the day of the tsunami, characteristics of evacuee behaviors, and issues in the process of mobilization; in other words, the collective mind of the society at risk can be explored to calibrate behavioral models and to validate evacuation models. Additionally, virtual big data can also be created to gather additional information on human behavior. Here, we introduce the concept of “virtual big data”, which is the information gathered via cloud gaming ( Liu et al. 2014 ) to create a library of human behavior in evacuation scenarios. In the future, real-time big data may be used to support the evacuation of residents during an event.

Other expectations in the field of agent-based modeling that apply to tsunami evacuation models of this nature are discussed by Helbing and Balietti ( 2011 ), who envision a new way of performing research using supercomputers in data-rich situations. These authors foresee a massive simulation platform with various types of data (i.e., demographic, socio-economic, and geographic) and simulation approaches (i.e., agent-based and equation-based) in large-scale environments within the next 10–15 years ( Helbing and Balietti 2010 ).

5 Conclusions

The 2004 IOT and the 2011 GEJT were the two most destructive tsunamis in recent years. Both events emphasize the necessity for effective evacuation plans and rapid evacuation behavior. In addition, the events provided large amounts of data that have been or will be used to produce, verify, validate and improve models to represent the evacuation of populations. Tsunami evacuation models have been developed using several techniques; here, we discussed the agent-based modeling approach because we considered it to be suitable for exploring human behavior and rapid low-level environmental changes using available high-resolution data. An agent-based model was applied to assess tsunami risk and evacuation scenarios in Indonesia, Thailand, Peru and Japan. As described in the case studies, agent-based models benefit tsunami mitigation and evacuation planning by describing the individuality of the evacuees and allowing for the observation of emergent behavior within the dynamics of agent interactions. Agent-based models are flexible and provide a natural description of a particular system. From the perspective of tsunami hazard mitigation, the model presented here provides estimates of casualties, an analysis of evacuee behavior in a two-step evacuation process, identifies bottlenecks, uncovers limitations in shelter capacity and evaluates the use of vehicles in evacuations. All of these outcomes are associated with evacuation planning and cannot be observed solely through evacuation drills or questionnaire surveys. Finally, several challenges to agent-based modeling exist. Past evacuation simulations for tsunamis were unable to model large-scale scenarios and various human traits; present research is considering much finer levels of detail in simulations with a huge amount of data using high-performance computational techniques. Future research should focus on comprehensive and integrated simulations by incorporating complex agent behavior. Engineering, social, psychological and educational sciences should work together to effectively understand, build, apply and share evacuation simulations and outcomes.

Abustan , M. S., Harada , E., and Gotoh , H. (2012). Numerical simulation for evacuation process against tsunami disaster at Teluk Batik in Malaysia by multi-agent DEM model . Proceedings of Coastal Engineering, JSCE, 3 , 56–60.

Bernard , E., Mofjeld , H. O., Titov , V. V., Synolakis , C. E., and González , F. I. (2006). Tsunami: scientific frontiers, mitigation, forecasting and policy implications . Philosophical Transactions of The Royal Society. Series A, Mathematical, Physical, and Engineering Sciences, 364 (1845), 1989–2007. doi: 10.1098/rsta.2006.1809 .

Bird , D.K. (2009). The use of questionnaires for acquiring information on public perception of natural hazards and risk mitigation a review of current knowledge and practice , Natural Hazards and Earth System Sciences, Vol. 9 , pp. 1307–1325.

Castle , C. J. E., and Crooks , A. T. (2006). Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations (No. 110) (Vol. 44 , pp. 1–62).

Charknol , T., and Tanaboriboon , Y. (2006). Tsunami Evacuation Behavior Analysis . IATSS Research, 30 (2), 83–96.

Clerveaux , V. I., Katada , T., and Hosoi , K. (2008). Tsunami Scenario Simulator: A Tool for ensuring effective disaster management and coastal evacuation in a multilanguage society . Science of Tsunami Hazards, 27 (3), 48–71.

Dewi , R. S. (2012). A Gis Based Approach of an Evacuation Model for Tsunami Risk Reduction . Journal of Integrated Disaster Risk Management, 2 (2), 1–32. doi: 10.5595/idrim.2012.0023 .

Dunin - Keplicz , B., and Verbrugge , R. (2010). Teamwork in Multi-Agent Systems. A Formal Approach (p. 246). Wiley.

Fraser , S., Leonard , G. S., Matsuo , I., and Murakami , H. (2012). Tsunami evacuation: Lessons from the Great East Japan earthquake and tsunami of March 11th 2011, GNS Science Report (pp. 1–12).

Freire , S., Aubrecht , C., and Wegscheider , S. (2012). When the Tsunami Comes to Town – Improving Evacuation Modeling by Integrating High-resolution Population Exposure. In Proceedings of the 9th International ISCRAM Conference (pp. 1–5). Vancouver, Canada.

Fujioka , M., Ishibashi , K., Kaji , H., and Tsukagoshi , I. (2002). Multi agent Simulation Model for Evaluating Evacuation Management System Against Tsunami Disaster . Journal of Architecture, Planning and Environmental Engineering, 562 , 231–236. Retrieved from http://ci.nii.ac.jp/naid/110004660530/ .

Gierlach , E., Belsher , B. E., and Beutler , L. E. (2010). Cross-cultural differences in risk perceptions of disasters. Risk analysis: an official publication of the Society for Risk Analysis, Vol. 30 , pp. 1539–1549.

Gonzalez - Riancho , P., Aguirre - Ayerbe , I., Aniel - Quiroga , I., Abad , S., Gonzalez , M., Larreynaga , J., Gavidia , F., Gutierrez , O.Q., Alvarez - Gomez , J.A., and Medina , R. (2013). Tsunami evacuation modelling as a tool for risk reduction: application to the coastal area of El Salvador . Natural Hazards and Earth System Sciences, 13 , 3249–3270. doi: 10.5194/nhess-13-3249-2013 .

Goto , Y., Affan , M., Agussabti , Nurdin , Y., Yuliana , D. K., and Ardiansyah . (2012). Tsunami Evacuation Simulation for Disaster Education and City Planning . Journal of Disaster Research, 7 (1), 92–101. Retrieved from http://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=DSSTR000700010010.xml .

Helbing , D., Buzna , L., Johansson , A., and Werner , T. (2005). Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions . Transportation Science, 39 (1), 1–24. doi: 10.1287/trsc.1040.0108 .

Helbing , D. and Balietti , S. (2010) From social simulation to integrative system design. Visioneer white paper. http://www.visioneer.ethz.ch .

Helbing , D., and Balietti , S. (2011). How to Do Agent-Based Simulations in the Future: From Modeling Social Mechanisms to Emergent Phenomena and Interactive Systems Design (No. 024) (pp. 1–55).

Hoppe , M., and Marhadiko , H. S. (2009). 30 Minutes in the City of Padang: Lessons for Tsunami Preparedness and Early Warning from the Earthquake on September 30, 2009. Working Document No. 25. GTZ-GITEWS.

Imamura , F., Suzuki , T., and Taniguchi , M. (2001). Development of a Simulation Method for the Evacuation from the Tsunami and Its Application to Aonae, Okushiri Is., Hokkaido . Journal of Japan Society for Natural Disaster Science, 20 (2), 183–195. Retrieved from http://sciencelinks.jp/j-east/article/200201/000020020101A0917476.php .

Imamura , F., Muhari , A., Mas , E., Pradono , M. H., Sugimoto , M., and Post , J. (2012). Tsunami Disaster Mitigation by Integrating Comprehensive Countermeasures in Padang City, Indonesia . Journal of Disaster Research, 7 (1). Retrieved from http://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=DSSTR000700010006.xml .

Japan Meteorological Agency (JMA). (2013). Lessons learned from the tsunami disaster caused by the 2011 Great East Japan Earthquake and improvements in JMA ’ s tsunami warning system October 2013 Japan Meteorological Agency (pp. 1–13). Retrieved from: http://www.data.jma.go.jp/svd/eqev/data/en/tsunami/LessonsLearned_Improvements_brochure.pdf .

Johnston , K. M. (2013). Agent Analyst. Agent-Based Modeling in ArcGIS. (Esri Press, Ed.) (pp. 1–559). California, USA.

Kahoku Shimpo (2011). August 3, 2011 published testimony. Retrieved from: http://ameblo.jp/yume–zuki/entry-10987309620.html .

Katada , T., Asada , J., Kuwasawa , N., and Oikawa , Y. (2000). Development of practical scenario simulator for dissemination of disaster information . Journal of Civil Engineering Information Processing System, 9 , 129–136.

Katada , T., Kuwasawa , N., Kanai , M., and Hosoi , K. (2004). Disaster Education for Owase citizen by using Tsunami Scenario Simulator and evaluation of that method . Sociotechnica, 2 , 199–208. Retrieved from http://www.jstage.jst.go.jp/article/sociotechnica/2/0/199/_pdf .

Katada , T., Kodama , M., Kuwasawa , N., and Koshimura , S. (2005). Issues of resident’s consciousness and evacuation from the tsunami - From questionnaire survey in Kesennuma city, Miyagi Pref . After the Earthquake of Miyagiken-oki, 2003 - Proceedings of the Japan Society of Civil Engineers, 789 (2), 93–104. Retrieved from http://www.jstage.jst.go.jp/article/jscej/2005/789/789_93/_pdf .

Katada , T., and Kuwasawa , N. (2006). Development of tsunami comprehensive scenario simulator for risk management and disaster education . Transactions of the Japan Society of Civil Engineers (D), 62 (3), 250–261.

Kato , S., Shimozono , T., and Okayasu , A. (2009). Hybrid Simulation for Tsunami Evacuation in Consideration of Individual Behaviors . Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering), 65 (1), 1316–1320. doi: 10.2208/kaigan.65.1316 .

Kennedy , W.G. (2012) Modelling Human Behavior in Agent-Based Models. In Heppenstall , A. J., Crooks , A. T., See , L. M., and Batty , M. (2012). Agent-Based Models of Geographical Systems (pp. 1–746). Springer.

Kietpawpan , M. (2008). Simulation Approach to Evaluating the Effectiveness of a Tsunami Evacuation Plan for Patong Municipality, Phuket, Thailand. Prince of Songkla University.

Lachman , R., Tatsuoka , M., and Bonk , W. J. (1961). Human behavior during the tsunami of May 1960 . Science, 133 , 1405–1409. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/13758063 .

Lämmel , G., Rieser , M., Nagel , K., Taubenböck , H., Strunz , G., Goseberg , N., Schlurmann , T., Klüpfel , H., Setiadi , and N. Birkmann , J. (2010). Emergency Preparedness in the case of a Tsunami - Evacuation Analysis and Traffic Optimization for the Indonesian city of Padang. In W. W. F. Klingsch , C. Rogsch , A. Schadschneider , and M. Schreckenberg (Eds.), Pedestrian and Evacuation Dynamics 2008 (pp. 171–182). Berlin Heidelberg: Springer. Retrieved from http://www.springerlink.com/index/R0887H6872071Q07.pdf .

Liu , R., Du , J., and Issa , R.A.R. (2014). Cloud-based deep immersive game for human egress data collection: a framework , Journal of Information Technology in Construction (ITcon), Special Issue BIM Cloud-Based Technology in the AEC Sector: Present Status and Future Trends, Vol. 19 , pg. 336-349, http://www.itcon.org/2014/20 .

Mas , E., Imamura , F. and Koshimura , S. (2011). Tsunami Risk Perception Framework for the Start Time Evacuation Modeling, in XXV IUGG General Assembly International Association of Seismology and Physics of Earths Interior, (Melbourne, Australia).

Mas , E., Suppasri , A., Imamura , F., and Koshimura , S. (2012). Agent-based Simulation of the 2011 Great East Japan Earthquake / Tsunami Evacuation: An Integrated Model of Tsunami Inundation and Evacuation . Journal of Natural Disaster Science, 34 (1), 41–57. Retrieved from http://www.jsnds.org/contents/jnds/34_1_3.pdf .

Mas , E., Adriano , B., and Koshimura , S. (2013a). An Integrated Simulation of Tsunami Hazard and Human Evacuation in La Punta, Peru . Journal of Disaster Research, 8 (2), 285–295. Retrieved from http://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=DSSTR000800020008.xml .

Mas , E., Suppasri , A., Srivihok , P., and Koshimura , S. (2013b). Feasibility of Evacuation at the Pakarang Cape in Thailand based on Tsunami Inundation Model and Human Evacuation Simulation. In 10th International Conference on Urban Earthquake Engineering (pp. 1–6). Tokyo, Japan.

Meguro , K., and Oda , K. (2005). Development of Evacuation Simulator for Tsunami Disaster Mitigation . Production Research, 57 (4), 343–347.

Munadi , K., Nurdin , Y., Dirhamsyah , M., and Muchalil , S. (2012). Multiagent based Tsunami Evacuation Simulation: A Conceptual Model. In The Proceedings of 2nd Annual International Conference Syiah University 2012 & 8th IMT Uninet Biosciences Conference (pp. 254–259). Banda Aceh, Indonesia.

Muhari , A., Imamura , F., Koshimura , S., and Post , J. (2011). Examination of three practical run-up models for assessing tsunami impact on highly populated areas . Natural Hazards and Earth System Science, 11 (12), 3107–3123. doi: 10.5194/nhess-11-3107-2011 .

Muhari , A., Imamura , F., Suppasri , A., Mas , E., and Koshimura , S. (2012). Tsunami arrival time characteristics of 2011 East Japan tsunami revealed from eyewitness, evidences and numerical simulation . Journal of Natural Disaster Science, 34 (1), 91–104.

Murakami , H., Takimoto , K., and Pomonis , A. (2012). Tsunami Evacuation Process and Human Loss Distribution in the 2011 Great East Japan Earthquake - A Case Study of Natori City, Miyagi Prefecture -. In 15th World Conference on Earthquake Engineering (pp. 1–10). Retrieved from http://www.iitk.ac.in/nicee/wcee/article/WCEE2012_1587.pdf .

Nguyen , T. N. A., Zucker , J. D., Nguyen , H. Du , Alexis , D., and Vo Duc , A. (2012a). A Hybrid Macro-Micro Pedestrians Evacuation Model to Speed Up Simulation in Road Networks. In AAMAS 2011 Workshop (pp. 371–383).

Nguyen , T. N. A., Zucker , J. D., Nguyen , M. H., Alexis , D., and Nguyen , H. P. (2012b). Simulation of emergency evacuation of pedestrians along the road networks in Nhatrang city . IEEE, 1–6.

Nozawa , S., Watanabe , K., and Kondo , A. (2006). Development of Evacuation Simulation Model for Tsunami Disaster . Geographical Information Systems Association, 15 , 483–486.

Ormerod , P., and Rosewell , B. (2009). Validation and Verification of Agent-Based Models in the Social Sciences. In F. Squazzoni (Ed.), Epistemological Aspects of Computer Simulation in the Social Sciences (pp. 130–140). Berlin: Springer.

Pan , X., Han , C. S., Dauber , K., and Law , K. H. (2007). A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations . Ai & Society, 22 (2), 113–132. doi: 10.1007/s00146-007-0126-1 .

Park , S., van de Lindt , J. W., Gupta , R., and Cox , D. (2012). Method to determine the locations of tsunami vertical evacuation shelters . Natural Hazards, 891–908. doi: 10.1007/s11069-012-0196-3 .

Pulido , N., Tavera , H., Aguilar , Z., Nakai , S., and Yamazaki , F. (2013). Strong Motion Simulation of the M8.0 August 15, 200, Pisco Earthquake; Effect of a Multi-Frequency Rupture Process . Journal of Disaster Research, 8 (2), 235–242.

Rand , W., and Wilensky , U. (2006). Verification and Validation through Replication: A Case Study Using Axelrod and Hammond ’ s Ethnocentrism Model. In North American Association for Computational Social and Organization Sciences (NAACSOS) (pp. 1–6). Retrieved from http://ccl.northwestern.edu/papers/naacsos2006.pdf .

Saito , T. (1990). Questionnaire Survey of Human Behaviors and Consciousness on the Tsunami of the 1989 Sanriku-Oki Earthquake , Journal of Japan Society for Natural Disaster Science, Vol. 9 , No.2, pp. 49–63.

Saito , T., and Kagami , H. (2004). Simulation of Evacuation Behavior from Tsunami utilizing Multi Agent System. In 13th World Conference on Earthquake Engineering (pp. 1–10). Vancouver, B.B., Canada. Retrieved from http://www.iitk.ac.in/nicee/wcee/article/13_612.pdf .

Shendarkar , A., and Vasudevan , K. (2006). Crowd Simulation for Emergency Response using BDI Agent Based on Virtual Reality. In L. F. Perrone , F. P. Wieland , J. Liu , B. G. Lawson , D. M. Nicol , and R. M. Fujimoto (Eds.), Proceedings of the 2006 Winter Simulation Conference (pp. 545–553).

Shuto , N. (2009). Tsunami Research - Its Past, Present and near Future -. In Proceedings of the Sixth International Workshop on Coastal Disaster Prevention (pp. 1–24). Bangkok, Thailand.

Simonovic , S. P., and Ahmad , S. (2005). Computer-based Model for Flood Evacuation Emergency Planning . Natural Hazards, 34 (1), 25–51. doi: 10.1007/s11069-004-0785-x .

Sugimoto , T., Murakami , H., Kozuki , Y., and Nishikawa , K. (2003). A Human Damage Prediction Method for Tsunami Disasters Incorporating Evacuation Activities . Natural Hazards, 2035 (29), 585–600.

Suppasri , A. (2010). Tsunami Risk Assessment to Coastal Population and building in Thailand. PhD thesis. Tohoku University.

Suppasri , A., Shuto , N., Imamura , F., Koshimura , S., Mas , E., and Yalciner , A. C. (2012a). Lessons Learned from the 2011 Great East Japan Tsunami: Performance of Tsunami Countermeasures, Coastal Buildings, and Tsunami Evacuation in Japan . Pure and Applied Geophysics, 170 (6–8), 993–1018. doi: 10.1007/s00024-012-0511-7 .

Suppasri , A., Koshimura , S., Imai , K., Mas , E., Gokon , H., Muhari , A., and Imamura , F. (2012b). Damage characteristics and field survey of the 2011 Great East Japan Tsunami in Miyagi Prefecture . Coastal Engineering Journal, 54 (1), 1250005–1. doi: 10.1142/S0578563412500052 .

Suppasri , A., Muhari , A., Ranasinghe , P., Mas , E., Imamura , F., and Koshimura , S. (2014). Damage and Reconstruction After the 2004 Indian Ocean Tsunami and the 2011 Tohoku Tsunami in Tsunami Events and Lessons Learned edited by Kontar , Y., Santiago - Fandino , V., Takahashi , T. Ed. Springer Netherlands, 2014.

Suzuki , T., and Imamura , F. (2005). Simulation model of the evacuation from a tsunami in consideration of the resident consciousness and behavior . Journal of Japan Society for Natural Disaster Science, 23 (4), 521–538. Retrieved from http://sciencelinks.jp/j-east/article/200509/000020050905A0347269.php .

Synolakis , C. E., and Bernard , E. N. (2006). Tsunami science before and beyond Boxing Day 2004. Philosophical Transactions . Series A, Mathematical, Physical, and Engineering Sciences, 364 (1845), 2231–65. doi: 10.1098/rsta.2006.1824 .

Takagi , H., Mas , E., and Koshimura , S. (2014). Analysis of the evacuation behavior in Natori, Yuriage during the Great East Japan Earthquake Tsunami. In Annual Meeting of the Tohoku Branch Technology Research Conference, Japan Society of Civil Engineers (Vol. 3 , pp. 2011–2012).

Usuzawa , H., Imamura , F., and Shuto , N. (1997). Development of the method for evacuation numerical simulation for tsunami events. In Annual Meeting of the Tohoku Branch Technology Research Conference, Japan Society of Civil Engineers (pp. 430–431). Retrieved from http://library.jsce.or.jp/jsce/open/00322/1997/1997-0430.pdf (In Japanese).

Vorst , H. C. M. (2010). Evacuation models and disaster psychology . Procedia Engineering, 3 , 15–21. doi: 10.1016/j.proeng.2010.07.004 .

Watanabe , K., and Kondo , A. (2009). Development of Tsunami Evacuation Simulation Model to Support Community Planning for Tsunami Disaster Mitigation . Journal of Architecture Planning, 74 (637), 627–634.

Watts , J. M. (1987). Computer Models for Evacuation Analysis . Fire Safety Journal, 12 , 237–245.

Wijerathne , M. L. L., Melgar , L. A., Hori , M., Ichimura , T., and Tanaka , S. (2013). HPC Enhanced Large Urban Area Evacuation Simulations with Vision based Autonomously Navigating Multi Agents . Procedia Computer Science, 18 , 1515–1524. doi: 10.1016/j.procs.2013.05.319 .

Wood , N. J., and Schmidtlein , M. C. (2012). Community variations in population exposure to near-field tsunami hazards as a function of pedestrian travel time to safety . Natural Hazards. doi: 10.1007/s11069-012-0434-8 .

Wray , R. E., and Laird , J. E. (2003). Variability in Human Behavior Modeling for Military Simulations. In Behavior Representation in Modeling & Simulation Conference (pp. 1–10).

Yamazaki , F., and Zavala , C. (2013). SATREPS Project on Enhancement of Earthquake and Tsunami Disaster Mitigation Technology in Peru . Journal of Disaster Research, 8 (2), 224–234.

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Acknowledgments

This research was financially supported by Grant-in-Aid for Scientific Research (Project numbers: 25242035), SATREPS Peru project, CREST project of JST, and IRIDeS grant. In addition, we offer a special thanks to the two anonymous reviewers and the editor in charge of this paper from whom we have received valuable comments that improved the quality of this publication.

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Mas, E., Koshimura, S., Imamura, F. 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). https://doi.org/10.1007/s00024-015-1105-y

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Article Contents

The impact of ozone on Earth-like exoplanet climate dynamics: the case of Proxima Centauri b

2.1 Coupled climate-chemistry model

2.2 Dynamical systems metrics

2.3 Statistical inference

3.1 How does interactive ozone influence the climatology of atmospheric variables?