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2018 Sulawesi, Indonesia Earthquake and Tsunami Case Study

What caused the Sulawesi, Indonesian earthquake and what were the effects?

On Friday 28th September 2018 a magnitude 7.5 earthquake struck Palu, on the Indonesian island of Sulawesi, just before dusk wreaking havoc and destruction across the city and triggering a deadly tsunami on its coast. The 7.5 magnitude earthquake hit only six miles from the country’s coast.

A map to show the location of Palu

A map to show the location of Palu

The shallow tremor was more powerful than a series of earthquakes that killed hundreds on the Indonesian island of Lombok this July and August.

Palu is located on the Indonesian island of Sulawesi, 1,650 kilometres northeast of Jakarta, at the mouth of the Palu River. It is the capital of the province of Central Sulawesi, situated on a long, narrow bay .

A satellite image to show the location of Palu

A satellite image to show the location of Palu – Source Google Earth

The coastal city of Palu is home to 350,000 people.

Small foreshocks had been happening throughout 28th September in Palu. However, in the early evening, the Palu-Koru fault suddenly slipped, a short distance offshore and only 10km (6 miles) below the surface. This generated the 7.5 magnitude earthquake.

The impact of the earthquake was magnified because of the thick layers of sediment on which the city lies. Whereas bedrock shakes in an earthquake, sediment moves a lot more, behaving like a liquid. Poorly constructed houses cannot withstand movement of this magnitude.

Scientists don’t pay much attention to the Palu-Koru fault line, as far as tsunamis are concerned.  This is because the two plates are moving past each other, not with the vertical thrust required to form a tsunami.

Scientists are still trying to work out what happened to cause the tsunami. It is possible that the earthquake caused an underwater landslide which disturbed the water or there could be inaccuracies in the identification of the type of fault.

Once the wave started moving, Palu, at the end of a narrow 10km-long bay, was a sitting duck.

Tsunamis are no danger when out at sea. But when the waves come closer to land, their base drags on the seabed causing them to rise up.

Primary Effects

The quake destroyed thousands of homes in the city, as well as an eight-storey hotel, hospital and a large department store.

More before/after comparisons from around the #PaluTsunami and #PaluEarthquake captured by @planetlabs . Included rough lat/long. Keep an eye on https://t.co/Kz73HlYmGF as they often post the sat. imagery for responders, relief agencies et al. pic.twitter.com/1Vreovjt9b — Murray Ford (@mfordNZ) October 1, 2018

At least 2256 people have been confirmed dead, with more than 10,679 injured and 1075 missing.  200,000 people were in urgent need of assistance, about a quarter of them children.

The earthquake caused widespread liquefaction , which is when soil and groundwater mix. The ground becomes very soft, similar to quicksand. It causes foundations of buildings and other structures to sink into the ground.

In the case of Palu, buildings not only collapsed but some were moved by the liquefaction. This is why it is better to build on bedrock rather than on top of the soil.

The control tower and runway at Palu’s airport also sustained damage. Commercial flights were cancelled with only humanitarian and search and rescue flights permitted.

Secondary Effects

The earthquake triggered a tsunami reaching 6 metres in height. As the tsunami approached the coast it was reported to be travelling 250mph. The damage was as extensive: the main highway was cut off by a landslide and a large bridge washed away by the tsunami wave, which hit Palu’s Talise beach and the coastal town of Donggala.

Landslides, downed communications networks and collapsed bridges have made it hard for aid workers and rescuers to reach rural areas.

Due to hospitals being damaged, people received medical treatment in the open.

Strong aftershocks hit the island the day after the earthquake.

Immediate (Short Term) Response

A tsunami warning was issued by Indonesia’s geophysics agency (BMKG) when the earthquake was detected. However, the agency lifted the warning 34 minutes after it was first issued. The closest tidal sensor to Palu is around 200km (125 miles) away. The decision to lift the tsunami warning was based on this data.

Search and rescue teams were deployed to the worst-affected areas. Around 700 army and police officers were dispatched to assist in the emergency response.

The military sent cargo planes with aid from Jakarta and other cities. However, this was slow to arrive.

A large number of charities set up appeals to raise funds to support people in the affected area. Buckingham Palace reported that the Queen had made a donation to the Disasters Emergency Committee (DEC) appeal for survivors, which raised £6m in a day when it was launched.

The RAF delivered thousands of shelter kits, solar lanterns and water purifiers to the disaster zone in addition to trucks and power generators to help get them to where they are needed.

At least 70,000 people gathered in evacuation sites across the island.

Long-term Response

Further reading.

Indonesia tsunami: UK charities launch a joint appeal – BBC News

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Tsunami disasters: case studies and reports.

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This Collection is part of the 'Tsunami Disaster Channel' containing a number of case studies and reports relevant to tsunami disasters, where we try to find out what we have learnt from the past and how we can best reduce risk in future natural disasters. Current guidance comes from leading global organizations: Foreign - Commonwealth & Development Office (FCDO) ,    Swiss Resource Centre and Consultancies for Development Foundation (SKAT) ,  Office of the UN Secretary General Special Envoy for Tsunami Recovery ,  United Nations Children's Fund (UNICEF) ,  United Nations Environment Programme (UNEP) ,  United Nations International Strategy for Disaster Reduction (UNISDR) .   Please send suggestions for additional content for this Collection to  [email protected] . You might find other helpful collections on tsunami disasters below."

Resources on this Collection

case studies of tsunami

10 Lessons Learned from the South Asia Tsunami of 26th December 2004

case studies of tsunami

Approaches to Equity in Post-Tsunami Assistance - Sri Lanka: A Case Study

case studies of tsunami

Environment and Reconstruction in Aceh: two years after the tsunami

case studies of tsunami

Evolving Strategies For Long-term Rehabilitation On Shelter and Development in the Tsunami Affected Areas of Tamil Nadu

case studies of tsunami

Impact of the tsunami response on local and national capacities: Maldives country report

case studies of tsunami

Indian Ocean Earthquake and Tsunami UNICEF response at six months update

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Tsunami and Earthquake Research Active

  • Publications

Here you will find general information on the science behind tsunami generation, computer animations of tsunamis, and summaries of past field studies.

Field Studies

A home, severely damaged by the tsunami that hit Sumatra on December 26, 2004, sits atop debris.

Our researchers collect data from sites of recent tsunamis to gain a better understanding of the potential impact on other regions with high probability of tsunamis. Their work helps inform coastal planning, protection, and resiliency.

Learn about the earthquakes that triggered recent tsunami events, and watch computer simulations of each tsunami from different angles.

Background information and links to our other tsunami research projects.

Could It Happen Here?

Life of a Tsunami

Local Tsunamis in the Pacific Northwest

Cascadia subduction zone marine geohazards.

  • Probabilistic Forecasting of Earthquakes and Tsunamis

Tsunami Hazards, Modeling, and the Sedimentary Record

  • Unusual Sources of Tsunamis - Presentation by Eric Geist

The scope of tsunami research within the USGS, however, is broader than the topics covered here. USGS researchers have also provided critical research toward understanding how sediments are transported during tsunami runup and deciphering the geologic record of prehistoric tsunamis. The USGS collaborates closely with the NOAA Center for Tsunami Research .

As part of the National Tsunami Hazard Mitigation Program , the USGS has also upgraded the seismograph network and communication functions of the U.S. Tsunami Warning Center .

Soon after the devastating tsunami in the Indian Ocean on December 26, 2004 many people have asked, “Could such a tsunami happen in the United States?” As a starting point, read “ Could It Happen Here? ”

Starting points:

Unusual Sources of Tsunamis

  • Not all tsunamis are generated by earthquakes
  • Tsunamis can be caused by volcanoes, landslides, and even atmospheric disturbances
  • Data from tide gauges can help unravel the complex physics of these sources

Tsunami events:

September 8, 2017, Mexico

March 11, 2011, Japan

  • Preliminary simulations of the tsunami
  • Notes from the field : International Tsunami Team visits Japan before (2010) and after (May 2011); plus eyewitness accounts from California on March 11

October 25, 2010, Indonesia

February 27, 2010 Chile

September 29, 2009, Samoa

  • Preliminary analysis of the tsunami
  • USGS scientists in Samoa and American Samoa studying impacts of tsunami

April 1, 2007, Solomon Islands

March 28, 2005, Sumatra

  • Analysis and comparison of the December 2004 and March 2005 tsunamis
  • Field study of the effects of the December 2004 and March 2005 earthquakes and tsunamis  - April 2005

December 26, 2004, Sumatra-Andaman Islands

  • Tsunami generation from the 2004 M=9.1 Sumatra-Andaman earthquake
  • Initial findings on tsunami sand deposits, damage, and inundation in Sumatra  - January 2005
  • Initial findings on tsunami sand deposits, damage, and inundation in Sri Lanka  - January 2005

June 23, 2001, Peru

  • Preliminary analysis of the tsunami generated by the earthquake
  • Preliminary analysis of sedimentary deposits from the tsunami

July 17, 1998, Papua New Guinea

  • Descriptive model of the tsunami

April 18, 1906, San Francisco

Below are current tsunami studies and tsunami education materials.

A map illustration of the seafloor off of a coastal area, that shows the features like submarine canyons and depth.

The Question: Soon after the devastating tsunamis in the Indian Ocean on December 26, 2004 and in Japan on March 11, 2011, many people have asked, "Could such a tsunami happen in the United States?"

Illustration shows a cross-section of a coastline and the beginnings of a tsunami wave that is caused by an earthquake.

In the past century, several damaging tsunamis have struck the Pacific Northwest coast (Northern California, Oregon, and Washington). All of these tsunamis were distant tsunamis generated from earthquakes located far across the Pacific basin and are distinguished from tsunamis generated by earthquakes near the coast—termed local tsunamis.

April 2011 in waterfront area of Tohoku, Japan following the March 11, 2011 earthquake and tsunami.

Probabilistic Forecasting of Earthquakes, Tsunamis, and Earthquake Effects in the Coastal Zone

A building after an earthquake has crumbled the roof and brick walls, the interior is now visible.

Coastal and Marine Geohazards of the U.S. West Coast and Alaska

A home, severely damaged by the tsunami that hit Sumatra on December 26, 2004, sits atop debris.

PubTalk 1/2017 — Unusual sources of tsunamis

A presentation on "Unusual Sources of Tsunamis From Krakatoa to Monterey Bay" by Eric Geist, USGS Research Geophysicist

- Not all tsunamis are generated by earthquakes. - Tsunamis can be caused by volcanoes, landslides, and even atmospheric disturbances - Data from tide gauges can help unravel the complex physics of these sources

Below are USGS publications on a wide variety of topics related to tsunamis.

Earthquake magnitude distributions on northern Caribbean faults from combinatorial optimization models

On-fault earthquake magnitude distributions are calculated for northern Caribbean faults using estimates of fault slip and regional seismicity parameters. Integer programming, a combinatorial optimization method, is used to determine the optimal spatial arrangement of earthquakes sampled from a truncated Gutenberg-Richter distribution that minimizes the global misfit in slip rates on a complex fau

The making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)

Book review of "tsunami propagation in tidal rivers", by elena tolkova, catastrophic landscape modification from a massive landslide tsunami in taan fiord, alaska.

The October 17th, 2015 Taan Fiord landslide and tsunami generated a runup of 193 m, nearly an order of magnitude greater than most previously surveyed tsunamis. To date, most post-tsunami surveys are from earthquake-generated tsunamis and the geomorphic signatures of landslide tsunamis or their potential for preservation are largely uncharacterized. Additionally, clear modifications described duri

Recent sandy deposits at five northern California coastal wetlands — Stratigraphy, diatoms, and implications for storm and tsunami hazards

A recent geological record of inundation by tsunamis or storm surges is evidenced by deposits found within the first few meters of the modern surface at five wetlands on the northern California coast. The study sites include three locations in the Crescent City area (Marhoffer Creek marsh, Elk Creek wetland, and Sand Mine marsh), O’rekw marsh in the lower Redwood Creek alluvial valley, and Pillar

A combinatorial approach to determine earthquake magnitude distributions on a variable slip-rate fault

Introduction to “global tsunami science: past and future, volume iii”, effect of dynamical phase on the resonant interaction among tsunami edge wave modes, probabilistic tsunami hazard analysis: multiple sources and global applications, introduction to “global tsunami science: past and future, volume ii”, reducing risk where tectonic plates collide, reducing risk where tectonic plates collide—u.s. geological survey subduction zone science plan.

Below are news stories about tsunamis.

National Preparedness Month 2020: Earthquakes and Tsunamis

Natural hazards have the potential to impact a majority of Americans every year.  USGS science provides part of the foundation for emergency ...

A Tale of Two Tsunamis—Why Weren’t They Bigger? Mexico 2017 and Alaska 2018

Why do some earthquakes trigger large tsunamis, and others don’t? Learn how earthquakes produce tsunamis, how scientists predict tsunami size and...

Below are FAQs associated with tsunamis.

Tsunami-evacuation sign in the city of Nehalem, Oregon

Could a large tsunami happen in the United States?

Large tsunamis have occurred in the United States and will undoubtedly occur again. Significant earthquakes around the Pacific rim have generated tsunamis that struck Hawaii, Alaska, and the U.S. west coast. One of the largest and most devastating tsunamis that Hawaii has experienced was in 1946 from an earthquake along the Aleutian subduction zone. Runup heights reached a maximum of 33 to 55 feet...

Tsunami Evacuation Route

Is there a system to warn populations of an imminent occurrence of a tsunami?

NOAA (National Oceanic and Atmospheric Administration) maintains the U.S. Tsunami Warning Centers , and work in conjunction with USGS seismic networks to help determine when and where to issue tsunami warnings. Also, if an earthquake meets certain criteria for potentially generating a tsunami, the pop-up window and the event page for that earthquake on the USGS Latest Earthquakes Map will include...

Image: Tsunami Carried Boat

What are tsunamis?

Tsunamis are ocean waves triggered by: Large earthquakes that occur near or under the ocean Volcanic eruptions Submarine landslides Onshore landslides in which large volumes of debris fall into the water Scientists do not use the term "tidal wave" because these waves are not caused by tides. Tsunami waves are unlike typical ocean waves generated by wind and storms, and most tsunamis do not "break"...

What is it about an earthquake that causes a tsunami?

Although earthquake magnitude is one factor that affects tsunami generation, there are other important factors to consider. The earthquake must be a shallow marine event that displaces the seafloor. Thrust earthquakes (as opposed to strike slip) are far more likely to generate tsunamis, but small tsunamis have occurred in a few cases from large (i.e., > M8) strike-slip earthquakes. Note the...

Large waves crashing on rocks at beach.

What is the difference between a tsunami and a tidal wave?

Although both are sea waves, a tsunami and a tidal wave are two different and unrelated phenomena. A tidal wave is a shallow water wave caused by the gravitational interactions between the Sun, Moon, and Earth ("tidal wave" was used in earlier times to describe what we now call a tsunami.) A tsunami is an ocean wave triggered by large earthquakes that occur near or under the ocean, volcanic...

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  • Open access
  • Published: 15 April 2021

Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks

  • Fumiyasu Makinoshima   ORCID: orcid.org/0000-0001-9247-4104 1 ,
  • Yusuke Oishi   ORCID: orcid.org/0000-0003-4264-8932 1 ,
  • Takashi Yamazaki 1 ,
  • Takashi Furumura   ORCID: orcid.org/0000-0002-2091-0533 2 &
  • Fumihiko Imamura   ORCID: orcid.org/0000-0001-7628-575X 3  

Nature Communications volume  12 , Article number:  2253 ( 2021 ) Cite this article

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  • Natural hazards

Rapid and accurate hazard forecasting is important for prompt evacuations and reducing casualties during natural disasters. In the decade since the 2011 Tohoku tsunami, various tsunami forecasting methods using real-time data have been proposed. However, rapid and accurate tsunami inundation forecasting in coastal areas remains challenging. Here, we propose a tsunami forecasting approach using convolutional neural networks (CNNs) for early warning. Numerical tsunami forecasting experiments for Tohoku demonstrated excellent performance with average maximum tsunami amplitude and tsunami arrival time forecasting errors of ~0.4 m and ~48 s, respectively, for 1,000 unknown synthetic tsunami scenarios. Our forecasting approach required only 0.004 s on average using a single CPU node. Moreover, the CNN trained on only synthetic tsunami scenarios provided reasonable inundation forecasts using actual observation data from the 2011 event, even with noisy inputs. These results verify the feasibility of AI-enabled tsunami forecasting for providing rapid and accurate early warnings.

Introduction

The rapid forecasting of hazards and dissemination of warnings can increase evacuation lead times and thus are key to saving lives during natural disasters. For tsunami disasters, quick evacuations supported by such warnings can drastically reduce the number of casualties, but inaccurate hazard forecasts and warnings can have the opposite effect. During the 2011 Tohoku tsunami event, the earthquake magnitude and tsunami height were initially underestimated based on a precomputed database; as a result, some residents felt safe based on the initial warning and were unaware of the need for evacuation 1 . Although the warning was updated several times based on additional observations, the updated information could not always reach the residents due to communication disruptions; consequently, many of the coastal residents did not realise the tsunami risk at their locations. The underestimation of tsunami risk increased the number of tsunami-induced casualties, and as a result, Japan experienced the loss of over 18,000 citizens, even with the in-place warning system. The need for accurate and reliable early warnings has been common during past mega-tsunamis; notably, the 2004 Indian Ocean tsunami had a catastrophic regional impact 2 , and the immense loss of nearly 230,000 lives stressed the importance of tsunami early warning systems, resulting in efforts to establish tsunami early warning frameworks in broader regions 3 . Therefore, fast and accurate tsunami forecasting methods based on real-time tsunami observation data are urgently needed, and the early warnings provided can contribute to mitigating casualties in future tsunami events.

To date, tsunami early warning systems have been developed based on past tsunami catastrophes and available technologies 4 . Especially in the decade since the 2011 Tohoku tsunami, dense tsunami observation networks have been implemented 5 , 6 , and various tsunami forecasting methods using real-time observation data, such as real-time tsunami inundation simulations using supercomputers 7 with rapid source estimations 8 , 9 and data assimilation approaches 10 , 11 , have been proposed based on the lessons learned from the 2011 event. However, real-time tsunami inundation forecasting immediately after an earthquake has remained challenging due to the difficulty of rapid estimation of the tsunami source, in which various uncertainties exist 12 , and due to the high computational costs associated with simulating nonlinear tsunami propagation in shallow water.

To overcome the above challenges, we present a tsunami forecasting method using a convolutional neural network (CNN) developed as a deep learning approach in AI research. The present method is capable of directly forecasting tsunami inundation based solely on up-to-date observation data and does not require extensive computational resources, such as those provided by supercomputers. During the past decade, deep learning has achieved great success in image and pattern recognition 13 as well as in broader areas, including physics-based simulations such as structural analysis 14 and computational fluid dynamics 15 ; moreover, tsunami observation networks have been improved.

In this work, we utilise a CNN to process valuable data from dense tsunami and geodetic observation networks and achieve remarkable tsunami forecasting performance. A notable advantage of a CNN is its low computational cost; i.e., the computational cost of CNN inference is much lower than that of nonlinear tsunami propagation simulations. Additionally, the present approach does not require a tsunami source estimation process since our CNN is designed for end-to-end forecasting from observation data to tsunami inundation forecasting. Therefore, the present CNN can immediately and accurately predict the tsunami inundation time series at a single location. To the best of our knowledge, this study is the first attempt at end-to-end tsunami inundation forecasting with a CNN, and the results verify the feasibility of AI-enabled tsunami forecasting for the establishment of early warnings.

CNN tsunami forecasting

Figure  1 shows a schematic view of the proposed tsunami forecasting method based on a CNN. First, 10,000 cases of numerical tsunami propagation and inundation simulations solving a set of partial differential equations were conducted to prepare the data sets for training the CNN. In the simulations, sets of synthetic observation data at observation points and the resulting tsunami inundation waveforms within 2 h were calculated based on randomly generated tsunami scenarios. For the tsunami observations obtained with ocean bottom pressure gauges, simulated tsunami waveforms converted into pressure waveforms at the ocean bottom were used as inputs. Then, we trained a CNN with synthetic tsunami data to directly predict the tsunami inundation waveform at a single onshore location (the green star in Fig.  1 ) solely from the observations. We built a 1-D CNN comprising a total of 15 layers for tsunami forecasting since compact 1-D CNNs are suited for real-time and low-cost applications because of their low computational complexity 16 . The stacked waveforms were fed into the network as inputs, and the corresponding features were extracted via convolutional and pooling-like layers. To consider additional observations as inputs for the network, we simply stacked same-size arrays so that the size of the input channels equalled the considered number of observation points. Since recent studies on tsunami source inversion suggest that inland geodetic observational data are useful for determining the tsunami source 8 , 9 , 17 , we also considered inland geodetic observations of initial ground heights as additional inputs to the CNN. The geodetic observations were fed as vectors with the same length as the offshore waveforms. The onshore tsunami inundation waveform was then forecasted by the fully connected layers using the features extracted from the convolutional layers. In contrast to general time series forecasting using deep learning, in which the subsequent transition of values at future time t  +  h is predicted based on the available observations at time t in the same series 18 , our network predicts a future onshore tsunami inundation waveform based on the available observations at different offshore and onshore points. After the training process, the trained CNN can output a tsunami inundation waveform at a single location on land (the green star in Fig.  1 ) when observation data for an unknown tsunami are given. Preparing multiple networks enables tsunami forecasting for multiple points.

figure 1

The CNN learns the relation between observation data and the resulting tsunami waveform from thousands of numerical simulation results. The tsunami and geodetic observation points are uniformly selected to cover the forecasting areas and are illustrated as red points in the map. After the training process is completed, the trained CNN can predict a time series of inundation tsunami waveform at the onshore forecasting site, which is represented with a green star, solely from observation data for unknown tsunami scenarios.

Data preparation for the CNN

The source fault geometry of the 2011 Tohoku-Oki earthquake was considered based on the parameters presented in Fujii et al. 19 , and 44 sub-faults were used to generate synthetic tsunami data sets for the training of CNN (Fig.  2a ). Here, the slip parameters of each sub-fault were randomly assigned within their defined range for the sub-faults to generate various tsunami scenarios. We conducted tsunami simulations for a total of 12,000 scenarios and obtained observations for inputs (red points in Fig.  2a ) and an onshore tsunami inundation waveform at a single location (the green star in Fig.  2b ) to train the CNN. A total of 49 offshore tsunami observation points and five onshore geodetic observation points were considered as inputs for the CNN based on an actual tsunami observation network 20 and the Global Navigation Satellite System observation network 21 in Japan. These tsunami and geodetic observation points were selected to cover the fault region and the forecasting point. The offshore tsunami observation waveforms were sampled at 1 Hz, and a constant deformed ground height with the same number of samples was considered as the onshore geodetic observations. The forecasting waveform was sampled at 0.5 Hz with a data size of 3600. For the observation data, various time windows of tsunami observations (e.g., 5, 10, 15, 20, 25, and 30 min) were considered as inputs for the CNN to investigate the effect of the observation length on the forecasting accuracy. From the 12,000 simulation results, we used 10,000 cases for training, 1000 cases for validation monitoring, and 1000 for testing. The generated earthquake scenarios have a seismic moment M 0 ranging between 4.03 × 10 22 and 8.21 × 10 22  Nm, which corresponds to a moment magnitude M w ranging from 9.0 to 9.2, assuming a rigidity of 30 GPa (Fig.  2c ). The initial sea-bottom deformation caused by an earthquake was calculated with Okada’s formula 22 , and the tsunami propagation and inundation were simulated using TUNAMI-N2 code (see “Methods” for details). A single tsunami simulation for 2 h to generate data for the CNN required ~3 h using a CPU node with two Intel Xeon Gold 6148 processors with 384 GiB memory.

figure 2

a Fault geometry and observation point as input for convolutional neural network (CNN). Red circles represent observation points for CNN. A total of 49 offshore tsunami observation points and five onshore geodetic observation points were considered to cover the fault region and the forecasting point (the green star). Fault geometry is considered based on Fujii et al. 19 , but its slip amount is randomly assigned within a given range to generate various tsunami scenarios. b A close view of the forecasting site, the Sendai plain where experienced the large extent of tsunami inundation. The green star is the forecasting site for which the CNN forecast a tsunami inundation waveform. c Distribution of the seismic moment of generated earthquake scenarios. A total of 12,000 scenarios were generated and divided into 10,000 training sets, 1000 validation sets and 1000 test sets.

Network configuration and training

The constructed CNN consisted of nine convolutional layers and three pooling-like convolutional layers followed by three fully connected layers (Supplementary Table  1 ). Each convolutional layer was followed by a Leaky ReLU activation function 23 with a negative slope parameter of a  = 0.01. Dropout with a dropout ratio of p  = 0.5 was applied to the outputs of the fully connected layers to prevent overfitting 24 . In the convolutional layers, we set the kernel size, stride and padding to minimise changes in the array size, and dimensionality reduction was performed mainly by the pooling-like layers. For the 5, 15, 25 and 35 min observation cases, the kernel size of the last convolutional layer was set as 3 to prevent the remainder from being generated, but such changes were minimised to evaluate the effect of the observation lead time. Thus, a longer observation period leads to a greater number of learnable parameters in the network.

We considered the mean squared error (MSE) between the simulated and forecasting waveforms over the forecasting time as the loss function. The network parameters were optimised to minimise the loss function by the Adam optimisation algorithm 25 . We used the default parameters suggested in the original paper that proposed the Adam algorithm ( β 1  = 0.9, β 2  = 0.999 and ε  = 10 −8 ), except for a step size of α  = 10 −4 . The training of the network was performed in the AI Bridging Cloud Infrastructure, a GPU-accelerated supercomputer in which each node has two Intel Xeon Gold 6148 CPUs and four NVIDIA Tesla V100 SXM2 GPUs with 384 GiB memory. The network training and validation in this study were implemented using Pytorch 26 and Horovod 27 . The batch size for each GPU during the training phase was 25, and five computer nodes were used for training. We considered 3000 epochs in the training process and retained the model that yielded the minimum validation loss for the validation data sets during training. The training process was completed within 2 h even for the largest network in this study.

Forecasting performance on synthetic tsunamis

We evaluated the performance of the trained CNN by analysing the forecasting results for 1000 test tsunami scenarios that were not considered during the training process, and we confirmed that the CNN successfully predicted the tsunami inundation waveform at the forecasting site (Fig.  3b ). For the evaluation of the forecasting accuracy, we considered two metrics: the maximum tsunami amplitude, defined as the maximum value of the tsunami amplitude over the forecasting time, and the tsunami arrival time, defined as the time when the tsunami flow depth first exceeds 10% of the maximum flow depth. Even with only 5 min offshore tsunami and inland geodetic observations, the mean absolute errors of the maximum tsunami amplitude and the tsunami arrival time were 0.4 m and 47.7 s, respectively. The average relative errors were 8.1% and 1.2% for the maximum tsunami amplitude and the tsunami arrival time, respectively. For these forecasts, the trained CNN required only 0.004 s on average using a single CPU node with 40 cores; this approach is much faster than conventional simulation-based forecasting approaches and requires fewer computational resources. The trained CNN yielded accurate and rapid forecasts of both the tsunami size and the arrival time for various tsunami scenarios directly from the observations.

figure 3

a Observation points and a snapshot of tsunami propagation at 300 s for a simulated test scenario. The location of the observation points as inputs for the CNN are illustrated as circles. The forecasting site is represented with the green star. b Result of tsunami inundation forecasting with the CNN at the forecasting site with the ground-truth simulation result. c Error distribution of the maximum tsunami amplitude at the forecasting site for 1000 test scenarios.

Effects of the offshore observation length and geodetic data

We investigated the effect of the length of the observation inputs (5, 10, 15, 20, 25 and 30 min) and the importance of geodetic observation inputs by training different CNN models with different inputs (Fig.  4 ). The results show that longer observations led to higher forecasting accuracy. Using the geodetic observation data, the CNN achieved good forecasting performance equivalent to that of a CNN with a long observation period. We confirmed that this performance improvement achieved with comparatively long-term observations and geodetic data corresponds mainly to increased accuracy in the initial ground height estimation in which the CNN with a short observation length and without geodetic data exhibited poor accuracy (Supplementary Fig.  1 ). This result can be explained by the characteristics of tsunami long wavelength. Since the propagation speed of a tsunami is slow in shallow water, it is difficult to obtain the waveforms generated from nearshore faults that cause coastal subsidence with a short observation length; thus, short-term observations alone are not sufficient for estimating the magnitude of subsidence. Inverting the offshore fault slip distribution from onshore geodetic information can lead to non-unique solutions; however, geodetic data offer direct information about subsidence much faster than offshore observations. The proposed CNN integrated different information types from different observations and achieved the presented forecasting performance, even with very short observation periods.

figure 4

The height of the bar represents the mean value for 1000 test scenarios, and the error bar in the figure represents the standard deviation.

Sensitivity of offshore and onshore observations

To understand information processing in CNN tsunami forecasting models, we conducted a sensitivity analysis (e.g., occlusion test 28 , 29 ) of the trained CNN models. In this analysis, we systematically removed inputs from certain observation points and evaluated the resulting amounts of change in the forecasting results to represent the impact on the CNN. The sensitivity analysis was conducted for models with different observation lengths, and the sensitivity of the observation points for forecasting was visualised (Fig.  5 ). The observation points with high sensitivities were located in the specific region of the observation network along the major path of tsunamis towards the forecasting site. High sensitivities were observed mainly over large slip areas since the amount of slip on an offshore fault has a predominant influence on tsunami inundation. In contrast, the information from distant observation points had almost no effect on the forecast, and thus, this information was not important for the CNN. This result indicates that for an accurate forecast, the CNN requires only certain observation points along the path of a tsunami propagating to a forecasting site. As the observation time increases, high sensitivities can also be confirmed at nearshore tsunami observation points. The CNNs using both offshore tsunami and onshore geodetic observations showed high sensitivities at both onshore and offshore points. The increased sensitivities for nearshore tsunami observations with longer observation periods and the higher sensitivities for additional onshore geodetic observations suggest that the CNN effectively integrates available information within limited observation periods to achieve high forecasting accuracy.

figure 5

a CNN trained only with offshore tsunami observation data. b CNN trained with both offshore tsunami and onshore geodetic observation data. The observation points with high sensitivity indicate that the forecasting result changes considerably when the inputs from these observation points are lacking.

Forecasting speed

The computational time for tsunami forecasting with the CNN was measured to investigate the forecasting speed and assess the ability of the method to be employed for the issuance of tsunami warnings. Here, we measured the time required to forecast 1000 test scenarios using a single CPU node with 40 cores. Table  1 reports the average computational time for tsunami forecasting. The computational time increases as the observation time increases since a consistent CNN architecture is adopted for all observation lengths; a large neural network structure and a corresponding increase in the number of parameters are needed for longer observation times. The addition of 5 min observations increased the number of parameters by ~13 million, mainly due to the larger size of the fully connected layer after the convolutional layers. Nevertheless, the computational time for tsunami forecasting with the CNN was only 0.011 s, even for the largest CNN settings in the test (30 min offshore tsunami observations with geodetic observations). The addition of five geodetic input channels slightly increased the number of parameters by 1920 but had a negligible effect on the computational time. Thus, for CNN tsunami forecasting, the use of geodetic data was effective from the perspectives of not only the forecasting accuracy but also the computational time required to consider additional inputs. The tsunami forecasting speed achieved by the CNN is sufficiently fast to provide tsunami warnings, even with limited computational resources.

Application to the 2011 Tohoku tsunami event

We trained the CNNs using the 10,000 synthetic tsunami scenarios with the observation settings at the time the 2011 Tohoku tsunami event occurred and investigated the forecasting performance of the CNN for real events using real-world data (Fig.  6a ). This application used the same network configuration and the same 10,000 tsunami scenarios employed in the previous tests using synthetic data. We used publicly available observation data 30 , 31 during the event as inputs for the CNN (Fig.  6b, c ), i.e., three offshore tsunami observations recorded by GPS buoys (803, 801 and 806) and three onshore GNSS observations (Rifu, Watari and Souma1). For the missing parts in the tsunami waveforms observed by GPS buoys, 1 Hz data were prepared by cubic interpolation. For the geodetic observations, displacements at 5 min after the occurrence of the earthquake were used as inputs. Since complete data were not available at Souma1, we used the latest observation as the input. The forecasting site is shown as the green star in Fig.  6a . Most of the buildings around the forecasting site were totally destroyed by the tsunami; however, Arahama Elementary School (Arahama ES, illustrated as the black cross in Fig.  6a ) located close to the forecasting site provided survey results to verify the inundation forecast. During the 2011 event, the tsunami reached the second floor of the Arahama ES 32 , and a survey immediately after the event reported that tsunami debris were found at a height of 4.62 m above the school basement 33 . The arrival time of the devastating tsunami at this site was also estimated as ~15:55 (JST) based on a stopped clock 34 .

figure 6

The elapsed time is from the earthquake occurrence. a Observation points for the CNN and the forecasting site. The red triangles represent the offshore tsunami observation points (803, 801, 806), and the red circles represent onshore geodetic observation points (Rifu, Watari, Souma1). The red cross mark in the small-scale map represents the epicentre of the 2011 earthquake. The forecasting site is shown in the large-scale map as the green star. Arahama ES (the black cross mark) where tsunami inundation traces for validation are available is located close to the forecasting site. b Tsunami waveforms at 803, 801 and 806 observed during the 2011 event 31 . c Geodetic observations at Rifu, Watari and Souma1 observed during the 2011 event. d Results of tsunami inundation forecasting with different observation periods. Grey triangles represent the observation interval used for forecast. Red waveforms are the CNN forecasting results. The height and position of the black cross mark represents the surveyed tsunami inundation trace at Arahama ES. e Results of offshore tsunami waveform forecasting at the Sendai New Port with different observation periods. Grey triangles represent the observation interval used for forecast. Red waveforms are CNN forecasting results. Grey lines are observed waveforms at the Sendai New Port during the 2011 event. Full observation was not available because the gauge was destroyed by the tsunami.

We trained the CNNs using only synthetic tsunami data with different observation periods and forecasted the tsunami inundation waveform using the actual observation data as inputs for the CNNs (Fig.  6d ). Initially, the CNNs forecasted unphysical waveforms with 20 min or less observations, when almost no tsunami signals were available. After obtaining the first positive peak of the tsunami from 30 min observations, the CNN forecasted an inundation waveform, but the forecasted amplitude was small compared to the actual trace at Arahama ES. With the 35 min observations, in which the entire first positive peak of the tsunami is available, the CNN forecasted a maximum flow depth of 3.88 m at 3974 s after the earthquake. Further 40 min observation revealed the negative peak of the tsunami, enabling the forecast of a larger inundation depth (5.64 m at 3952 s). Although the reported tsunami traces do not directly reflect the actual maximum flow depth, the CNN forecasting can be considered reasonably accurate. However, the forecasted arrival time of the maximum flow depth was ~3 min earlier than the estimated arrival time indicated by the stopped clock at the Arahama ES.

To further verify the CNN forecasting result, we trained the CNN for forecasting a tsunami waveform at the Sendai New Port where a time series of the tsunami until the first peak was recorded by a wave gauge (subsequent observations were not available because the gauge was destroyed by the tsunami). The forecasting results for the Sendai New Port are summarised in Fig. 6e . Similar to the previous inundation forecasting results, the CNN could not provide a reasonable forecast with 20 min or less observation time when sufficient tsunami signals were not available. After obtaining the positive peak of the tsunami using 30 min observation, the CNN could forecast the tsunami having the first peak value of 6.78 m, which is compatible with the observed first peak (6.62 m). The CNNs trained with 35 and 40 min observations forecasted similar peaks (5.77 and 6.42 m, respectively), and the rise of the first wave was more consistent with the observation data. Nevertheless, even with sufficient offshore tsunami observations, an ~3 min difference between the observation and the forecast appeared in the tsunami arrival time, suggesting that the CNN forecast tends to be slightly earlier than the actual tsunami arrival. A possible cause of this earlier arrival tendency is the effect of rupture propagations, i.e., when generating tsunami data sets for the CNN, the effect of rupture delays on each sub-fault was not considered, and instantaneous slip was assumed; however, the tsunami source inversion assuming sub-faults with multiple time windows suggested that the duration of the tsunamigenic slip of the 2011 Mw 9.0 earthquake lasted ~2.5–3 min 35 , and this duration was consistent with the difference in the arrival time. The current CNN forecast provides a slightly earlier arrival tendency and might serve as a cautious warning; however, to forecast the tsunami arrival time more accurately for large earthquakes, it may be necessary to consider the effect of rupture propagation on faults in large earthquakes.

Recent high-sampling-rate tsunami observations, especially by ocean bottom pressure gauges, can capture a wide range of geophysical phenomena in the ocean including ocean currents and seismic waves with much shorter periods (seconds to minutes) than that of tsunamis (minutes to hours) 36 . Consequently, such non-tsunami components can affect tsunami forecasting as noise 37 . To investigate the performance of the CNN under actual observation conditions, we further evaluated the effect of the short-period observation noise on the CNN tsunami forecasting. Noise waveforms were obtained using actual sea-level observation data at the GPS buoys 803, 801 and 806 a day before the 2011 tsunami event (Fig.  7a, b ), and the effect of noisy tsunami input on the forecasting results was evaluated using 40 min observation data (Fig.  7c–e ). For this evaluation, the similarity between the forecasting results with and without noise were evaluated using the formula for calculating the variance reduction 38 ; therefore, the similarity becomes 100% for a perfect match and lower for misfits. The result demonstrated that even with disturbances, the CNN successfully forecasted both the inundation and offshore waveforms with a small difference. The similarity between forecasting result with and without noise was 99.999% for inundation forecasting and 99.997% for offshore waveform forecasting. Additionally, we also examined the effect of much larger noise on the inundation forecasts. Larger noise waveforms were generated by adding white noise that ranged from −1.0 to 1.0 and was amplified by a certain ratio of the maximum observation amplitude. The additional noise test demonstrated that the CNN tsunami forecast maintained a high similarity of 99.7% on average for 1000 noisy inputs, even with a large noise level of 20% (see Supplementary Fig.  2 ).

figure 7

a Sea-level observations from GPS buoys before the 2011 tsunami event. The grey and red lines represent the raw observations and the 15 min running average of the raw data, respectively. The elapsed time is from a day before the earthquake occurrence. b Noise waveforms extracted from the observations. The deviation from the averaged data (the red lines in a ) is extracted as noise. The figure shows the noise waveforms during the same observation period for the actual tsunami observations, but a day before the tsunami. c Noise waveforms for CNN input. The extracted noise waveforms before the event are added to the actual tsunami observations to prepare the noise inputs. d Inundation forecasting results with and without noise. The grey and red lines represent the forecasting results without and with noise, respectively. Because the difference between the two waveforms is small, the result without noise is illustrated with a thicker line. e Offshore tsunami forecasting results with and without noise. The grey and red lines represent the forecasting results without and with noise, respectively. Because the difference between the two waveforms is small, the result without noise is illustrated with a thicker line.

In this study, we presented a tsunami inundation forecasting approach based on a CNN trained with a large quantity of synthetic tsunami scenarios and verified the approach against both synthetic tsunamis and the actual tsunami observations from the 2011 Tohoku earthquake. The CNN tsunami forecast in this study is expected to overcome the bottlenecks of previous simulation-based real-time tsunami forecasting approaches for real applications, such as the difficulty of rapid tsunami source estimations immediately after the earthquake and high computational costs for simulating nonlinear tsunami propagations.

Large earthquakes generating tsunamis are infrequent, resulting in a lack of sufficient data to train the CNN; however, the test using actual observations during the 2011 tsunami event demonstrated that the CNN trained on only synthetic tsunamis can provide an accurate tsunami inundation forecast even for a real tsunami if the target tsunami scenario exists within the distribution of the training data sets. Therefore, it is important to prepare as many tsunami scenarios as possible for training the CNN to address the various mechanisms generating tsunamis.

Large tsunamis can be caused by large slip at plate boundaries, which is a common tsunami generation mechanism, as well as different types of mechanisms, such as a steep angle slip of splay faults 39 , outer-rise normal faults 40 and slow slip at a shallow plate boundary (tsunami earthquake) 41 . Additionally, non-seismic sources, such as volcanic eruptions 42 and landslides 43 , can also cause large tsunamis. To address these various tsunami sources by the CNN tsunami forecasting, a wide variety of tsunami scenarios should be included in the training set. A promising solution to address the various types of tsunami is to generate synthetic tsunami scenarios directly assuming a sea surface fluctuation (e.g., using Gaussian distributions having a range of several kilometres or tens of kilometres) rather than considering a sea surface deformation based on fault movements. Local tsunamis caused by volcanic eruptions or landslides can be represented with a few sea surface displacement units 44 , 45 , and the initial tsunami profiles generated by large earthquakes can also be represented by a superposition of a series of sea surface displacement units 46 , 47 , 48 . Simulating a wide variety of tsunami scenarios and training CNNs on large data sets are computationally expensive; however, it is feasible given the recent advances in high-performance computing of tsunami simulations and an efficient training approach as demonstrated in this study. CNN tsunami forecasting trained on various sea surface displacements should have the potential to be applied to a wide variety of tsunamis, including non-seismic tsunamis, for which the issuance of early warnings has been difficult by employing conventional earthquake-triggered approaches.

Tsunami simulation

We used the TUNAMI-N2 code 49 , 50 , which is distributed by the Tsunami Inundation Modelling Exchange project of the International Union of Geodesy and Geophysics and Intergovernmental Oceanographic Commission of United Nations Educational, Scientific and Cultural Organisation 50 , to create the tsunami simulation data for training the CNNs. The TUNAMI-N2 solves the following nonlinear shallow water Eqs. ( 1 )–( 3 ) with a staggered-grid finite-difference method:

where η is the tsunami height, D is the water depth, and M and N are the velocity fluxes in the x and y directions, respectively. g is the gravitational acceleration (=9.81 m/s 2 ), and n is Manning’s roughness coefficient, which we set to 0.025 s/m 1/3 in the simulations in this paper. As employed in general tsunami simulations, nested grid configurations were prepared in which the grid size was decreased by a factor of 3 (1215, 405, 135, 45 and 15 m) to reduce the computational cost; accordingly, tsunamis in coastal areas are evaluated at higher resolutions than those in offshore areas. Bathymetry data projected onto the Japan Plane Rectangular CS X were used for the simulation. The entire calculation domain with nested grids is illustrated in Supplementary Fig.  3 . The finest domain (∆ x  = ∆ y  = 15 m) covers the Sendai Plain, which experienced a large inundation extent and devastating damage during the 2011 tsunami. The time step of the simulation was set to ∆ t  = 0.2 s.

Sensitivity analysis

To evaluate the sensitivity at each station, we set the signal from a station to zero and evaluated the change in the MSE over 1000 test scenarios. An occlusion test was conducted for every observation point, and the sensitivity of an observation point was evaluated based on the relative change in the MSE from the baseline MSE (no occlusion). The sensitivity is calculated by Eq. ( 4 ):

where n is the number of test scenarios, \({\mathrm{MSE}}^\prime\) is the MSE with occlusion, and MSE is the MSE without occlusion (baseline).

Data availability

The tsunami observation data used in this study are available from The Nationwide Ocean Wave information network for Ports and HArbourS, NOWPHAS ( https://www.mlit.go.jp/kowan/nowphas/index_eng.html ). The GNSS observation data processed by Shu and Xu are used and available from ( https://doi.pangaea.de/10.1594/PANGAEA.914110 ). Other relevant data in this study are available from the corresponding author upon reasonable request.

Code availability

The code that supports the findings in this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The computational resources of the AI Bridging Cloud Infrastructure (ABCI) provided by the National Institute of Advanced Industrial Science and Technology (AIST) were used. The topography and bathymetry data used in the tsunami numerical simulation were obtained by integrating the data from the Central Disaster Prevention Council; Tohoku Regional Development Bureau of Ministry of Land, Infrastructure, Transport and Tourism (MLIT); and Geospatial Information Authority of Japan. We used the observation data of the 2011 Tohoku tsunami obtained from the Nationwide Ocean Wave information network for Ports and HArbourS, NOWPHAS. The NOWPHAS tsunami and tidal observation data are observed by Ports and Harbours Breau, MLIT and processed by Port and Airport Research Institute.

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Fumiyasu Makinoshima, Yusuke Oishi & Takashi Yamazaki

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F.M. and Y.O. designed the research. F.M. and T.Y. prepared the simulation codes. F.I. prepared the data during the 2011 tsunami event. F.M. and T.F. wrote the manuscript. T.F. and F.I. contributed to data interpretation and provided discussions to improve the quality of the paper.

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Makinoshima, F., Oishi, Y., Yamazaki, T. et al. Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks. Nat Commun 12 , 2253 (2021). https://doi.org/10.1038/s41467-021-22348-0

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case studies of tsunami

case studies of tsunami

The Science of Tsunamis

case studies of tsunami

The word “tsunami” brings immediately to mind the havoc that can be wrought by these uniquely powerful waves. The tsunamis we hear about most often are caused by undersea earthquakes, and the waves they generate can travel at speeds of up to 250 miles per hour and reach tens of meters high when they make landfall and break. They can cause massive flooding and rapid widespread devastation in coastal areas, as happened in Southeast Asia in 2004 and in Japan in 2011.

But significant tsunamis can be caused by other events as well. The partial collapse of the volcano Anak Krakatau in Indonesia in 2018 caused a tsunami that killed more than 400 people. Large landslides, which send immense amounts of debris into the sea, also can cause tsunamis. Scientists naturally would like to know how and to what extent they might be able to predict the features of tsunamis under various circumstances.

Most models of tsunamis generated by landslides are based on the idea that the size and power of a tsunami is determined by the thickness, or depth, of the landslide and the speed of the “front” as it meets the water. In a paper titled “Nonlinear regimes of tsunami waves generated by a granular collapse,” published online in the Journal of Fluid Mechanics, UC Santa Barbara mechanical engineer Alban Sauret and his colleagues, Wladimir Sarlin, Cyprien Morize and Philippe Gondret at the Fluids, Automation and Thermal Systems (FAST) Laboratory at the University of Paris-Saclay and the French National Centre for Scientific Research (CNRS), shed more light on the subject. (The article also will appear in the journal’s July 25 print edition.)

This is the latest in a series of papers the team has published on environmental flows, and on tsunami waves generated by landslides in particular. Earlier this year, they showed that the velocity of a collapse — i.e., the rate at which the landslide is traveling when it enters the water — controls the amplitude, or vertical size, of the wave.

In their most recent experiments, the researchers carefully measured the volume of the granular material, which they then released, causing it to collapse as a cliff would, into a long, narrow channel filled with water. They found that while the density and diameter of the grains within a landslide had little effect on the amplitude of the wave, the total volume of the grains and the depth of the liquid played much more crucial roles.

An illustration reflecting the fluid dynamics of a tsunami

A photograph of an experimental lab setup reflecting the fluid dynamics of a tsunami

Photo Credit:   ILLUSTRATION COURTESY OF FAST AND UC SANTA BARBARA

“As the grains enter the water, they act as a piston, the horizontal force of which governs the formation of the wave, including its amplitude relative to the depth of the water,” said Sauret. (A remaining challenge is to understand what governs the speed of the piston.) “The experiments also showed that if we know the geometry of the initial column [the material that flows into the water] before it collapses and the depth of the water where it lands, we can predict the amplitude of the wave.”

The team can now add this element to the evolving model they have developed to couple the dynamics of the landslide and the generation of the tsunami. A particular challenge is to describe the transition from an initial dry landslide, when the particles are separated by air, to an underwater granular flow, when the water has an important impact on particle motion. As that occurs, the forces acting on the grains change drastically, affecting the velocity at which the front of grains that make up the landslide enters the water.

Currently, there is a large gap in the predictions of tsunamis based on simplified models that consider the field complexity (i.e., the geophysics) but do not capture the physics of the landslide as it enters the water. The researchers are now comparing the data from their model with data collected from real-life case studies to see if they correlate well and if any field elements might influence the results.

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The Science of tsunamis

The word "tsunami" brings immediately to mind the havoc that can be wrought by these uniquely powerful waves. The tsunamis we hear about most often are caused by undersea earthquakes, and the waves they generate can travel at speeds of up to 250 miles per hour and reach tens of meters high when they make landfall and break. They can cause massive flooding and rapid widespread devastation in coastal areas, as happened in Southeast Asia in 2004 and in Japan in 2011.

But significant tsunamis can be caused by other events as well. The partial collapse of the volcano Anak Krakatau in Indonesia in 2018 caused a tsunami that killed more than 400 people. Large landslides, which send immense amounts of debris into the sea, also can cause tsunamis. Scientists naturally would like to know how and to what extent they might be able to predict the features of tsunamis under various circumstances.

Most models of tsunamis generated by landslides are based on the idea that the size and power of a tsunami is determined by the thickness, or depth, of the landslide and the speed of the "front" as it meets the water. In a paper titled "Nonlinear regimes of tsunami waves generated by a granular collapse," published online in the Journal of Fluid Mechanics , UC Santa Barbara mechanical engineer Alban Sauret and his colleagues, Wladimir Sarlin, Cyprien Morize and Philippe Gondret at the Fluids, Automation and Thermal Systems (FAST) Laboratory at the University of Paris-Saclay and the French National Centre for Scientific Research (CNRS), shed more light on the subject. (The article also will appear in the journal's July 25 print edition.)

This is the latest in a series of papers the team has published on environmental flows, and on tsunami waves generated by landslides in particular. Earlier this year, they showed that the velocity of a collapse -- i.e., the rate at which the landslide is traveling when it enters the water -- controls the amplitude, or vertical size, of the wave.

In their most recent experiments, the researchers carefully measured the volume of the granular material, which they then released, causing it to collapse as a cliff would, into a long, narrow channel filled with water. They found that while the density and diameter of the grains within a landslide had little effect on the amplitude of the wave, the total volume of the grains and the depth of the liquid played much more crucial roles.

"As the grains enter the water, they act as a piston, the horizontal force of which governs the formation of the wave, including its amplitude relative to the depth of the water," said Sauret. (A remaining challenge is to understand what governs the speed of the piston.) "The experiments also showed that if we know the geometry of the initial column [the material that flows into the water] before it collapses and the depth of the water where it lands, we can predict the amplitude of the wave."

The team can now add this element to the evolving model they have developed to couple the dynamics of the landslide and the generation of the tsunami. A particular challenge is to describe the transition from an initial dry landslide, when the particles are separated by air, to an underwater granular flow, when the water has an important impact on particle motion. As that occurs, the forces acting on the grains change drastically, affecting the velocity at which the front of grains that make up the landslide enters the water.

Currently, there is a large gap in the predictions of tsunamis based on simplified models that consider the field complexity (i.e., the geophysics) but do not capture the physics of the landslide as it enters the water. The researchers are now comparing the data from their model with data collected from real-life case studies to see if they correlate well and if any field elements might influence the results.

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Materials provided by University of California - Santa Barbara . Original written by James Badham. Note: Content may be edited for style and length.

Journal Reference :

  • Wladimir Sarlin, Cyprien Morize, Alban Sauret, Philippe Gondret. Nonlinear regimes of tsunami waves generated by a granular collapse . Journal of Fluid Mechanics , 2021; 919 DOI: 10.1017/jfm.2021.400

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Tsunami Case Studies

Profile image of Kieran  Hickey

Tsunamis are caused by geological processes, such as earthquakes, landslides, or volcanic eruptions, that displace large volumes of ocean water. Large-magnitude, subduction zone earthquakes, where two plates in the ocean push into each other, are the most common source of the recent large tsunamis. Submarine landslides, sometimes triggered by earthquakes, and coastal or submarine volcanoes also cause tsunamis. This chapter describes 10 modern and historic tsunami events that were significant in terms of their size, impact, extent, and/or triggering mechanisms. Each tsunami event is described using four different categories: (1) tsunami generation; (2) tsunami size, and extent (3) impact of the event at the local, regional and, where applicable, global scales; and (4) lessons learned in the aftermath of the event. The case studies are grouped according to the tsunamigenic source:

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Earthquakes and tsunamis are high-impact geohazard events that can be extremely destructive when they occur at large magnitudes and intensities, although their causes and potential locations are, for the most part, predictable within the framework of plate tectonics. Amongst the main reasons for their high impact include enormous numbers of casualties, extensive property damage in vast areas and significant social and economic disruption in urban settings where populous residential areas, global banking centres, industrial factories and critical facilities (nuclear power plants, dams) may be located. In order to reduce the impact of these geohazards, nations, societies, professional organizations and governments need to collaborate to prepare more effective seismic and tsunami risk assessments, disaster management plans, educational and training programmes for increased preparedness of the public, and strategic plans and objectives for capacity building, skill and knowledge transfer...

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The run-up catalogs of two global tsunami databases maintained by the NCEI/WDC NOAA and NTL/ICMMG SD RAS are examined to compile the list of annual maximum runups observed or measured in the oceanic, marine and inland basins during the last 120 years (from 1900 to 2019). All the retrieved annual maximum runups were divided into four groups according to four main types of tsunami sources (seismogenic, landslide-generated, volcanic, and meteorological). Their distribution over the type of sources shows that of the 120 maximum runups only 78 (65%) resulted from seismogenic sources, while the remaining 42 runups were divided between landslide-generated (19%), volcanic (8%), and meteorological (7.5%) sources. The analysis of geographical distribution of source locations demonstrates that tsunamis are not exclusively a marine hazard-over 15% of all maximum runups were observed in coastal and inland water basins (narrow bays, fiords, lakes, and rivers). Temporal distribution of the collected runups shows that annual occurrence of large tsunamis was more or less stable throughout the twentieth century and only demonstrates some increase during the last 27 years (since 1992) when the practice of post-event surveys of all damaging tsunamis was implemented. This paper also outlines the existing problems with data compilation, cataloguing, and distribution, and discusses incompleteness of runup and wave-form data for a considerable number of non-damaging tsunamis, even those resulting from the strong (magnitude higher than 7.5) submarine earthquakes.

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Tsunamis, commonly induced by undersea earthquakes, are formidable natural hazards capable of causing widespread devastation. This comprehensive chapter examines the complex dynamics of tsunamis, their generation mechanisms, and their broad-reaching impacts. The multifaceted nature of tsunami triggers, both seismic and non-seismic, is dissected, highlighting the role of undersea earthquakes, landslides, volcanic eruptions, and meteorological events in driving these devastating natural phenomena. The intricate interplay of seismic parameters such as magnitude, depth, and activity type is elaborated, underscored by an insightful case study on the 2011 Tohoku Earthquake and Tsunami. A pivotal part of the discussion lies in the exploration of non-seismic triggers of tsunamis, an area often overshadowed in tsunami studies. The impact of landslide-induced and volcanically triggered tsunamis is considered alongside the contentious topic of meteorologically influenced tsunami events. Delving further into the genesis of tsunamis, the chapter explores the influences of bathymetry and tectonic structures, particularly in the context of non-seismic tsunami generation. The chapter serves as a beacon for continuous research and predictive modeling in the field of tsunami studies, emphasizing the necessity for societal preparedness and strategic risk mitigation against these potent natural disasters.

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A comprehensive review on structural tsunami countermeasures

  • Review Article
  • Open access
  • Published: 16 May 2022
  • Volume 113 , pages 1419–1449, ( 2022 )

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Tsunamis pose a substantial threat to coastal communities around the globe. To counter their effects, several hard and soft mitigation measures are applied, the choice of which essentially depends on regional expectations, historical experiences and economic capabilities. These countermeasures encompass hard measures to physically prevent tsunami impacts such as different types of seawalls or offshore breakwaters, as well as soft measures such as long-term tsunami hazard assessment, tsunami education, evacuation plans, early-warning systems or coastal afforestation. Whist hard countermeasures generally aim at reducing the inundation level and distance, soft countermeasures focus mainly on enhanced resilience and decreased vulnerability or nature-based wave impact mitigation. In this paper, the efficacy of hard countermeasures is evaluated through a comprehensive literature review. The recent large-scale tsunami events facilitate the assessment of performance characteristics of countermeasures and related damaging processes by in-situ observations. An overview and comparison of such damages and dependencies are given and new approaches for mitigating tsunami impacts are presented.

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Tsunami Occurrence 1900–2020: A Global Review, with Examples from Indonesia

Jessica A. Reid & Walter D. Mooney

Avoid common mistakes on your manuscript.

1 Introduction

Many coastal communities are exposed to the hazards of marine flooding induced by tsunamis or storm surges resulting in adverse impacts on the coastal ecosystem and built environment. The highly destructive energy of tsunamis can cause large numbers of causalities, damages to infrastructure and affect the livelihood of coastal communities. The threat due to tsunamis intensify since the already densely populated coastal areas are experiencing further population growth as predicted by Neumann et al. ( 2015 ). From 625 million people in 2015, the population growth in low-lying coastal areas is expected to raise by 68–122% resulting in about 1052 to 1388 million people by 2060 (Neumann et al. 2015 ). The Indian Ocean Tsunami (IOT) on the Boxing Day of 2004 was the most destructive recent tsunami with about 230,000 fatalities (Telford et al. 2006 ). Apart from instantaneous destruction, tsunamis can cause medium-term impacts such as the destruction of power plants and long-term impacts such as salt-water intrusion in intensely cultivated delta plains (Villholth and Neupane 2011 ; Nakamura et al. 2017 ), which may be mitigated by hard or soft tsunami countermeasures. Although, in several potentially affected locations, authorities operate  state-of-the-art tsunami early-warning systems, the available time for alerts or the evacuation of the threatened coastal population is often insufficient and the possibility of malfunctions cannot be ruled out (UNDRR 2019 ). For instance, the 2004 IOT reached the town of Banda Aceh in northern Sumatra within 15 min after the earthquake. The danger of a malfunctioning of early-warning systems can aggravate the effects of insufficient additional countermeasures (Strunz et al. 2011 ; Bernard and Titov 2015 ; Samarasekara et al. 2017 ). For instance, in the area around Hikkaduwa City in Sri Lanka, about 47% of the residents do not trust in the functionality of the present early-warning tower since it failed during the 2012 Sumatra tsunami (Samarasekara et al. 2017 ). Furthermore, the damages to crucial infrastructure, e.g. communication, freshwater supply, industry or agriculture, are unavoidable, even through early warnings, if the structures are not designed to resist the impact of a tsunami (Palermo et al. 2011 ). Esteban et al. ( 2013 ) claim that combinations of hard and soft countermeasures (multi-layer approaches) should be promoted in tsunami-prone areas. The present review provides an overview on hard tsunami countermeasures classified as having blocking, steering or slowing character. The main body of the review is divided into three sections as projected in Fig.  1 . In order to limit the extent of the present review, soft countermeasures (e.g. vegetation belts, risk management) are not considered in detail here.

figure 1

Broad classification of structural tsunami countermeasures

2 A brief overview on structural tsunami countermeasures

For protecting coastal settlements from tsunami impact, different mitigation measures are adopted depending on the regional tsunami impact assessment, tsunami awareness and economic capability. Even if existing hard tsunami mitigation measures are often effective against frequently occurring high-energy wave events (Sato 2015 ), recent tsunami events have shown that such countermeasures and their design need to be improved to withstand the impact of extreme tsunami events of unexpected magnitude in some areas. Focusing on tsunamis, such events may be divided into Level 1 and Level 2 tsunamis, where Level 1 events describe tsunamis with a return period of 50–160 years with inundation depths below 10 m while Level 2 tsunamis have a return period of hundreds to thousands of years with inundation depths above 10 m (Shibayama et al. 2013 ). As an example for a Level 2 tsunami, the 2011 Tohoku Tsunami has shown that several of the Japanese defence structures were not designed to withstand the tsunami force that unfolded during the event and that exceeded more recent historical events (Suppasri et al. 2013 ; Takagi and Bricker 2015 ; Goltz and Yamori 2020 ). However, as a highly exposed country, Japan has a long history in tsunami research (Shuto 2019 ). To the best knowledge of the authors, Matuo ( 1934 ) and Takahasi ( 1934 ) were the first to conduct laboratory experiments for examining the effectiveness of seawalls as tsunami mitigation measure. Prior to the Chile Tsunami in 1960, Japan enforced its tsunami countermeasures broadly and during the Chile Tsunami (and the Ise Bay Typhoon in the year before), the installed countermeasures proved their effectiveness. Based on this positive experience the “Chile Tsunami Special Measures Law” was revealed and floodgates and breakwaters were planned as additional countermeasures for preventing tsunami penetration into rivers and bay mouths (Shuto 2019 ). After the 2011 Tohoku Tsunami, structural and non-structural countermeasures have been reinforced again (Strusińska-Correia 2017 ).

In 1933, three months after Japan was exposed to a large tsunami, the Council on Earthquake Disaster Prevention (CEDP) of Japan released ten tsunami countermeasure rules (CEDP 1933 ; Shuto and Fujima 2009 ). In addition to the suggestions of CEDP ( 1933 ), the manuals of the National Oceanic and Atmospheric Administration of the USA (NOAA 2001 ) and UNESCO ( 2011 ) also proposed concepts for mitigation measures. In general, tsunami mitigation measures can be broadly divided into constructional, hard countermeasures, such as dikes and seawalls, and soft countermeasure encompassing nature-based solutions (e.g. coastal afforestation) and those based on the management of the tsunami impact (e.g. evacuation plans, creating public awareness), as projected in Table 1 . CEDP ( 1933 ), NOAA ( 2001 ) and UNESCO ( 2011 ) sometimes use divergent terms that describe basically the same concept or depict a subgroup of each other (e.g. relocation of dwelling houses is a subset of general retreating). Such diverging terminology is addressed in Table 1 . In this paper, only constructional hard countermeasures are considered which have been also discussed by Yamamoto et al. ( 2006 ), Kreibich et al. ( 2009 ) and Strusińska-Correia ( 2017 ), for example.

Common constructional mitigation measures (Fig.  2 ) are designed to avoid or attenuate tsunami impact on the coast and structures, by preventing direct wave impact or dissipating the tsunami impact energy. Today such measures are intended to prevent or mitigate the impact of Level 1 tsunamis. For Level 2 tsunamis, constructional countermeasures may be able to mitigate the tsunami impact to a certain extent or provide additional evacuation time. However, they may not have any mitigating effect at all for Level 2 tsunamis (PARI 2011 ; Shibayama et al. 2013 ; Goltz and Yamori 2020 ). Following UNESCO ( 2011 ) and NOAA ( 2001 ), basically three structural options for preventing/mitigating the risks of damage or loss are available:

Structural (protecting; Fig.  2 a, b, c, e).

Retreating (accommodating, Fig.  2 d).

Non-structural measures.

figure 2

Basic strategies to reduce tsunami risk following NOAA ( 2001 , modified)

The countermeasures presented in Fig.  2 cannot be applied at every potentially threatened coast and, depending on the regional setting, the optimum option needs to be applied by the responsible authorities. The mitigation measures provided by the NOAA and UNESCO can significantly reduce the expectable damage exerted by an extreme coastal hazard, but certain crucial shortcomings need to be considered.

Option a) Blocking with several options an easily be implemented in a developed environment. However, the structures need to be designed to resist the loads of extreme events, and construction schemes need to be carefully planned as they are site-specific. The structures planned under this option should also allow acceptable risk. Further, uncertainties arise from possible amplifications due to reflection and redirecting of waves to unintended directions, which might happen in densely populated locations or in the vicinity of important infrastructures. The space between the protected structure (e.g. a dwelling unit) and the protection measure (e.g. the blocking wall) could function as a stilling basin, probably inducing wave oscillations between them. This effect, consequently, might lead to hydrodynamic forces on the above stated shore-based structures that are higher than for the case without protection measures. Considering Level 2 tsunamis, blocking has often shown to be an unreliable and insufficient countermeasure (e.g. Onishi 2011 ; Takagi and Bricker 2015 ). However, it is subject of research and there is debate as to whether certain measures (i.e. breakwaters) can mitigate flow velocities and heights, at least regionally (e.g. Tomita et al. 2011 ; Aldrich and Sawada 2015 ).

Option b: Avoiding is only realisable if considered during the planning phase of construction and developing an area. Following the guidelines of NOAA ( 2001 ), this option encompasses constructions above inundation levels (in fact on higher ground and/or at greater longer distance from the shore, which is preferable in undeveloped areas) or building over elevated structures such as piers or hardened podiums. However, even if avoiding might be preferred for undeveloped stretches of the coast, it is not applicable after development and therefore ineligible for subsequent enforcements of coastal areas (Cruz 2014 ). Avoiding in the sense of elevated structures can be sufficient for Level 1 tsunamis. For Level 2 tsunamis, the required construction heights will most probably exceed any reasonable cost-benefit ratio and the structural stability would still be questionable.

Option c: Steering requires more space between protected structures and the shoreline. This option may focus the flood along adjoining structures and may also be dangerous to the community due to increased flow velocities. Due to the above stated facts, this option is unsuitable for coastal areas of dense development and is not a suitable option for Level 2 tsunamis.

Option d: Retreating is, in consequence, the ultimate mitigation measure against high-energy wave impact, if the retreat area is chosen with a sufficient distance and/or height to the shoreline. However, retreating is an immense intervention for local population and is only applicable in recently affected areas or areas under initial or planned development. Most countries publish a related setback line for planning of coastal infrastructure that depends on the frequency and magnitude of the coastal hazards (Simpson et al. 2012 ; Coastal Wiki 2020 ). Retreating can avoid the impact of Level 2 tsunamis on populated areas if the distance is chosen sufficiently. However, the retreat of whole existing coastal cities or villages is not a realistic option for most of such populated areas.

Option e: Slowing is viable in areas that are already densely developed, requires lesser space and is economically feasible in most cases. Slowing the wave impact by macro-roughness elements that can act as dissipators can be adopted for reducing the wave run-up and inundation distance. However, the information on the nature, physics and effectiveness behind such dissipators is scanty till date, with no proper design guidelines in place. The main target of countermeasures aiming at slowing is Level 1 tsunamis. However, if designed in sufficient dimensions a mitigating effect may be possible in regard of Level 2 tsunamis.

3 Hard tsunami mitigation measures

3.1 general.

The two mainly adopted constructional mitigation techniques (Table 2 ) are likely the construction of continuous or detached breakwaters (Fig.  3 a), either of submerged or emerged types (blocking/slowing) or massive seawalls (Fig.  3 b, c; Mikami et al. 2015 ). Sea dikes (Fig.  3 d) are usually applied for protecting low-lying areas against flooding. The understanding of hydrodynamic processes on such structures and their mitigation capability are discussed by several authors in detail (e.g. Oshnack et al. 2009 ; Al-Faesly et al. 2012 ; Elchahal et al. 2009 ; Rahman et al. 2014 ; Mikami et al. 2015 ; Chock et al. 2016 ; Chaudhary et al. 2018 ; Ning et al. 2017 ; Sirag 2019 ; Lawrence and Nandasena 2019 ), with some authors questioning their efficacy (e.g. Nateghi et al. 2016 ). Both types of countermeasures have their own functionality, advantages and disadvantages.

figure 3

Schematic setups and applications of breakwater ( a ), seawalls ( b , c ), and sea dikes ( d ), and the connected dominating forces during the impact of the initial tsunami wave

3.2 Breakwaters

Detached breakwaters (Fig.  3 a) have the original purpose to reduce beach erosion. However, the installation of multiple detached breakwaters, each of comparably small dimension, can mitigate wave impact on the shore by wave reflection and energy dissipation. Detached breakwaters are normally designed as low-crested rubble-mound structures. The comparably small height of detached breakwaters allow significant wave overtopping during storm or tsunami events. Beside detached breakwaters, non-detached breakwaters are often applied to mitigate wave impact and create tranquillity (e.g. in harbours). Breakwaters can be divided into two main types: with sloping or vertical-fronts. Another type of breakwaters, floating breakwaters, is only applied in areas of mild wave climates and are not suitable as protection against tsunami impact (Burcharth and Hughes 2003 ), and are not discussed here. The construction of breakwaters is a significant intervention in the water ecology with potentially negative impacts on the environment (Dugan et al. 2011 and references therein). Further resentments arise from the possible negative consequences on tourism (Nateghi et al. 2016 ; Reuters 2018 ).

A comprehensive overview on possible breakwater failures during tsunami impact has been reported by the National Institute for Land and Infrastructure Management, Japan (NILIM 2013a ; Raby et al. 2015 ). A key lesson from the breakwater failures in 2011 was that such failures are connected to scour on the lee side due to wave overtopping. Subsequently, it was recommended to strengthen the lee side of breakwaters by providing proper toe protection and to provide innovative crown shapes for redirecting the flow towards the sea (NILIM 2013b ; Raby et al. 2015 ). Esteban et al. ( 2009 ) conducted physical experiments on the stability of breakwaters and found that the breakwater location is a crucial parameter defining its resisting capability. In deep water, the breakwater is reported to be washed away when hit by a tsunami, while it is able to withstand the impact in shallower waters (Esteban et al. 2008a , b , 2009 ). In contrast, Hanzawa and Matsumoto ( 2012 ) described that breakwaters in shallower water are more damaged by solitary wave impact compared to breakwaters in deeper water. However, Esteban et al. ( 2015a , b and references therein) reported that the most destabilising process occurs during overflow of the breakwater and that the approach of solitary waves as destabilising event is not substantial. Hanzawa and Matsumoto ( 2015 ) have stated that detached breakwaters can reduce the run-up by 30% to 90%, when exposed to solitary waves, and that damaged breakwaters can still reduce the wave-induced pressure by about 40%. As projected in Fig.  3 , detached breakwaters and seawalls are constructed alongshore and are designed to prevent the lee side against overtopping or flooding (Burchath and Hughes 2003 ). In general, detached breakwaters serve as a coastal protection and help to redeposit lost beach substrate. However, the spacing must be carefully planned as it might lead to the generation of rip currents.

3.3 Seawalls, coastal dikes and water gates

Seawalls can either completely protect settlements from tsunami impact or extend the available time for evacuation if they are suitably designed (Samarasekara et al. 2017 ). However, they also could increase the hazard if they fail or allow overtopping (Reuters 2018 ). Obviously, seawalls avoid coastal damages if they are designed as non-overtopping structure, otherwise, they are likely to be destroyed by an extreme-wave event. Furthermore, even if seawalls have a significant potential to protect coastal areas completely against extreme-wave events, their application is expensive (Reuters 2018 ). On the other hand, seawalls can create the impression of false security leading to settlement in dangerous areas or reduced willingness or preparedness to evacuate. Nagethi et al. ( 2016 ) reported that seawalls of 5 m height in Japan lead to forced development in vulnerable areas and can subsequently result in an increased damage during extreme events.

Several designs exist for sea dikes which are mostly constructed from fine-grained materials like sand, silt and clay with surfaces of grass, asphalt, stones or concrete with or without berms (Burcharth and Hughes 2003 ). Most seawalls in Japan, as along the Minami-Sanriku coast, were designed based on the experience from historical tsunamis occurring during the past century. However, considering the regional tsunami spectrum over only short historical periods as a basis for structural countermeasures may be insufficient, as demonstrated by the 2011 Tohoku Tsunami (Kato et al. 2012 ; Goto et al. 2014 ; Strusińska-Correia 2017 ), an event with a recurrence interval of c. 500–800 years (Sawai et al. 2012 ; Goto et al. 2014 ). This example clearly shows that long-term tsunami hazard assessment integrating instrumental, historical and geological data is crucial for designing downstream hard and soft countermeasures (Weiss and Bourgeois 2012 ; Engel et al. 2020 ). Damage to coastal dikes and seawalls is connected to several processes depending on the structural design. On armoured dikes, it was observed that during the overflow scour occurred on their lee side resulting in destabilisation of the armour layer. Scour failure on the leeward toe of coastal dikes and seawalls is reported as the major failure type during the 2011 Tohoku Tsunami on tsunami countermeasures in Japan. The failure mechanism is attributed to wave overtopping and the resulting turbulent flow at the toe. With decreasing flow velocity, the acting pressure on the bed stratum decreases and coinciding with a rise in the pore-pressure gradient, the effective stress within the soil medium is reduced (Tonkin et al. 2003 ; Jayaratne et al. 2015 ). Over time, the armour is detached by the overflow enabling further removal of the dike interior (fine sediment, gravel), leading to a general malfunction of the structure (Kato et al. 2012 ). This type of failure is reported to be independent from additional seaward dike protections with artificial armour blocks like tetrapods.

Japanese seawalls were not designed considering wave overtopping as potential design criteria. Therefore, the leeward toe of the seawalls was not designed to resist destabilising erosional processes, which subsequently lead to overturning or sliding. However, even a failing seawall can possibly reduce the tsunami impact (Guler et al. 2018 ). In summary, Jayaratne et al. ( 2015 ) identified six main failure types of seawalls and sea dikes during field surveys in the aftermath of the 2011 Tohoku Tsunami, which are described in Figs. 4 and 5 . It is stated that seaward toe scour was not often observed during the 2011 Tohoku Tsunami. However, this failure mechanism may occur during the backwash of a tsunami, destabilising the seaward dike armour (Fig.  5 b; Jayaratne et al. 2015 ) as observed by Sundar et al. ( 2014 ) elsewhere. Whilst the tsunami impact on vertical walls/seawalls is broadly investigated (e.g. Asakura et al. 2003 ; Kato et al. 2012 ; Mizutani and Imamura 2000 ), the effect of preceding breakwaters is understudied as pointed out by Hanzawa and Matsumoto ( 2015 ).

figure 4

Dike failure due to overflow-induced erosion. a Scouring on the landward toe, b scouring on the seaward toe, c malfunction of the landward armour and subsequent erosion, d failure of crown armour and subsequent inner erosion (modified and redrawn from Kato et al. 2012 and Jayaratne et al. 2015 )

figure 5

Typical tsunami-induced seawall failure. a Landward scour leads to seawall instability. b Tsunami impact forces lead to overturning. c The backwash current after overtopping leads to seaward overturning (modified and redrawn from Jayaratne et al. 2015 )

3.4 Effectiveness of breakwaters and seawalls

3.4.1 breakwaters.

The mitigation measures prior to the 2011 Tohoku Tsunami in Japan were less effective due to the failures which were mainly caused by scour at the foundations and sliding/overturning due to hydrodynamic forces. However, even the failed structural mitigation measures are reported to have reduced the wave height and delayed the flood impact by several minutes and, thus, still saved lives (PARI 2011 ; Goltz and Yamori 2020 ).

Regarding effectiveness, breakwaters showed divergent performance during the 2011 Tohoku Tsunami. Mikami et al. ( 2015 ) investigated detached breakwaters in front of coastal dikes considering the openings between a pair of breakwaters and were unable to obtain a clear relationship between dike damages and the location of breakwater openings. They described cases in which coastal areas on the lee side of breakwaters were clearly protected compared to areas behind the openings. Subsequent experimental investigations indicated an effective breakwater application with a low ratio of the gap between the breakwaters and its distance from the shore (Mikami et al. 2015 ). However, the world’s largest breakwater in Kamaishi (Japan) failed during the Tohoku Tsunami 2011 resulting in massive damage. Furthermore, the Kamaishi breakwater is suspected to have even increased the tsunami damage due to wave deflection (Onishi 2011 ). Aldrich and Sawada ( 2015 ) concluded that the Kamaishi breakwater was not able to provide any protection to the adjacent town. In contrary, Tomita et al. ( 2011 ) have stated that the breakwater was able to reduce the flow velocity and height significantly and provided additional evacuation time (see also Nagheti et al. 2016 ).

The possibility of increased damage due to insufficiently designed countermeasures of any type (barriers, water gates, tree belts) is also indicated by the tsunami impact at Iwaizumi, Iwate prefecture (Japan) (Ogasawara et al. 2012 ). Takagi and Bricker ( 2015 ) analysed breakwater failures during the 2011 Tohoku Tsunami numerically and revealed that a breakwater of width below 8 m always suffered damage if the wave height exceeded 14 m. Furthermore, no damage was found at breakwaters broader than 14 m if the tsunami height was below 6 m. In contrary to the overall relations, Takagi and Bricker ( 2015 ) were not able to identify significant wave reductions behind the Ishinomaki breakwater (armour block height of 7.00 m above low-water level) and attributed this to the comparably wide openings between the breakwaters. Subsequently, the tsunami was able to enter through these gaps, with an accelerated flow. The case of the Ishinomaki breakwater shows that the use of “permeability” (compare paragraph 5.1) needs to be handled carefully and high attention needs to be paid to the ratio between openings and blocking elements (breakwater elements). However, the numerical investigations were based on 2D simulations with the Delft3D numerical modelling suite. Due to the two-dimensional simulation, vertical velocities and force transfers are neglected. Hence, the simulation suffered from some crucial shortcomings (Bricker et al. 2013 ; Takagi and Bricker 2015 ):

Neglecting vertical motions can result in an enhanced fluid energy.

The shallow nearshore bathymetry enforces the emergence of bores. This process is not resolved by the horizontal 2D model.

For better understanding of the processes acting during the impact of the 2011 Tohoku Tsunami, completely three-dimensional numerical models based on sufficiently fine meshes (as recommended in Takagi and Bricker 2015 ) or even conducted by meshless methods, could be an option if the computational costs can be reduced to permit a practical application.

3.4.2 Seawalls

Based on their post-tsunami surveys, Sundar et al. ( 2014 ) and Sundar and Sannasiraj ( 2018 ) showed that the seawall constructed over a length of about 300 km along the coast of Kerala at the southwest coast of the Indian peninsula was damaged at several locations mainly due to significant overtopping at lower crest elevations, not only during the 2004 IOT but also during storm-wave run-up.

During the 2011 Tohoku Tsunami, the Noda Bay (Noda village, Japan) was protected by two seawall lines (concrete-covered and buttress type) of 10.3 m and 12.0 m height above sea level and a length of 875 m and 380 m, respectively (Ogasawara et al. 2012 ). Ogasawara et al. ( 2012 ) observed that the additional water gates shielding the three rivers crossing Noda village (Myonai, Ube, Izumisawa) were significantly damaged during the tsunami. The seawalls showed differential effectiveness. While the 12.0 m high seawall did not break at all and only the landward slope was eroded, the 10.3 m high seawall did break. The additionally installed natural barrier of pine trees was also not able to withstand the tsunami. Trunks were broken and trees were washed away causing additional damage to many buildings. In Iwaizumi town the present seawall (design tsunami height 13.3 m) and river water gate were overtopped by the tsunami. Furthermore, both, left and right, riverbanks at the water gate were overtopped resulting in large damages to many houses in the lower areas. In contrary, in Fudai village the installed countermeasures showed a good performance during the same tsunami. Even if the present water gate (15.5 m high) was overflowed during the tsunami, the gate did not fail which is addressed to its design: The Fudai water gate and seawall are connected to the adjacent mountains providing additional stability to the structure (Ogasawara et al. 2012 ). In Taro town, an X-shaped seawall system of 10 m height existed before the tsunami of 2011, but its effectiveness is questionable (Yamashita 2003 ; Ogasawara et al. 2012 ; Tachibana 2015 ). Based on in-situ observations, Tachibana ( 2015 ) was unable to finally determine if the seawalls in Taro town even influenced the inundation pattern. Only for the western edge of the seawalls, the flow direction was influenced notably. It is finally concluded that the seawalls were likely not reducing the damage in Taro, overall.

The uncertainties in the design of structural countermeasures against tsunamis are widely reported in literature and the research community agrees that existing design guidelines (e.g. for breakwaters or seawalls) need to be revised based on the observations of recent tsunami events and that additional advanced mitigation techniques (e.g. combined techniques, systematic plantation) are needed in order to be better prepared for future events (Rahman et al. 2014 ; Suppasri et al. 2016 ). In particular, the need for a better understanding of the interaction of tsunamis with countermeasures during the phases of wave impact, flooding and possible backflows has been highlighted (e.g. Palermo and Nistor 2008 ; Macabuag et al. 2018 ). Nevertheless, the 2011 Tohoku Tsunami has led to considerable insights into the functionality and effectiveness of breakwaters as tsunami mitigation measure (e.g. Mimura et al. 2011 ; Takahashi et al. 2014 ; Mikami et al. 2015 ; Raby et al. 2015 ; Sozdinler et al. 2015 ; Suppasri et al. 2016 ).

3.5 Comparison between seawalls and breakwaters

A summary on the advantages and disadvantages of seawalls and offshore detached breakwaters as coastal protection and tsunami mitigation measures are discussed below.

3.5.1 Seawalls

Seawalls act as mitigation measure against flooding and coastal erosion. Their benefits are: Prevention of hinterland erosion, increased security for property from flooding, physical barrier between land and sea, increased perceived security of local people and maintenance of hinterland value. However, crucial shortcomings are adverse impacts on fronting beaches up to a total loss of them, interruption of longshore sediment movement, disturbance of sediment budgets and coastal ecosystems, increased erosion down drift (terminal scour), and freezing the coast and thus preventing its response to recent and future sea-level rise. The recommended usage of seawalls is to protect high-value hinterland development and to increase and protect amenity usage where other solutions are not suitable. Questions remain, however, regarding overtopping and run-up particularly during tsunamis.

3.5.2 Offshore breakwaters

The benefits of construction of offshore breakwaters as mitigation measures against tsunamis are reduction in wave activity received at the coast, increased sedimentation and beach formation, reduction of flood risk due to wave overtopping at the coast, reduction in sediment loss through rip-cell activity, formation of new “reef” ecosystems and increased biodiversity. Whereas the problems associated with offshore breakwaters are possible deflection and modification of longshore currents, high construction and maintenance costs, possible scour problems through gaps in segmented breakwaters and retention of sediment with corresponding increased erosion elsewhere along the coast. The usage of offshore breakwaters is recommended in: Coastal areas experiencing erosion because of wave activity and excessive sediment loss by shore normal currents, and where sediment build up would enhance coastal resilience.

4 Integrated and combined approaches

4.1 integrated mitigation measures.

Beside structures designed solely as mitigation measure against coastal erosion or tsunamis, they can also be integrated as part of infrastructure constructions. At the coast of Banda Aceh (Indonesia), it is proposed to construct a circuit road (Banda Aceh Outer Ring Road; BORR) intended to also act as tsunami mitigation measure (Syamsidik et al. 2019 ). During the 2004 IOT, the maximum tsunami height in Banda Aceh is estimated to be 15 m (Lavigne et al. 2009 ) and its impact resulted in a death toll of about 26,000 (Doocy et al. 2007 ). The BORR is planned to be constructed as an elevated road (3 m) as shown in Fig.  6 to act as a mitigation measure and shall be located behind the shoreline in Banda Aceh. Syamsidik et al. ( 2019 ) showed that the construction of the BORR may reduce the area of inundation by 8–22%, depending on the tsunami intensity, but also point to the possibility of damage (e.g. due to breaching) which needs to be examined further.

figure 6

Intended course of the elevated road in Banda Aceh. Left: Consequences of the 2004 IOT in Banda Aceh (Satellite data composite from Maxar Technologies accessed through Google Earth Pro, vers. 7.3.4.8248)

Samarasekara et al. ( 2017 ) discussed the reinforcement of an existing railway embankment as an additional tsunami countermeasure in the two coastal villages Dimbuldooa and Wenamulla in Sri Lanka. While they clearly found a tsunami-mitigating effect by enhancing the present rail embankments, the expected benefit (protected goods) seems to not compensate the anticipated costs.

4.2 Alternative approaches

4.2.1 multi-layer approach.

Several studies address multi-layer approaches (sometimes referred to as multi-layer safety) regarding tsunami impact mitigation (Fig.  7 ). This approach has received greater interest after the 2011 Tohoku Tsunami (Tsimopoulou et al. 2015 ; Samarasekara et al. 2017 ). Both studies referred to the National Water Plan of the Netherlands 2009–2015, which is explained in detail by Hoss et al. ( 2011 ). The Dutch multi-layer approach encompasses three main components:

Layer 1 as prevention that encompasses all measures focussing on preventing floods (e.g. seawalls).

Layer 2 as spatial solution addresses the spatial planning of areas and buildings in flood threated areas.

Layer 3 as emergency management that focusses on the hazard management in terms of hazard awareness among the population, evacuation plans or early-warning systems (Hoss et al. 2011 ; Esteban et al. 2013 ).

figure 7

A schematic view of the multi-layer approach. Layer 1: Prevention (e.g. by offshore breakwaters or seawalls). Layer 2: Spatial planning (e.g. creating retention areas or lifted structures with porous structures). Layer 3: Management (e.g. evacuation plans, early-warning systems) (modified and redrawn from Tsimopoulou et al. 2013 , 2015 )

The application of multi-layer or prioritisation of a particular layer depends on the region and country. In developing countries, single-mitigation measures are often preferred since they are economically more feasible. In developed countries on the other hand, more financial resources are available and, additionally, the assets at risk are economically more valuable. This leads to more comprehensive mitigation measures, for instance, in Japan (Esteban et al. 2013 ). In general, multi-layer approaches are considered as a parallel system instead of a serial system. This means, if one of the three layers fails, the remaining layers still provide mitigation (Jongejan et al. 2012 ; Tsimopoulou et al. 2013 ). However, in the case of tsunami mitigation, this is not entirely valid since a failure of Layer 1 (e.g. a seawall) may cause additional damage. Tsimopoulou et al. ( 2013 ) illustrated this by referring to a dike-ring area in the Netherlands. If the probability of a failure of an evacuation plan (Layer 3) is higher than the possibility for a dike failure (Layer 1), the synergy between Layer 1 and Layer 3 diminishes and the costs for establishing the evacuation plan may surpass its expected benefit (Tsimopoulou et al. 2013 ). Furthermore, in the case of Layers 2 and 3 a threshold for the accepted damage in case of a hazard needs to be defined, determining the boundary conditions for these layers (e.g. settlement retreat from the coast; Layer 2) (Tsimopoulou et al. 2013 ).

In Tohoku region, a multi-layer mitigation approach already existed prior to the 2011 tsunami. However, it is not clear to what extent the approach was strategically planned and coordinated by local authorities or if it was implemented rather unintentionally/accidentally. In fact, the system failed in 2011 starting from the breakdown of most of the Level 1 measures (breakwaters, seawalls). On Layer 2, the early-warning system did respond and provided warning only three minutes after the earthquake, but the local emergency plans were not prepared for such an intense tsunami. Even some evacuation buildings were partially overtopped, while, in the low-lying areas people did not reach them in time (Tsimopoulou et al. 2013 ). Based on the analysis of Tsimopoulou et al. ( 2015 ) in Tohoku, it is recommended to elaborate risk-based multi-layer approaches based on damage and casualty thresholds determining the point of “failure” of a layer. Such an approach would provide additional protection in a multi-layer system. Furthermore, the authors emphasised the importance of tsunami awareness among the population for a functional multi-layer safety approach, based on a case study in the city of Rikuzentakata (Iwate Prefecture, Japan; Tsimopoulou et al. 2015 ).

4.2.2 Channels and dug pools

The Buckingham Canal along the city of Chennai situated along the southeast of India is a 30 m wide, 10 m deep and 310 km long channel flowing at a distance of 1 to 2 km parallel to the shoreline. In the area between Buckingham Canal and the shoreline, hamlets inhabited by several thousands of fishermen are located. During the 2004 IOT, the canal preserved elevated patches in this area from tsunami damage since the tsunami run-up approached and filled the canal at first, which then acted as an additional buffer zone (Rao 2005 ). The canal regulated the run-up back to the sea within 10 to 15 min. From this observation, Rao ( 2005 ) suggested investigating the influence of channels on tsunami run-up scientifically by considering further geomorphologic features and coastal inlets. Furthermore, Dao et al. ( 2013 ), Usman et al. ( 2014 ) and Rahman et al. ( 2017 ) investigated the application of channels and depressions as tsunami countermeasures both experimentally and numerically.

Dao et al. ( 2013 ) investigated the Kita-Teizan Canal in Sendai (Japan) numerically, which is assumed to have mitigated the impact of the 2011 Tohoku Tsunami significantly (Tokida and Tanimoto 2012 ). The Kita-Teizan Canal is a 9 km long canal running parallel to the shoreline at a distance of about 300 m to 400 m and is 40 m wide and 2 m deep. By several setups with and without the canal as well as different canal dimensions, the canal is found to be capable of reducing the tsunami energy significantly and its effectiveness would increase by greater width and depth. The canal effectiveness in terms of reducing tsunami overland flow velocity is reported to vary from about 13% to 20% during the 2011 Tohoku Tsunami. By applying fragility curves (Gokon et al. 2011 ) for structures, Dao et al. ( 2013 ) furthermore assumed that the canal’s contribution corresponds to a reduction of structural damage of 3–4%.

Rahman et al. ( 2017 ) studied different configurations of canal dimensions (width, depth) and additional countermeasures (dunes) for tsunami mitigation and identified a combined approach to be most promising. In general, the canal of largest dimensions (depth, width) showed the best mitigation performance. Flat but wide canals showed high wave reflections. However, all tested canals had a considerable mitigation effect in terms of reduced tsunami velocity and delayed tsunami flooding. Even though shore-parallel canals were capable to reduce the energy of the tsunami impact, there was no influence on inundation depth. The combination of sand dunes and a canal reduced both inundation depth and flow velocity (Rahman et al. 2017 ). Further studies on canal geometries as well as combinations of canals and traditional countermeasures for tsunami mitigation were suggested.

The mitigation function of canals, channels or dug pools was accidentally identified and also today such structures are not planned by intention. However, based on the experiences of the 2004 IOT and 2001 Tohoku Tsunami, the interest in understanding the associated hydrodynamic processes and elaborating quantifiable mitigation potentials of such structures is increasing.

4.2.3 Vertical evacuation

Structures for vertical evacuation could be considered both as hard and as soft tsunami countermeasures. However, in areas without natural high grounds as evacuation space, the construction of artificial structures is an option for shortening evacuation distances. These structures might further be divided into those originally designed as evacuation shelters or those constructed for other purposes (e.g. parking garages, hotels, etc.). However, if existing buildings are assigned as evacuation location, their stability against tsunami impact and the accessibility needs to be ensured (Goltz and Yamori 2020 ). The construction of elevated or high grounds as evacuation sites is another option for designed vertical evacuation space. Such high grounds are suggested by the Federal Emergency Management Agency of the US (FEMA 2019 ) as comparably cost-effective structure for vertical evacuation compared to stand-alone structures or buildings. A provision of bottom clearance to the building by using continuous stilts was found to reduce the pressure impulse of the order of 20% to 30% through numerical and experimental investigations (Sannasiraj and Yeh 2011 ). However, beyond a certain elevation extent, the clearance may not yield further reduction of the impact.

5 Future directions

5.1 use of permeability.

Mitigation structures of staggered non-continuous configurations lead to a reduction in the hydrostatic and hydrodynamic stresses during the initial wave impact, ongoing wave penetration and backflow. Recent research proves the linkage between hydrodynamic loads of tsunamis and the permeability of coastal structures, e.g. in terms of opened or closed windows. In all of these studies (e.g. Thusyanthan and Madabhushi 2008 ; Wilson et al. 2009 ; Lukkunaprasit et al. 2009 ; Triatmadja and Nurhasanah 2012 ), authors confirmed the effect of solid or elastic structures in combination with openings that permit free flow and provide energy dissipation. Lukkunaprasit et al. ( 2009 ), and independently Wilson et al. ( 2009 ), found that opening a structure of 25% and 50% reduces the hydrodynamic force by 15–25% and 30–40%, respectively. Such low or no-resistance mitigation measures (which are based on the idea of least resistance) should be based on openings in buildings as large as possible, or the implementation of weak and non-stability-supporting elements in the building in order to provide a calculated path for the flow that does not affect the stability of the building (ASCE 2017 ). An increase in the permeability of coastal buildings increases their stability, but the buildings will still be affected by flooding, and the final success is highly depending on the existing structure strength. Increasing the permeability of existing structures (e.g. open windows, doors, etc.) is a reasonable approach in order to mitigate the worst case and should be the last mitigation option since certain types of mid-term and long-term damages (in particular regarding crucial infrastructure or flood-caused diseases) may not be prevented. For tsunami-prone areas, it is strongly recommended to leave sufficient space between ground level and the floor level of dwelling units. For critical installations, such as power plants, adequate caution should be taken by locating the sensitive components at high grounds to avoid any tsunami flooding.

5.2 Slowing by artificial elements (buffer blocks)

As explained in the previous paragraph, the use of permeability in tsunami mitigation measures is a promising approach. The main purpose of such constructions is to dissipate the impact energy and, therefore, also to reduce tsunami height. Permeable structures generate additional turbulences in the flow field, while they are not designed to resist the full wave impact energy. The dissipation results from the flow through the elements on both sides and over its top. Basically, the concept is comparable to the increased roughness provided by vegetation which is intensely studied (e.g. Shuto 1987 ; Kathiresan and Rajendran 2005 , 2006 ; Olwig et al. 2007 ; Iverson and Prasad 2008 ; Tanaka 2007 , 2010 ; Baird and Kerr 2008 ; Yanagisawa et al. 2009 ; Sundar et al. 2011 ; Noarayanan et al. 2012 , 2013 ; Strusińska-Correia et al. 2013 ; Nateghi et al. 2016 ).

Until now, and related to mitigating storm surges, buffer blocks have been adopted as roughness elements over dikes and as space-saving reinforcement measure to existing dikes in order to enhance energy dissipation of overflow as shown in Fig.  8 (Oumeraci 2009 ; Hunt-Raby et al. 2010 ; EurOtop 2018 ).

figure 8

View on small buffer blocks attached to coastal dikes (foreground) and large buffer blocks as mitigation measure against storm waves (photograph by Schüttrumpf, 2003)

Although such buffer blocks are not applied as countermeasure against tsunamis so far, their general applicability as tsunami mitigation measure is discussed by several authors (e.g. Oumeraci 2009 ; Thorenz and Blum 2011 ; Goseberg 2011 , 2013 ; Rahman et al. 2014 ; Capel 2015 ). In his flume experiments, Goseberg ( 2011 , 2013 ) showed that macro-roughness elements have a significant effect on the run-up height of non-breaking long waves mainly depending on element configuration (aligned, staggered) and wave direction. Goseberg ( 2011 , 2013 ) focussed on the run-up reduction due to the presence of macro-roughness elements as buildings (referred to as coastal urban settlements), which are not fully submerged during the run-up, but did not consider the force reduction behind the macro-roughness elements (Fig.  9 ). The run-up reduction was mainly addressed towards momentum exchanges within the wave during the overflow of the macro-roughness elements, leading to the generation of higher turbulences. These preliminary findings support the use of buffer blocks for tsunami mitigation (Goseberg 2011 , 2013 ). Similarly, Giridhar and Muni Reddy ( 2015 ) investigated the effect of different shapes of buffer blocks (rectangular, semi-circular, trapezoidal) installed over sloped structures to assess their effectiveness in the reduction of wave run-up and reflection. Rahman et al. ( 2014 , 2017 ) investigated the performance of continuous seawalls of two different heights and one perforated seawall regarding wave impact attenuation. A dam-break setup and a load cell for investigating the bore impact were used. The load cell was installed behind the seawall to gain insights into the mitigation characteristics of these structures (Fig.  9 ). For continuous seawalls, the performance of higher seawalls built closer to a structure of interest led to the highest impact-force reduction on the structure of interest. Nevertheless, the perforated seawall exhibited a reduction in wave height and force of about 35% compared to no protection. Furthermore, the perforated seawall allows overtopping and backflow into the sea, resulting in decreased forces acting on nearby structures. The perforated seawall had the same total height as the continuous sea wall (8 cm) but is divided into a lower continuous section (3.8 cm height) and an upper discontinuous section (elements of 4.2 cm height). This results in material savings to an extent of about 25% with good attenuation characteristics.

figure 9

Simplified sketch of the laboratory experiments of Goseberg ( 2011 , 2013 ; redrawn) and Rahman et al. ( 2014 ; redrawn). Goseberg ( 2013 ) shows that buffer elements can reduce the run-up significantly. In Rahman et al. ( 2014 ) the continuous seawall leads to a force reduction of 41% in the experiments, while the perforated seawall reduces the impact force by 35% of the case without any protection of the load cell

5.3 Recurved seawalls

Recurved seawalls (also recurved parapet walls) or breakwaters are occasionally applied as storm-wave countermeasure (Fig.  10 ). Their application as tsunami countermeasure is not common and, to the best knowledge of the authors, no publication addressing tsunamis is available beside a patent application of Igawa ( 2012 , Fig.  10 c).

figure 10

Recurved seawalls on a mounted breakwater ( a ) and as a coastal seawall ( b ). c Approach of Igawa ( 2012 ) which aims at more controlled flow redirection

Anand et al. ( 2011 ) compared the hydrodynamic characteristics of seawall profiles and found the lowest reflections for circular cum parabola shapes (CPS) followed by Galveston wall shapes (GS). The CPS shape mentioned in the patent of Weber ( 1934 ) consists of a smooth parabola to gently guide the incoming waves to the quadrant circle at the top that redirects the waves back to the seaside. The Galveston wall shape (GS) consisting of two radii of curvature has been earlier adopted as a seawall at Galveston, Texas, USA (Anand et al. 2011 ).

Molines et al. ( 2019 , 2020 ) investigated mound breakwaters enforced with parapet walls regarding wave forces by flume experiments and numerical simulations using OpenFOAM. They have reported that the horizontal wave forces increase by a factor of 2 compared to standard vertical wall breakwaters. However, they showed that curved crowns are able to reduce wave overtopping significantly until the impact discharge is too high. Then, no further significant influence of the curved parapet on wave overtopping was observed.

Castellino et al. ( 2018 ) conducted two-dimensional numerical investigations on the interactions between curved seawalls and impulsive forces. It was shown that the hydrodynamic pressures due to non-breaking waves increase significantly on a larger portion of the fully submerged recurved parapet wall. A high influence on the impact forces is attributed to the opening angle of the curve. Investigations on the correlation between wave period and wave impact on the curved seawall crest show that the wave load increases with wave steepness (Castellino et al. 2018 ).

Martinelli et al. (2018) investigated the loads  of non-breaking waves on a recurved parapet with different exit angles. They reported “partially recurved parapets” with exit angles of 60° to be a good compromise between the reduction of forces and overtopping. Ravindar et al. ( 2019 ) studied the characteristics of wave impact on vertical walls with recurve in large scale and analyse the variation of impact pressure. Stagonas et al. ( 2020 ) compared the impact forces on three types of recurves based on large-scale experiments and found that the mean of the largest peak force increases with an increasing angle of curvature. Recently, Ravindar and Sriram ( 2021 ) reported on the influence of three recurved and plain parapets on the top of vertical walls. It was concluded that large parapets seem to be most effective in the reduction of forces for higher waves compared to other parapet types.

5.4 Large tsunami barrier

Scheel ( 2014a , b , c ) proposed a novel tsunami countermeasure based on shoaling processes and preventing steepening of waves in the nearshore (Fig.  11 ). The idea is based on reflecting the wave motion by a submerged vertical wall in front of the shoreline. The vertical wall needs to be placed up to several tens of kilometres offshore at a depth between 20 and 200 m (Scheel 2014b ) or 50 m and 500 m (Scheel 2014a ), respectively. The crest is equipped with an extending wall of 6 m to 8 m on top of the vertical wall. To avoid wave reflections, Scheel ( 2014a , b , c ) suggested a slight inclination in the wall, irregular shapes or optimised surface roughness for introducing wave distraction to the reflected wave. Scheel ( 2014a , b , c ) acknowledged the large financial and material demands of this measure and proposed to reclaim the space between wall and shoreline as additional land. This type of measure could be considered for protecting crucial installations that cannot easily be protected or relocated, or which pose a hazard themselves in the event of a collapse, such as nuclear power plants. However, scour could be a serious problem if not properly addressed. As another option, Scheel ( 2014a , b , c ) recommended constructing not one single deep vertical wall but to implement several smaller walls for reducing the costs (Fig.  11 c).

figure 11

Tsunami countermeasure after Scheel ( 2014a , b , c ). a No tsunami countermeasures. b Tsunami barrier in large distance to the high-tide line (dashed line). c Fragmentation of the barrier into several sub-terraces in order to save material and costs (redrawn and extended after Scheel 2014a , b , c )

Furthermore, Scheel ( 2014b ) suggested combining the tsunami countermeasure with hydro-power plants. Here, the vertical wall would be equipped with turbines driven by the tidal current. Alternatively, the space between the vertical walls is proposed to be used for fish farming (Scheel 2014a , b , c ). A numerical study by Elsafti et al. ( 2017 ) revealed that such a barrier is effective in reducing the tsunami energy significantly before reaching the shoreline. However, at the wall, the run-up height increases more than twice the height of the approaching tsunami, and the influence of the face-roughness of the barrier has only minor influence on wave run-up and reflection. The approach of Scheel ( 2014a , b , c ) seems to have not been validated or tested physically so far. Furthermore, the construction of such countermeasures would require substantial fundamental research not only on the hydrodynamic characteristics and design but also on the construction sequence and procedure, which might require further innovations (Scheel 2014b ). A further adverse effect would be imposed on the ecology of shallow marine environments around and behind these barriers.

6 Discussion

The review revealed that a range of hard countermeasures for mitigating tsunami impact exist, but that they also need a critical evaluation prior to installation. In most cases, the local environmental, social and financial factors determine the technique to be adopted. Hard structural measures like dykes, seawalls or breakwaters have high construction costs and can provide a false feeling of security which might even increase the structural damage and fatalities if they fail during tsunami impact. Due to the known disadvantages of seawalls and dykes (Sect.  3.5 ), further developments in the field of structural tsunami countermeasures are necessary, some of which are summarised in Table  3 .

Despite breakwaters and seawalls do have disadvantages, a re-design of such structures (e.g. by raising their crest elevation or applying recurved parapets) can, at least marginally, increase their efficacy during the tsunami ingress. On the other hand, physical and numerical investigations show that hydrodynamic forces acting on the walls increase significantly due to the recurved parapet. Based on the high hydrodynamic energy of tsunamis, it is questionable how reliable such recurved seawalls in dimensions sufficiently high for large tsunamis would be (i.e. if they are reasonable applicable for Level 2 tsunamis). Furthermore, this would involve a huge financial investment; a decision would depend on the local frequency–magnitude pattern of tsunamis, the value of assets, as outlined by Stein and Stein ( 2013 ), and of course the vulnerability of the coastal population. In any case should their dimensions be large enough to reduce the tsunami inundation levels. However, with regard to the perennial problems of coastal erosion, today's breakwaters and seawalls may serve their purpose.

The application of artificial slowing elements (buffer blocks) could be effective as they are easier to install and can serve as buffers in reducing the tsunami inundation. Their general applicability is already proven against storm waves along the coast of  Norderney Island, Germany (Schüttrumpf et al. 2002 ). Such buffer blocks might also be highly useful as (supportive) countermeasure for tsunamis if their dimensions are derived from detailed scientific investigations. Extended investigations are also necessary to determine whether the buffer block approach may also be suitable for Level 2 tsunamis.

Recently integrated tsunami mitigation measures are considered as a practical solution. The reinforcement of existing or construction of combined structures might be a useful alternative especially in regions where financial resources for countermeasures are limited. Especially elevated roads or railway embankments can be suitable options, as in the case of Banda Aceh. Channels and dug pools might also be considered as further integrated mitigation measures. Recent investigations show that channels and topographic depressions are capable to mitigate tsunami run-up and, depending on their arrangement, to steer the backflow to the open sea in a more controlled way. The application of such integrated countermeasures needs to be investigated further and more systematically in terms of sufficient dimensions, integration into the coastal ecosystems and tourism, and economic questions. Nevertheless, the application of channels/topographic depressions would always divide the coastal area into a more and less protected part. Therefore, their application might be combined with a first defence line of breakwaters, seawalls, buffer blocks or vegetation belts. The separation of the coastal area into more and less protected parts, needs to be combined with specifically adapted land use in the flood-prone areas.

The combination of topographic depressions and hard structural countermeasures results in multi-layer approaches. If Layer 1 (e.g. seawalls) fails, Layer 2 (e.g. topographic depressions) will still provide attenuation. However, the failure of the first defence line would lead to additional damage in the area between seawall and depression, while Layer 2 (topographic depression) would prevent areas on its lee side from higher damages. Herein, Layer 3 (emergency management) would act in combination with Layer 2 since the functionality of Layer 2 would highly depend on timely evacuation of the area between Layer 1 (seawall) and 2 (depression). However, as stated by Tsimopoulou et al. ( 2013 ), the Dutch multi-layer approach has to be adjusted in order to be suitable for combating other types of high-energy wave impacts such as tsunamis. A great deal of research on this topic is recommended since none of the presented mitigation measures can serve as an overall valid and completely successful mitigation technique on its own. Furthermore, multi-layer approaches can also be a promising option regarding Level 2 tsunamis if Layers 1 and 2 are considered as “failable” layers which provide additional time for evacuation.

Completely novel approaches of tsunami countermeasures are rare, which might be due to the complexity of the hydrodynamic processes and the low predictability of tsunami occurrence and intensity. Connected to the unpredictability of tsunamis, test applications of novel approaches are not easy to implement. Test areas need to be selected carefully. Whether the selected area will be affected by a tsunami within a manageable period is not predictable. On the other hand, if the effectiveness of such measures cannot be fully proven by numerical or experimental investigations, a remnant risk is associated to the application in populated areas. This might hamper the development and implementation of new approaches.

As stated earlier, a novel tsunami barrier which is based on the idea of preventing a tsunami from shoaling and reducing its impact energy and run-up was proposed by Scheel ( 2014a , b , c ) and Elsafti et al. ( 2017 ). It is at concept stage and substantial research through experimental and numerical investigations as well as trials in the field are required to prove its efficacy. A large amount of economic, material and labour resources would be needed for construction and the (most probably very high) ecological impact is unforeseeable.

The available literature mostly concentrates on failed countermeasures. Naturally, resisting and successful countermeasures do not receive as much attention. Therefore, we encourage to include also successful tsunami countermeasures in future research studies in order to raise datasets showing dependencies between countermeasure type, design and dimensions, and the tsunami impact. Such data would enable authorities and other persons in charge at affected coasts to better evaluate their hazard management. Furthermore, such reviews would highly benefit from preferably comprehensive datasets encompassing data for the tsunami intensity and properties, countermeasure design (dimensions, material, vegetation type, soil type, etc.) and coastal topography and bathymetry. Elaborating such datasets and corresponding correlations would help to increase the planning security at threatened coasts.

As further support to tsunami mitigation, researchers started to utilise tsunami deposits for reconstructing the energy of palaeotsunamis, over the last three decades (Etienne et al. 2011 ; Engel and May 2012 ; Vött et al. 2013 ; Sugawara et al. 2014 ; Costa and Andrade 2020 ; Oetjen et al. 2020 ). Knowledge on palaeotsunamis can help to successfully improve regional specific tsunami countermeasure programmes since they allow to extend the scale of known events to several thousands of years and lead, subsequently, to an increased prepardeness and awareness of possible tsunami events and their energy and flooding potential.

This review shows that tsunami mitigation measures are a broad research field of high interest. Recent destructive tsunamis intensified the research interest further since tsunami hazards can result in enormous damages and fatalities. Past tsunamis show that it is dangerous to base tsunami mitigation on only one layer since its failure highly likely results in disastrous hazards. For establishing new approaches and enhancing existing countermeasures, broad datasets can support researchers in adjusting mitigation measures to specific regional areas, e.g. in terms of land use and topography and expectable tsunami impacts. This requires close collaborations between different scientific disciplines (e.g. engineers, geologists, geographers, sociologists) since knowledge on construction, seismology, palaeotsunamis, and regional social-economic and cultural properties highly determine the success of local mitigation measures and connected management plans.

Regarding the hard countermeasures only, a combination of blocking (e.g. seawalls), slowing (e.g. vegetation, buffer blocks) and steering structures (e.g. channels, topographic depressions) that considers long-term tsunami hazard, people and assets at risk, financial resources and the coastal configuration at a local scale is considered most promising. However, it should always be considered that tsunami mitigation measures as a whole can never provide a safety level of 100%, as there is an upper limit of mitigation investment depending on the assets at risk (Stein and Stein 2013 ) and the magnitude of future tsunamis is still difficult to assess.

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Acknowledgements

This contribution received funding by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) for the project “Modelling tsunami-induced coarse-clast transport—combination of physical experiments, advanced numerical modelling and field observations” (SCHU 1054/7-1, EN 977/3-1). Further, we would like to acknowledge funding from the Department of Science & Technology, India, Grant No. DST/CCP/CoE/141/2018C under SPLICE—Climate Change Programme.

Open Access funding enabled and organized by Projekt DEAL. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.

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Oetjen, J., Sundar, V., Venkatachalam, S. et al. A comprehensive review on structural tsunami countermeasures. Nat Hazards 113 , 1419–1449 (2022). https://doi.org/10.1007/s11069-022-05367-y

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The Asahi Shimbun

Dashcams record stunning footage of Noto quake and tsunami

By RYOMA KOMIYAMA/ Staff Writer

February 22, 2024 at 07:00 JST

Photo/Illutration

Horrifying scenes of the ground rolling, clouds of dust engulfing collapsing homes and cars being swept away by dark water were captured by dashboard cameras during the Jan. 1 Noto Peninsula earthquake and tsunami.

The dashcams were installed aboard a shuttle van belonging to Choju-en, an intensive care home for the elderly, located in the Horyu-machi Kasugano district of Suzu, Ishikawa Prefecture.

The van, containing six elderly passengers, was parked in a residential area some 200 meters from the coast when the quake struck.

The driver was Kumiko Inagawa, a 59-year-old employee. She had set out from Choju-en, which was on higher ground, to drive the six day-service users back to their homes at around 4 p.m. on New Year’s Day.

She had just stepped out of the van and was chatting with a user's family member when the violent tremors hit.

The dashcams recorded the front, rear and interior of the van. The timestamp on the footage was 4:10:14 p.m.

The cameras trained outside the van recorded the ground rolling like Jell-O, as houses and shops crumbled one after another into clouds of brown dust.

A man nearby staggered and fell to the ground, unable to withstand the shocks.

Inside the van, the passengers rocked wildly from side to side--one of the women began shrieking.

The shaking lasted for at least a minute and a half.

“I had never experienced shocks like that,” Inagawa said. “I took it to heart that when you’ve felt a big quake, you should get to higher ground before anything else.”

However, that was easier said than done.

The road in front of the van was blocked by collapsed houses. Behind the van, a large bulge had risen between the road to higher ground and a bridge connecting to it.

Unable to take the van, Inagawa and the passengers decided to flee for safety on foot. They would try to make it back to Choju-en.

The video recordings ended when the van’s engine turned off seven minutes after the quake.

Inagawa supported two of the elderly passengers as they walked, one under each of her arms. She asked a passing minitruck to carry one of the two. Another passenger took flight with her sister, who happened to be nearby. Yet another was carried piggyback by a local resident.

Remarkably, everyone made it safely to Choju-en before the tsunami struck.

Back at the abandoned van, the dashcams suddenly flickered to life and resumed recording at 4:47:37 p.m. The filming was likely triggered automatically by the impact of the tsunami hitting the vehicle.

The 15-second footage recorded dark, muddy water flowing into windows and hallways of houses and washing away cars and rubble.

Choju-en officials said the van was later found close to where it had been left--but rotated back to front.

A contractor who salvaged the van from the site to scrap it discovered the dashcam footage inside and delivered it to the elder care facility on Jan. 30, the officials said.

“The footage is a valuable piece of testimony to what the quake and tsunami were like,” said Yasutaka Kodo, a counselor to Choju-kai, the social welfare corporation that operates Choju-en.

Kodo added: “Help from local residents is extremely important for evacuation in a depopulated community with many elderly people. I hope the footage will be used in efforts to help mitigate future disasters.”

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Newsweek

Tsunami 8,000 Years Ago May Have Devastated Stone Age Community—Study

A prehistoric tsunami that occurred more than 8,000 years ago may have devastated Stone Age coastal communities, a study has revealed.

The event, known as the Storegga tsunami, affected a large area of northern Europe and beyond, leaving traces in the form of sediment deposits in areas as distant as northern Norway, northern England, western Scotland, the Scottish Shetland Islands and eastern Greenland.

The tsunami, triggered by a huge submarine landslide off the coast of western Norway between 8,120 and 8,175 years ago, generated waves of up to around 40 feet in height along the Norwegian coast and 10-20 feet in mainland Britain. In the Shetland Islands—an archipelago located over 100 miles north of the Scottish mainland—the waves are thought to have reached more than 65 feet in height.

The tsunami event coincided with an apparent large population decline in the coastal populations living nomadic, hunter-gatherer lifestyles in northern Britain during the Mesolithic (or Middle Stone Age ) period.

But despite extensive evidence of the tsunami over a wide area of the North Sea, there is a lack of research quantifying the impact of the event on Mesolithic communities in the region. In fact, very little is known about the impacts of ancient tsunamis on contemporary populations in general.

In the study, published in the Journal of Quaternary Science , researchers attempted to assess what impact the event might have had on populations living at the time.

"This is one of the first attempts to directly link the Storegga tsunami to its effects on Mesolithic people," Patrick Sharrocks, an author of the study affiliated with the University of York and University of Leeds in England, told Newsweek .

For the study, Sharrocks and his colleague Jon Hill—also affiliated with the University of York—used a supercomputer to model the Storegga tsunami and then assessed its potential impacts on the human populations living in the region.

Specifically, the researchers examined the impacts of the tsunami on the coast of what is now Northumberland, a region in the far northeast of England bordering Scotland.

The Northumberland coast is home to Howick, one of Britain's most important Mesolithic sites. The site is located within a resource-rich location on a river estuary a few hundred feet from the coast. Evidence of extensive hazelnut collecting suggests the site was occupied at least during the autumn and winter, although year-round living may also have been possible.

Humans appear to have inhabited Howick before the Storegga tsunami, but it was likely to have been occupied when the event occurred. Previous research has uncovered a sediment deposit close to the site that the tsunami may have produced. This deposit may represent a rare piece of evidence that could help to shed light on the direct impacts of the tsunami on Mesolithic people—but its origin is uncertain.

In the latest study, the researchers found that the tsunami could have inundated the sediment deposit site—and thus, could have formed the deposit—but only if the wave struck the coast at high tide.

The researchers also found that the impact on the Mesolithic people in the area would likely have been "significant" with the models showing high mortality rates at Howick—a direct result of the tsunami. At Howick, mortality estimates varied but reached up to 100 percent for prehistoric humans within the resource-rich intertidal zone.

"The mortality estimations for the intertidal zone were higher than we expected with the implication that the impacts on Mesolithic people could have potentially been severe," Sharrocks said.

The models suggest the tsunami would have inundated a large area, leading to the loss of critical resources, such as hazelnuts, before the winter months. Aside from the direct impacts of the tsunami on Mesolithic peoples, this loss of resources would have significantly reduced their ability to survive the harsher months.

The combined effects of the tsunami would also likely have been replicated throughout coastal populations in northern Britain, according to the researchers.

The results suggest "a potentially significant local and regional impact, which could have contributed to the suggested population decline at the time," Sharrocks said. "The lack of previous tsunamis in living memory would have meant warning signs such as a receding ocean would likely have been ignored, as was the case in some of the worst affected areas in the 2004 Indian Ocean tsunami."

The researchers said the framework of the study could be used to investigate other locations affected by the Storegga event or other ancient tsunamis to understand the impact on past humans. Studies of past events such as this could also help us to better understand the potential effects of future tsunamis.

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Stock image: An illustration of a tsunami wave. A study has revealed new insights into a prehistoric tsunami that occurred more than 8,000 years ago.

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    1 Case Study I: Tsunami Hazards in the Indian Ocean The eastern Indian Ocean basin is a region of high earthquake and volcanic activity, so it should come as no surprise that tsunamis pose a threat to the Indian Ocean basin.

  15. The Science of tsunamis

    The word 'tsunami' brings immediately to mind the havoc that can be wrought by these uniquely powerful waves. The tsunamis we hear about most often are caused by undersea earthquakes, and the ...

  16. PDF TSUNAMI: Japan Tsunami of 2011

    On March 11, 2011, a 9.1 earthquake occurred near Japan, shifting the earth 200 feet along a fault line under the sea. The epicenter was located 45 miles east of the city of Sendai out in the Pacific Ocean. It was almost 3:00 in the afternoon when the earthquake started, and the shaking lasted for 6 minutes.

  17. Landslide tsunami case studies using a Boussinesq model and a fully

    during the tsunami generation phase, i.e. b(t) = b. o. and so on. Using the wavemaker formalism of Watts (1998), we derive center of mass motions that are specific to mass failure type (i.e. slide or slump) and geometry. For many SMF tsunami case studies, these simple center of mass motions will pro-

  18. Indian Ocean tsunami of 2004

    On December 26, 2004, at 7:59 am local time, an undersea earthquake with a magnitude of 9.1 struck off the coast of the Indonesian island of Sumatra. Over the next seven hours, a tsunami —a series of immense ocean waves—triggered by the quake reached out across the Indian Ocean, devastating coastal areas as far away as East Africa.

  19. General Review of the Worldwide Tsunami Research

    With the advancement of the global economy, the coastal region has become heavily developed and densely populated and suffers significant damage potential considering various natural disasters, including tsunamis, as indicated by several catastrophic tsunami disasters in the 21st century.

  20. Earthquakes and tsunami

    Case study: Japan tsunami 2011 (HIC) On Friday 11 March 2011 at 14:46:24, an earthquake of magnitude nine on the Richter scale occurred. It was at the point where the Pacific tectonic plate...

  21. (PDF) Tsunami Case Studies

    The subsequent ocean surveys and field land surveys in the years preceding this event have led to improved models of tsunami generation and also caused a reevaluation of tsunamigenic sources and the risk associated with tsunami hazards. Chapter j 4 Tsunami Case Studies 119 4.3.2 Storegga Slide Tsunamis 4.3.2.1 Generation Three substantial ...

  22. Environmental hazards Case study: Indian Ocean Tsunami 2004

    9 on the Richter Scale and as it happened under the ocean, caused a devastating sea wave called a tsunami. The of the earthquake occurred 200 kilometres west of the island of Sumatra in the...

  23. A comprehensive review on structural tsunami countermeasures

    However, in the case of tsunami mitigation, this is not entirely valid since a failure of Layer 1 (e.g. a seawall) may cause additional damage. ... UNDRR (2019) Limitations and challenges of early warning systems: a case study of the 2018 Palu-Donggala Tsunami. UNESCO-IOC Technical Series No. 150. UNESCO (2011) Reducing and manging the risk of ...

  24. Dashcams record stunning footage of Noto quake and tsunami

    Study: 'Shogun pillow syndrome' heightens the risk of stroke Famed festival with 1,000-year history held for the last time Japan's GDP falls behind Germany to fourth place amid weak yen

  25. Tsunami 8,000 Years Ago May Have Devastated Stone Age Community—Study

    A prehistoric tsunami that occurred more than 8,000 years ago may have devastated Stone Age coastal communities, a study has revealed. The event, known as the Storegga tsunami, affected a large ...