Tsunami Research Articles

Tsunami Waves off the Southeastern Coast of Sakhalin Island Caused by the Eruption of the Hunga–Tonga–Hunga–Ha’apai Volcano on 15 January 2022 According to Observations by Bottom Devices of Waves

The observation of waves (sea level oscillations) in the coastal water area of the southeastern coast of Sakhalin Island was carried out from November 2021 to May 2022 using bottom-mounted high-precision ARW 14 K devices with one second discreteness. The analysis of the obtained time series revealed the presence of anomalous waves in the range of tsunami waves in January 2022. It is shown that the waves detected on 15 January 2022 with periods of 15 minutes–4 hours, significantly standing out above the background, are associated with volcanic tsunami waves. Spectral analysis of the time series showed that the maximum in the spectra of sea level oscillations found after the volcanic eruption is close to the period of the first mode of the shelf seiche. This indicates that incoming tsunami waves excite shelf seiches in the coastal zone and contribute to the generation of longshore and transverse seiches into Mordvinov Bay. Time series analysis made it possible to establish that the spectral density of sea level oscillations for the time interval of the beginning of the eruption of the Hunga–Tonga–Hunga–Ha’apai volcano from 21 December 2021 to 11 January 2022 significantly differs from the spectral density for the time of the Plinian volcanic eruption. An analysis of the statistical characteristics of sea level oscillations performed on two–day series in the range of periods 1 min–4 h for background waves and tsunamis showed an increase in the rms by 3.5 times for a time series containing tsunami waves. It has been established that for background time series containing tsunami waves, the coefficients of asymmetry and kurtosis differ little from“Gaussia” values. This indicates that the arrival of tsunami waves from distant sources does not significantly disrupt the stationarity of the wave field in the observation area.

Tsunami hazard assessment in the South China Sea based on geodetic locking of the Manila subduction zone

Abstract. This study provides a dataset and shows the spatial distribution of tsunami hazard in the South China Sea sourced from the Manila subduction zone. The plate motion data around the Manila subduction zone are used to invert the geodetic locking of the Manila subduction zone, further used to estimate the maximum possible magnitude and applied to obtain a more reliable tsunami hazard assessment. The spatial distribution of tsunami wave height with a 1000-year return period is shown, and several high-hazard areas in the South China Sea are pointed out. Uncertainties in the seismic source are explored, including the slip heterogeneity, the upper limit of seismic magnitude and segmentation. The impact of the locking distribution and randomness of slip on tsunami hazard assessment demonstrates that the traditional uniform slip assumption significantly underestimates the tsunami hazard. Moreover, the assessment results involving the effect of the locking distribution should be more realistic and show a larger tsunami height than when only considering the stochastic slip in most areas, which should prompt coastal management agencies to enhance tsunami prevention awareness.

Modeling wave attenuation through vegetation patches: The overlooked role of spatial heterogeneity

Coastal wetlands serve as vital buffers against extreme hazards due to the wave-dissipating capacity of vegetation. While most studies assume a spatial-uniform vegetation cover when modeling wave attenuation through wetlands, they are far from homogeneous in reality due to life-stage dependent growths, die-offs, and zonation of different vegetation species. In the present study, the XBeach model was applied to explore the role of vegetation spatial heterogeneity on wave attenuations. Random distributed vegetation maps with the same coverage but diverse patch sizes are generated to replicate the complex patterns of real wetland vegetations. We focus on the attenuation of solitary waves and wave groups, representing tsunami and storm waves, respectively. Different wave heights, vegetation densities, and land slopes are considered. For solitary wave cases, it is found that smaller patch sizes lead to higher wave attenuation rates, lower run-up, and less inundation extents. For wave group cases, it is found that the attenuation rates are dependent not only on patch sizes but also wave frequencies; while the higher frequency wave components are attenuated at similar magnitudes by different patch sizes, the infragravity waves are much less attenuated by larger patch sizes due to the penetrating effects through the major gaps between patches. Both cases revealed that under the same vegetation coverage, smaller patch sizes outperform their larger counterparts in terms of wave attenuation capacity. Out study highlights the importance of vegetation spatial heterogeneity, which are seldom considered in evaluating the wetlands' capacity of attenuating extreme waves.

Niedawne tsunami na świecie (2020-2023) - porównania między modelowaniem a pomiarami

Few high magnitude earthquakes were generated worldwide in the last three and a half years, some of which triggered tsunami waves. We took into account all the events during the interval January 2020 - June 2023. There was a total of 15 earthquakes (5 in 2020, 5 in 2021, 2 in 2022, 2 in 2023) which lead to moderate and/or small tsunami waves (above 0.1 m), having magnitudes higher than 7, but also one earthquake with magnitude below 7 (6.8) which lead to very small tsunami waves generation. Not all the high magnitude earthquakes resulted in tsunami waves, depending on the depth, focal mechanism and / or other parameters (distance to shore, local conditions, etc.). From tsunami measurements point of view, we considered the most relevant ones and studied only the events that lead to measured waves higher than 0.5 m. The most significant ones are 5 events: 23rd of June 2020 (15:29 UTC), Near Coast of Oaxaca Mexico, M7.4 (maximum waves 0.68 m); 19th of October 2020 (20:55 UTC) South of Alaska M7.4 (maximum waves 0.76 m); 10th of February 2021 (13:20 UTC) Southeast of Loyalty Island, M7.7 (maximum waves 0.78 m); 4th of March 2021 (19:28 UTC) Kermadec Island region, M8.1 (maximum waves 0.56 m) and 19th of September 2022 (18:05 UTC), Coast of Michoacan Mexico M7.6 (maximum waves 0.79 m). We compared, in this paper, the values of sea level measurements with the results of the tsunami simulations, using the parameters of each earthquake (latitude, longitude, magnitude, depth, focal mechanism). The modeling simulations were accomplished using TRIDEC Cloud software, provided by the German Research Centre for Geosciences (GFZ), Potsdam, Germany. When comparing the values between the two types of data (measured vs. computed), the results show that some simulations overestimate the measured values, others underestimate it. More studies are necessary for a better numerical assessment of sea level, in order to be more precise and closer to the real measurements. Future work might include using two or three different modeling software, for the same earthquake parameters, and comparing the results.

Simulation of large plausible tsunami scenarios associated with the 2019 Durres (Albania) earthquake source and adjacent seismogenic zones

We present an analysis of the hazards of potential earthquake-generated tsunamis along the Albanian–Adriatic coast. The study adopts a case study approach to model plausible tsunamigenic events associated with the 2019 Mw 6.4 Durres (Albania) earthquake source zone. The approach combines current findings on regional tectonics and scenario-based calculations of potential tsunami impacts. The study’s goal is to analyse the propagation of tsunami waves generated by identified seismogenic sources (namely ALCS002 [Lushnje] and ALCS018 [Shijak]) and determine the tsunami risk assessment for Durres City on the Albanian–Adriatic coast. The sources can generate earthquakes with maximum moment magnitudes of Mw 7.5 and Mw 6.8, which are likely to trigger tsunamis that could cause significant impacts in the region. The modelling is performed deterministically with the NAMI DANCE numerical code, including scenarios associated with the largest plausible earthquake. The model integrates bathymetry and topography datasets of large and medium resolutions. Each tsunami scenario simulation is based on the solution of the non-linear shallow water equations used to generate maximum positive wave amplitudes (water elevation), travel time, and tsunami inundation maps. In Durres City, modelling indicates that medium-sized waves could reach up to 2.5 m inland, posing a significant danger to the city’s low-lying areas. The most substantial tsunami waves are expected to impact the area within the first 10 to 20 min. Combining inundation maps and information on exposed assets allows for identifying areas where damages can be expected. In terms of human impact, a preliminary analysis shows that the study area is prone to tsunami threat, with more than 138,000 inhabitants living in vulnerable urban areas of Durres City by 2036. The model’s capacity to capture details related to the presence of buildings is limited due to constraints posed by the resolution of bathymetry and topography datasets available during this study. If refined with high-resolution bathymetry and topography datasets, our results can be considered a backbone for exposure and resilience assessment features to be integrated into preparedness or new urban development plans.

Deep neural network-based prediction of tsunami wave attenuation by mangrove forests

The goal of this research is to develop a model employing deep neural networks (DNNs) to predict the effectiveness of mangrove forests in attenuating the impact of tsunami waves. The dataset for the DNN model is obtained by simulating tsunami wave attenuation using the Boussinesq model with a staggered grid approximation. The Boussinesq model for wave attenuation is validated using laboratory experiments exhibiting a mean absolute error (MAE) ranging from 0.003 to 0.01. We employ over 40,000 data points generated from the Boussinesq numerical simulations to train the DNN. Efforts are made to optimize hyperparameters and determine the neural network architecture to attain optimal performance during the training process. The prediction results of the DNN model exhibit a coefficient of determination (R2) of 0.99560, an MAE of 0.00118, a root mean squared error (RMSE) of 0.00151, and a mean absolute percentage error (MAPE) of 3 %. When comparing the DNN model with three alternative machine learning models— support vector regression (SVR), multiple linear regression (MLR), and extreme gradient boosting (XGBoost)— the performance of DNN is superior to that of SVR and MLR, but it is similar to XGBoost.•High-accuracy DNN models require hyperparameter optimization and neural network architecture selection.•The error of DNN models in predicting the attenuation of tsunami waves by mangrove forests is less than 3 %.•DNN can serve as an alternate predictive model to empirical formulas or classical numerical models.

Using Ground-Penetrating Radar (GPR) to Investigate the Exceptionally Thick Deposits from the Storegga Tsunami in Northeastern Scotland

A submarine landslide on the edge of the Norwegian shelf that occurred around 8150 ± 30 cal. years BP triggered a major ocean-wide tsunami, the deposits of which are recorded around the North Atlantic, including Scotland. Ground-penetrating radar (GPR) was used here to investigate tsunami sediments within estuaries on the coast of northeastern Scotland where the tsunami waves were funnelled inland. Around the Dornoch Firth, the tsunami deposits are up to 1.6 m thickness, which is exceptionally thick for tsunami deposits and about twice the thickness of the 2004 IOT or 2011 Tohoku-oki tsunami deposits. The exceptional thickness is attributed to a high sediment supply within the Dornoch Firth. At Ardmore, the tsunami appears to have overtopped a beach ridge with a thick sand layer deposited inland at Dounie and partly infilled a valley. Later, fluvial activity eroded the tsunami sediments locally, removing the sand layer. At Creich, on the north side of the Dornoch Firth, the sand layer varies in thickness; mapping of the sand layer with GPR shows lateral thickness changes of over 1 m attributed to a combination of infilling an underlying topography, differential compaction, and later reworking by tidal inlets. Interpretation of the GPR profiles at Wick suggests that there has been a miscorrelation of Holocene stratigraphy based on boreholes. Changes in the stratigraphy of spits at Ardmore are attributed to the balance between sediment supply and sea-level change with washovers dominating a spit formed during the early Holocene transgression, while spits formed during the subsequent mid-Holocene high-stand are dominated by progradation.

Nested Modeling Framework for Assessing Tsunami Wave Impact on the Coastal Region of Gujarat, West Coast of India: A Hypothetical Approach

Nested Modeling Framework for Assessing Tsunami Wave Impact on the Coastal Region of Gujarat, West Coast of India: A Hypothetical Approach

Dynamics of tsunami wave propagation in uncertain environment

Dynamics of tsunami wave propagation in uncertain environment

A Review of Tsunamis Generated by Volcanoes (TGV) Source Mechanism, Modelling, Monitoring and Warning Systems

Tsunamis generated by volcanic eruptions have risen to prominence since the December 2018 tsunami generated by the flank collapse of Anak Krakatau during a moderate eruption and then the global tsunami generated by the explosive eruption of the Hunga volcano in the Tongan Archipelago in January 2022. Both events cause fatalities and highlight the lack in tsunami warning systems to detect and warn for tsunamis induced by volcanic mechanisms. Following the Hunga Tonga—Hunga Ha’apai eruption and tsunami, an ad hoc working group on Tsunamis Generated by Volcanoes was formed by the Intergovernmental Oceanographic Commission of UNESCO. Volcanic tsunamis differ from seismic tsunamis in that there are a wide range of source mechanisms that can generate the tsunamis waves and this makes understanding, modelling and monitoring volcanic tsunamis much more difficult than seismic tsunamis. This paper provides a review of both the mechanisms behind volcanic tsunamis and the variety of modelling techniques that can be used to simulate their effects for tsunami hazard assessment and forecasting. It gives an example of a volcanic tsunami risk assessment undertaken for Stromboli, outlines the requirement of volcanic monitoring to warn for tsunami hazard and provides examples of volcanic tsunami warning systems in Italy, the Hawaiian Island (USA), Tonga and Indonesia. The paper finishes by highlighting the need for implementing monitoring and warning systems for volcanic tsunamis for locations with submarine volcanoes or near-shore volcanoes which could potentially generate tsunamis.

Tsunami Waves off the Southeastern Coast of Sakhalin Island Caused by the Eruption of the Hunga–Tonga–Hunga–Ha’apai Volcano on January 15, 2022 According to Observations by Bottom Devices of Waves

Tsunami Waves off the Southeastern Coast of Sakhalin Island Caused by the Eruption of the Hunga–Tonga–Hunga–Ha’apai Volcano on January 15, 2022 According to Observations by Bottom Devices of Waves

Potential Sunda Strait tsunami hazard due to the current deformation of Anak Krakatau

Abstract The tsunami Sunda Strait event at 2018 is a prove that volcanic activity may trigger a devastating and unpredictable tsunami. Several findings indicate that this catasrophe generated by flank collapse caused by the instability of Gunung Anak Krakatau (AK). Other processes of volcanic activity that cause tsunamis include pyroclastic flows, subaerial and submarine landslides, underwater explosions, blasts, and caldera collapse. In the 1883, Krakatau Mount produced the largest tsunami event, and it is apparent that AK is one of threats in Sunda Strait that may potentially produce tsunami in the future since it is still active until now. Some latest research shows there are two submarine landslide threats near Gunung Anak Krakatau; in the northeast part of AK and an elliptical landslide source in the west part of AK with the estimated landslide volume about 0.014 km3 and 0.6 km3, respectively. Those threats will be simulated by shallow water equation model to obtain the tsunami wave height near the source and at crucial areas along Sunda Strait as well as its tsunami time arrival. The first scenario is sourced from a new deposit from the 2018 eruption, and the latter is a change of slopes bathymetry due to volcanic or seismic activity. It is observed that the highest elevation of tsunami from the first scenario reached only 2.5 cm at Tanjung Lesung, Banten and 80 cm at the nearest island, Panjang Island. The second scenario has the height of 50 cm at Labuan, Banten and 70 cm at Panjang Island. The tsunami time travel at the surrounding islands is ranging between 0.6 - 5.8 minutes (scenario 1) and 0.7-4.9 minutes (scenario 2). Both scenario reached the inexpensive device of sea level monitoring at the east side of Rakata after 5 minutes and the height is 30 cm and 1.6 cm respectively for scenario 1 and 2.

Tsunami model of the 2021 Flores earthquake and its impact on Labuan Bajo, East Nusa Tenggara

Abstract The Flores earthquake on December 14, 2021, was recorded to cause a weak tsunami in East Nusa Tenggara. The epicenter was in the Flores Sea within 10 km of the hypocenter that triggered M 7.3 and impacted some coastline regions of East Nusa Tenggara, including Labuan Bajo. This study aimed to hindcast this phenomenon and mitigate the coastline region by applying the forecasting scenarios related to the amplification of the earthquake magnitudes and type of the sub-bottom displacements. The TUNAMI Software has been selected to illustrate tsunami wave propagation and inundation. The elevation data for the inputting model used BATNAS-DEMNAS National, while the rest of the parameters were applied using the two scenarios and the best guess. Based on the model results, it can be concluded that the source characteristics and the magnitudes played an essential role in inundating the coastal region in Labuan Bajo, West Manggarai Regency, East Nusa Tenggara.

Computer model for detecting tsunami wave hazard on built-up land using machine learning and sentinel 2A satellite imagery

<p>The aim of this research is to compile a tsunami wave hazard scale based on built-up land density extracted and classified by machine learning from Sentinel 2A satellite and digital elevation model (DEM) imageries. This research was carried out in 5 stages, namely: (i) pre-processing of Sentinel 2A and DEM images, (ii) Classification of VI data using the machine learning algorithms, (iii) Spatial prediction using the ordinary kriging method, (iv) Field testing using the confusion matrix method, (v) Preparation of decision matrix for tsunami wave hazard. The results of the study show that the most accurate classification algorithm for classifying built-up indices data is the k-nearest neighbor (k-NN) algorithm. The results of the statistical accuracy test show that the most accurate is normalized difference built-up index (NDBI) with a mean of square error (MSE) value of 0.073 and a mean of absolute error (MAE) of 0.003. DEM analysis shows that the research area is at an altitude of 0–15 meters above sea level so it is in the high vulnerability to medium vulnerability category. Field testing showed user accuracy of 91.11%, manufacturer accuracy of 92.16%, and overall average accuracy of 91%.</p>

The impact of the Hunga Tonga–Hunga Ha’apai volcanic eruption on the Peruvian atmosphere: from the sea surface to the ionosphere

The eruption of the Hunga Tonga Hunga Ha’apai volcano on 15 January 2022 significantly impacted the lower and upper atmosphere globally. Using multi-instrument observations, we described disturbances from the sea surface to the ionosphere associated with atmospheric waves generated by the volcanic eruption. Perturbations were detected in atmospheric pressure, horizontal magnetic field, equatorial electrojet (EEJ), ionospheric plasma drifts, total electron content (TEC), mesospheric and lower thermospheric (MLT) neutral winds, and ionospheric virtual height measured at low magnetic latitudes in the western South American sector (mainly in Peru). The eastward Lamb wave propagation was observed at the Jicamarca Radio Observatory on the day of the eruption at 13:50 UT and on its way back from the antipodal point (westward) on the next day at 07:05 UT. Perturbations in the horizontal component of the magnetic field (indicative of EEJ variations) were detected between 12:00 and 22:00 UT. During the same period, GNSS-TEC measurements of traveling ionospheric disturbances (TIDs) coincided approximately with the arrival time of Lamb and tsunami waves. On the other hand, a large westward variation of MLT winds occurred near 18:00 UT over Peru. However, MLT perturbations due to possible westward waves from the antipode have not been identified. In addition, daytime vertical plasma drifts showed an unusual downward behavior between 12:00 and 16:00 UT, followed by an upward enhancement between 16:00 and 19:00 UT. Untypical daytime eastward zonal plasma drifts were observed when westward drifts were expected. Variations in the EEJ are highly correlated with perturbations in the vertical plasma drift exhibiting a counter-equatorial electrojet (CEEJ) between 12:00 and 16:00 UT. These observations of plasma drifts and EEJ are, so far, the only ground-based radar measurements of these parameters in the western South American region after the eruption. We attributed the ion drift and EEJ perturbations to large-scale thermospheric wind variations produced by the eruption, which altered the dynamo electric field in the Hall and Pedersen regions. These types of multiple and simultaneous observations can contribute to advancing our understanding of the ionospheric processes associated with natural hazard events and the interaction with lower atmospheric layers.Graphical

A Lie symmetry approach to traveling wave solutions, bifurcation, chaos and sensitivity analysis of the geophysical Korteweg–de Vries equation

Strong waves known as tsunamis are caused by earthquakes, landslides, or volcanic eruptions that traverse oceans. This article examines the geophysical Korteweg–de Vries (GPKdV) equation, which controls the propagation of tsunami waves in seas. The study involves exploring symmetry diminution exerted Lie group analysis, examining the properties of the dynamical structure with the help of bifurcation phase pictures, and researching the dynamic demeanor of the perturbed dynamical system utilizing chaos theory. Techniques such as 3D and 2D phase portraits, time series analysis, poincaré maps, looking into the existence of multistability in the autonomous structure under diverse beginning circumstances, lyapunov exponent, and bifurcation diagram are applied to identify chaotic demeanor. Furthermore, the study establishes general forms of solitary wave results, containing periodic, trigonometric, and singular soliton results, by using the unified Riccati equation expansion approach to address the examined problem analytically. These results are visually represented as 2D and 3D graphs with cautiously chosen parameters, along with their accompanying constraint conditions. Moreover, the sensitivity evaluation of the investigated equation is discussed and demonstrated pictorially. The discoveries revealed are intriguing, novel, and potentially helpful in understanding a wide range of physical events in engineering and science.

Experimental and Analytical Investigations of Tsunami Waves Caused by Earthquakes and Landslides

Experimental and Analytical Investigations of Tsunami Waves Caused by Earthquakes and Landslides

Nonlinear processes in tsunami simulations for the Peruvian coast with focus on Lima and Callao

Abstract. This investigation addresses the tsunami inundation in Lima and Callao caused by the massive 1746 earthquake (Mw 9.0) along the Peruvian coast. Numerical modeling of the tsunami inundation processes in the nearshore includes strong nonlinear numerical terms. In a comparative analysis of the calculation of the tsunami wave effect, two numerical codes are used, Tsunami-HySEA and TsunAWI, which both solve the shallow water (SW) equations but with different spatial approximations. The comparison primarily evaluates the flow velocity fields in inundated areas. The relative importance of the various parts of the SW equations is determined, focusing on the nonlinear terms. Particular attention is paid to the contribution of momentum advection, bottom friction, and volume conservation. The influence of the nonlinearity on the degree and volume of inundation, flow velocity, and small-scale fluctuations is determined. The sensitivity of the solution concerning the bottom friction parameter is also investigated, showing the effects of nonlinearity processes in the inundated areas, wave heights, current velocity, and the spatial structure variations shown in tsunami inundation maps.

Effect of bathymetry data on tsunami wave ray tracing in the western Banten sea

The lack of tsunami wave warning during the Anak Krakatau collapse in 2018 resulted in devastating damage to coastal areas. The tsunami wave arrived in the coastal areas approximately half an hour after the collapse. As a tsunami wave can travel abruptly, an early warning system should warn faster. However, a warning system based on a sophisticated hydrodynamic model in real-time would take time to conduct the numerical tsunami wave simulation. Hence, this study proposes a fast and reliable estimation of tsunami wave propagation through a classic ray tracing analysis. We use two ray-tracing methods to investigate the tsunami wave propagation from Anak Krakatau to the western Banten Sea. The first method follows Snell’s law, considering dispersive waves, and the second assumes non-dispersive waves based on the ray tracing equations, considering the Earth’s sphericity. Both methods are quantitatively evaluated by comparing the travel time measured at Anyer and Marina Jambu. This study finds that non-dispersive wave tracing performs a shorter computational time and slightly better prediction of tsunami wave propagation than dispersive-based wave tracing, with a relative absolute difference of the travel time of 17.9–26.7% in Anyer and 3.6–5.2% in Marina Jambu. This study also confirms the importance of bathymetry validity in wave ray tracing. Two regional bathymetry datasets with a mean difference of less than 5 m result in different wave ray tracing, in which one dataset does not produce the wave ray path towards the Panaitan Island. Based on bathymetric surveys in Anyer and Marina Jambu, the national bathymetry data (BATNAS) has shown its superiority to being used as a bathymetry in the ray-tracing process, with correlations of 81% and 93% in both areas compared to other available bathymetry datasets. We summarize that reliable bathymetry data and the non-dispersive ray tracing method can be used as an initial estimation of tsunami wave propagation efficiently.

New source model for the 1771 Meiwa tsunami along the southern Ryukyu Trench inferred from high-resolution tsunami calculation

The 1771 Meiwa tsunami which struck the southern Ryukyu Islands (Sakishima Islands) had greater than 22 m run-up height, leaving about 12,000 casualties in its wake. At many places, the tsunami inundation or lack of inundation is well recorded in historical documents. Several tsunami source models have been proposed for this event using historical records as constraints of tsunami calculations. Nevertheless, the source model remains under discussion. This study re-evaluated the tsunami wave source model of the 1771 Meiwa tsunami using high-resolution (10 m mesh) bathymetric and topographical data for tsunami calculation, the latest historical record dataset, and seismological knowledge. Results demonstrated that a tsunami earthquake along the southern Ryukyu Trench was the likely cause of the 1771 event. However, it is noteworthy that assumption of a large slip with 30 m is necessary for a shallow and narrow region (fault depth = 5 km, fault width = 30 km, Mw = 8.49) of the plate boundary in the Ryukyu Trench, which is far larger than previously thought. This requirement of very large initial water level change at the source might involve not only the fault rupture along the plate boundary but also deformation by splay faults, inelastic deformation of unconsolidated sediments near the trench axis, and/or giant submarine landslides. Results also show that the effects of fault parameters on the run-up were quite different depending on the offshore coral reef width. This phenomenon strongly constrained the fault width to 30 km. Our tsunami ray tracing analysis further revealed the effects of bathymetry on tsunami propagation. It is noteworthy that meter-long huge tsunami boulders tend to be distributed along the specific coasts at which the tsunami was concentrated by bathymetric effects. This finding suggests that past tsunamis, including the 1771 event, might have affected the specific coral reefs on Sakishima Islands repeatedly, which is crucially important for understanding the heterogeneous distribution of tsunami boulders. This feature might also be useful to elucidate the effects of large tsunamis on the corals and reefs because a direct comparison of coral reefs that are damaged and not damaged by tsunami waves is testable in narrow areas in the case of the Sakishima Islands.