Tsunami Research Articles

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.

Exploring tsunami generation and propagation: A case study of the 2018 Palu earthquake and tsunami

The tsunami disaster in Palu, Indonesia, on September 28, 2018, caused many casualties and damage along Palu Bay, Indonesia. Prior to the tsunami, an earthquake was recorded 25 km northeast of Donggala. The analysis suggests that the tsunami was triggered by a landslide beneath the waters of Palu Bay. The investigation then focused on the earthquake's impact on the tsunami. This study conducts simulations to discern the dominant causes of tsunamis, comparing the effects of landslides with earthquakes. Three distinct tsunami sources were identified: earthquake-only (T-E), earthquake and submarine landslide (T-EL), and submarine landslide-only (T-L). Interpretation of tsunami source parameters for underwater landslides is carried out accurately. A nesting grid numerical method was employed to increase resolution and accuracy in the simulations. This study accurately interprets tsunami source parameters using data from the Bathymetry National Data-Geospatial Information Agency (185 m resolution) and the field survey data from Baruna Jaya IV-BPPT Research Vessel Indonesia (25 m resolution), thereby contributing to the creation of a tsunami inundation model map. This map serves to inform preparedness efforts and guide recommendations for infrastructure development in the Palu Bay area. The tsunami inundation model map is verified with field data and estimated using tide gauge monitoring data. The characteristics of the tsunami waves on September 28, 2018, reflect a T-EL-type tsunami source, while the T-L type source produces tsunami waves with longer periods compared to earthquake-generated tsunamis (T-EL). The T-E type, located on land, had minimal influence in causing the tsunami, despite the destructive potential of the shallow-depth M7.5 earthquake.

Tsunami Ping-Pong: Generating The Whole Tsunami Event

Considerable progress has been made over the past two decades in modelling representative tsunami waves in the laboratory. Developments such as the pneumatic tsunami simulator, TS, (Chandler et al, 2021), large wave paddles (Schimmels et al, 2016) and pump driven systems (Goseberg et al, 2013) have allowed full incident tsunami time series to be reproduced at large enough scales to provide reliable physical modelling data. These developments have enabled new insights into the time-varying influence of tsunami waves on run-up (McGovern et al, 2018), buildings (Foster et al, 2017), coastal structures (McGovern et al, 2022) and in particular the scour of sediments around these structures (McGovern et al, 2019). These advances are changing the way engineers and others design and plan for tsunami. However we are still only modelling ‘half the story’. In all existing published studies, the experiments simulated the effect of a single incident wave. We have so far ignored two significant elements of real tsunami events. Firstly, the rundown or return flow and secondly, the effect of multiple waves within a wave train. Both these aspects are difficult to model physically, particularly as the return flow and subsequent waves require a representative topography downstream of a test section. In most facilities there is not enough flume left to create this overland topography. In addition, changing this bathymetry to represent different scenarios such as a coastal plain or a small foreshore and an inland cliff, requires significant modelling effort and cost. To solve these challenges we propose the use of two TSs facing each other with the test section in between. This concept is shown in Figure 1. This extended abstract presents data from initial trials of the dual TS system conducted at HR Wallingford in 2023 during a three-day test window as part of the MAKEWAVES collaboration. The importance of simulating the return flow from a tsunami is demonstrated through its influence on scour around a rectangular building.

Tsunami Runup Attenuation By Onshore Obstacles

In a time of climate emergency due to global warming, nature-based coastal defence systems are attractive solutions for flood mitigation and adaptation. Coastal forests such as mangroves have received a growing interest for their disaster mitigation effectiveness such as water flow energy dissipation, hence helping communities to become more resilient (Iimura & Tanaka, 2012). The role of coastal forests as a defence measure was highlighted in the aftermath of the 2004 Indian Ocean Tsunami, which claimed the lives of more than 200,000 people and displaced millions more across fourteen countries. Post-disaster damage observations indicated that forests, particularly mangroves, reduced the impact of the tsunami wave in some locations. As a result, significant international relief and reconstruction efforts focused on extensive forest replantation of coastlines (Satake, 2014). The role of coastal vegetation in reducing the severity of tsunami waves has been studied since. Several studies using physical modelling and computational approaches have provided insights into the wave attenuation provided by coastal vegetation, in terms of relationships between incident hydrodynamic conditions, forest configurations and wave height decay. However, there are still many gaps in knowledge, particularly in quantifying the efficacy of coastal forests in reducing inland hydrodynamic conditions (Tomiczek et al., 2020). It is therefore essential to improve the understanding on how wave heights, velocities and runup are influenced by the characteristics of the “obstacles”, e.g. the forest density, as well as the incident hydrodynamic conditions, e.g. the wave period. This study aims to address these questions conducting physical experiments using the novel pneumatic Tsunami Simulator (TS) developed by HR Wallingford together with UCL (Rossetto et al., 2011).

Geohazard features of the Tyrrhenian Calabria

ABSTRACT This paper accompanies the Maps of Geohazard features of the Cilento and the Calabro-Tyrrhenian continental margin in the southern Tyrrhenian Sea (Italy). The main geohazard-related features were derived from extensive seafloor mapping through the collection of high-resolution multibeam data acquired during several oceanographic cruises. They encompass many fluids seepage features, fault scarps, landslides scars, gullies, channels, and canyons. Hazards related to coastal landslides and shelf-indenting canyons are very high in these sectors (especially in southern Calabria) due to active seismicity coupled with rapid uplift, high sedimentation rates and narrow or totally absent continental shelf, thus promoting a direct connection between steep slopes and coastal areas. In this setting, mass-wasting features can directly impact coastal or submarine infrastructures or indirectly create local tsunami waves, as observed in historical times. Moreover, this physiographic setting of the margin facilitates the transfer of marine litter toward deep-sea areas.