The coasts of Türkiye are vulnerable to tsunami hazards due to the intensive use of coastal areas and the activity of faults in the surrounding seas. The Samos-İzmir earthquake occurred on October 30, 2020, followed by a tsunami that affected the Sığacık Bay revealed this fact once again, demonstrating the importance of accurately modeling the tsunami hazard across the country. Probabilistic Tsunami Hazard Assessment (PTHA) results for various coastal engineering parameters (i.e., tsunami wave height, tsunami inundation distance) constitute one of the essential inputs of performance-based tsunami risk analysis. The TSUMAPS-NEAM project that ended in 2018 was one of the studies following the probabilistic approach for the Northeast Atlantic, Mediterranean, and connected seas (Basili et al. 2021). The primary objective of this study which is constructed within the TUBITAK (Scientific and Technological Research Council of Turkey) funded 121M750 project, is to develop a comprehensive probabilistic tsunami hazard analysis framework in which the uncertainties regarding active faults that can generate tsunamis for our country's Aegean Sea coasts are addressed fully. For this purpose, a holistic seismotectonic database has been created by compiling catalogs of active faults that can generate tsunamis in the Aegean Sea and its surroundings, important fault parameters, earthquake and focal mechanism solutions from national and international sources. The compiled database is utilized to determine possible tsunami source scenarios and model the epistemic and aleatory uncertainties in these scenarios. In this regard, a complete probabilistic set of tsunami source scenarios that have not been included in previous studies is being developed, and the near-shore tsunami wave height estimations will be determined by performing high-resolution tsunami simulations for each scenario. Considering the lack of hazard-based tsunami assessment for the coasts of the Aegean Sea, the near-shore tsunami wave height hazard curves to be obtained as a result of the project are of great importance in determining the effects of possible tsunamis and assessing the tsunami risk.

Acknowledgement: This study is supported by TUBITAK 1001-Grant Project No: 121M750.

How to cite: Dogan, G. G., Yalciner, A. C., Ozacar, A., Gulerce, Z., Tufekci-Enginar, D., Lutfi Suzen, M., Kanun, O., Pekcan, O., and Yucemen, S.: Fault Based Tsunami Generation and Hazard Analysis: A Probabilistic Study for Aegean Coasts of Türkiye, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17571, https://doi.org/10.5194/egusphere-egu23-17571, 2023.

The Pacific Ring of Fire, stretching over 15 countries, is one of the earth's most Tsunami-prone regions. 80 Percent of the Tsunami Occurrences could be directly or indirectly associated with this region. This study deals with the development of Conditional Probability and Total Probability based approaches for estimating the probability of Tsunami Occurrence in the study area. This study suggests ten regions with a high probability of tsunami occurrence in the region. The prediction results are validated by computing the occurrence probabilities of the known tsunami events in the region. The study reveals that East Asian Countries like Japan, North and South Korea and Parts of China have a probability, more than 75 per cent, of experiencing a strong tsunami (Mw > 7.5) in the next three years from now. Also, certain South American countries like Peru, Chile and Ecuador, Southeast Asian Counties like Indonesia and South Pacific Countries like Papua New Guinea, Australia, and the Solomon Islands have a high probability of tsunami occurrence (90 Percent and above) in the next five years.  Based on this methodology, it has been possible to predict the Indonesian Tsunami of December 14th, 2021, with a probability of 83 Percent.

How to cite: Sabah, N. and Shanker, D.: A Conditional Probability based Tsunami Prediction for the Pacific Ring of Fire, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11328, https://doi.org/10.5194/egusphere-egu23-11328, 2023.

Local Probabilistic Tsunami Hazard Analysis (PTHA) aims to quantify the probability distribution of inundation intensity parameters, such as maximum flow-depth, at a given location over a specified period of time. In a Monte Carlo framework such an analysis is dependent on the simulation of a large number of scenarios. A particularly expensive step, from a computational point of view, is the solving of the nonlinear shallow water equations associated with the tsunami run-up. This problem is even more pronounced in the context of Tsunami Early Warning and Probabilistic Tsunami Forecasting (PTF). A site specific (local) tsunami run-up emulator, trained on precalculated simulation results, enables rapid estimation of inundation maps allowing an assessment of a large number of scenarios with limited computational resources. While high dimensional input and output, dependence on topography and nonlinear dynamics has made the problem intractable for traditional statistical methods, the problem has recently been approached using new techniques developed within the field of Machine Learning. In this work we consider the problem of predicting onshore maximal flow-depth based on timeseries associated with simulated offshore gauge measurements. The site of study is the town of Catania in eastern Sicily. The dataset comprises more than 32,000 tsunami simulations for different earthquake sources in the Mediterranean Sea. Promising results have been obtained using only a small fraction of the total number of simulations as training data. The ML-based inundation predictions for locations close to the water's edge, which are flooded in many of the scenarios, show excellent correspondence with the numerical simulation results. Predicting inundation at locations further inland, which are flooded in only a small number of the simulations, is more challenging.

How to cite: Storrøsten, E., Gibbons, S., and Løvholt, F.: Site specific emulators for tsunami run-up simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7459, https://doi.org/10.5194/egusphere-egu23-7459, 2023.

Tsunami warning systems currently focus on the first parameters of the earthquake, based on a 24-hour monitoring of earthquakes, seismic data processing (Magnitude, location), and tsunami risk modelling at basin scale.

The French Tsunami Warning Center (CENALT) runs actually two tsunami modelling tools where the water height at the coast is not calculated (i.e., Cassiopee based on a pre-computed database, and Calypso based on real time simulations at basin scale). A complete calculation up to the coastal impact all along the French Mediterranean or coastline is incompatible with real time near field or regional forecast, as nonlinear models require fine topo-bathymetric data nearshore and indeed a considerable computation time (> 45 min). Predicting coastal flooding in real time is then a major challenge in such context. To overcome these limitations, non conventional approches such as machine learning methods are being explored. Among the huge number of actual models, deep learning techniques are becoming increasingly popular. Severals studies have shown the interest of using MLPs (Multilayer perceptrons) and CNNs (Convolutional neural networks) to quickly transform a deep ocean simulation result into a coastal flooding model. Once trained on a specific output area with a large dataset, the networks are able to predict in seconds the tsunami inundation map from any earthquake scenario drawn from a seismic source database representative of the seismotectonic context of the region of interest.

A first study training neural networks to predict the maximum water height maps was performed on three specific French cities (Nice, Antibes and Cannes) to evaluate the capacity of the models to reproduce the ground truth. The objective here is to extend the method to predict, in addition to maximum wave heights and runups, maximum retreats and currents along the entire French Mediterranean coastline. The spatial resolution of the finer bathymetric grids is set to 25 meters. To be representative of reality, the training dataset is fed with seismic scenarios derived from the CENALT fault database and taking into account a stochastic slip distribution. The method provides promising early results.

How to cite: Andraud, P., Gailler, A., Dias, F., and Vayatis, N.: Deep learning approach for real-time tsunami impact forecasting in near field context – application to the French Mediterranean coastline, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7763, https://doi.org/10.5194/egusphere-egu23-7763, 2023.

Subduction zones are the most seismically active regions in the world and hosted many great tsunamigenic earthquakes in the past, often with destructive coastal consequences. Hence, an accurate estimate of the tsunami forecast is crucial in Tsunami Early Warning Systems (TEWS) framework. However, the inherent uncertainties associated with the tsunami source estimation in real-time make tsunami forecasting challenging. 

In this study, we consider the South American subduction zone, where in the last 15 years occurred, three M8+ tsunamigenic earthquakes; in particular, we focus on the 2014 M_w 8.1 Iquique event.

Here, we evaluate the variability of the tsunami forecasting for the Chilean coast as resulting i) from the coseismic slip model obtained by geophysical data inversion and ii) from an expeditious method for the tsunami source estimation, based on an extension of the well-known spectral approach. 

In the former method, we estimate the slip distribution of the 2014 Iquique earthquake by jointly inverting tsunami (DARTs and tide-gauges) and GPS data; we adopt a 3D fault geometry and Green’s functions approach.

On the other hand, a set of stochastic slip models in the latter is generated through a Phase Variation Method (PVM), where realizations are obtained from both the wavenumber and phase spectra of the source.

In the analysis, we also evaluate how the different physics complexity included in the tsunami modelling (e.g. by including dispersion or not) can be mapped into the tsunami forecasting uncertainty. Finally, as an independent check, we compare the predicted deformation field from the slip models (inverted or by PVM) with the RADARSAT-2 InSAR data.

How to cite: Romano, F., Catalan, P., Lorito, S., Cipriano, E. S., Atzori, S., Lay, T., Tonini, R., Volpe, M., Piatanesi, A., Jorge, M. S., and Manuel J, C. D.: Comparison between the uncertainty in the tsunami forecast from slip models obtained from geophysical data inversion and by a Phase Variation Method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9530, https://doi.org/10.5194/egusphere-egu23-9530, 2023.

Tsunami urgent computing procedures quantify the potential hazard due to an earthquake right after its occurrence, that is within a few hours. The hazard is quantified by simulating the propagation of the tsunami waves in the sea, accounting for the uncertainty due to the scarce knowledge of the source parameters and wave modelling uncertainty.

In the context of the European project eflows4HPC, a workflow is currently in development for tsunamis hazard urgent computing, which consists of the following steps: 1) Retrieval of information about the tsunamigenic seismic event (magnitude, hypocentre and their uncertainties); 2) Definition of an ensemble of seismic sources; 3) Simulation of seismic/tsunamigenic waves propagation for each scenario in the ensemble; 4) Results aggregation to produce an estimate of seismic and tsunami hazard, which also incorporates a basic treatment of modelling uncertainty. The ensembles cover the uncertainty on source characteristics and may consequently be very large (generally 10,000 to 100,000 of scenarios; Selva et al., Nat. Comm.), requiring very high computational resources for the urgent computing context. It is thus necessary to reduce the size of these ensembles to limit the number of simulations and to converge faster towards stable results of hazard calculation.

We developed and tested several sampling procedures aiming to reduce the number of scenarios in the ensemble and, at the same time, to integrate the new incoming information as they become available (e.g. solutions for focal mechanisms, seismic or tsunami records). When applied to several past earthquakes and tsunamis (e.g., the 2003 Boumerdes and the 2017 Kos-Bodrum earthquakes), our novel sampling strategies yielded a reduction of 1 or 2 order of magnitudes of the ensemble size, allowing a drastic reduction of the computational effort. Also, the update of the ensemble based on the incoming of new data, which strongly reduce the uncertainty, yields to an update of the probabilistic forecasts without compromising its accuracy. This may result very important for mitigating the risk far from the seismic source, as well as improving the risk management by better informing decision making in a frame of urgency.

How to cite: Cordrie, L., Selva, J., Bernardi, F., and Tonini, R.: Using available and incoming data for reducing and updating seismic source ensembles for probabilistic tsunami forecasting (PTF) in early-warning and urgent computing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12363, https://doi.org/10.5194/egusphere-egu23-12363, 2023.

Operational Tsunami Early Warning Systems (TEWS) are crucial for mitigation and highly reducing the impact of tsunamis on coastal communities worldwide. In the North-East Atlantic, the Mediterranean, and connected Seas (NEAM) region, these systems have historically utilized Decision Matrices for this purpose. The very short time between tsunami generation and landfall in this region makes it extremely challenging to use real-time simulations to produce more precise alert levels and the only way to include a computational component in the alert was to use precomputed databases. Nevertheless, in recent years, computing times for a single scenario have been progressively reduced to a few minutes or even seconds depending on the computational resources available. In particular, the EDANYA group at the University of Málaga, Spain, has focused on this topic and developed the GPU code Tsunami-HySEA for Faster Than Real Time (FTRT) tsunami simulations. This code has been implemented and tested in TEWS of several countries (such as Spain, Italy, and Chile) and has undergone extensive testing, verification and validation.

In this study, we propose the use of neural networks (NN) to predict the maximum height and arrival time of tsunamis in the context of TEWS. The advantage of this approach is that the inference time required is negligible (less than one second) and that this can be done in a simple laptop. This allows to consider uncertain input information in the data and still providing the results in some seconds. As tsunamis are rare events, numerical simulations using the Tsunami-HySEA are used to train the NN model. This part of the workflow requires producing a large amount of simulations and large HPC computational resources must be used.

Machine learning (ML) techniques have gained widespread adoption and are being applied in all areas of research, including tsunami modeling. In this work, we employ Multi-Layer Perceptron (MLP) neural networks to forecast the maximum height and arrival time of tsunamis at specific locations along the Chipiona-Cádiz coast in Southwestern Spain. In the present work, initially several individual models are trained and we show that they provide accurate results. Then ensemble techniques, which combine multiple single models in order to reduce variance, are explored. The ensemble models often produce improved predictions.

The proposed methodology is tested for tsunamis generated by earthquakes on the Horseshoe fault. The goal is to develop a neural network (NN) model for predicting the maximum height and arrival time of such tsunamis at multiple coastal locations simultaneously. The results of our analysis show that deep learning is a promising approach for this task. The proposed NN models produce errors of less than 6 cm for the maximum wave height and less then 212 s for the arrival time for tsunamis generated on the Horseshoe fault in the Northeastern Atlantic.

How to cite: Rodríguez Gálvez, J. F., Macías Sáncez, J., Castro Díaz, M. J., de la Asunción, M., and Sánchez-Linares, C.: Use of Neural Networks for Tsunami Maximum Height and Arrival Time Predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12935, https://doi.org/10.5194/egusphere-egu23-12935, 2023.

The estimation of expected damage and losses from natural hazards requires that uncertainties in the modelling and knowledge of future events be quantified and taken into consideration. This is true not only in a fully probabilistic context but also in future scenario calculations, particularly when looking at two or more cascading hazards in which the link between them is not univocal. An offshore earthquake that triggers a tsunami would be one such case. Even if the moment magnitude and rupture size and location of the earthquake were fully defined, it is not possible to know a priori the slip distribution along the rupture and the subsequent co-seismic topographic displacements. Many feasible slip distributions can be associated with the same moment magnitude and dimensions of the rupture, and these lead to a distribution of subsequent topographic displacements and, with that, a diversity of tsunami outcomes. Exactly how much variety exists in the resulting tsunamis, in terms, for example, of maximum wave height or maximum flow velocity at points of interest, and, ultimately, damage to buildings and losses, is the question driving the present study, which is part of the “risk workflow for CAScading and COmpounding hazards in COastal urban areas” (CASCO) project. The ultimate objective is to understand the relevance of this uncertainty and whether it needs to be modelled in the whole damage/loss calculation chain.

To investigate this, 500 realisations of stochastically generated rupture slip have been produced for the 1908 Mw 7.1 Messina earthquake, whose rupture source is taken from the Italian Database of Individual Seismogenic Sources (DISS). The subsequent realisations of ground surface deformation (at the bottom of the sea and on land) were used as input to run realisations of the resulting tsunami in the Strait of Messina, eastern Sicily and western Calabria with the TsunAWI software. Maximum wave heights, maximum absolute velocities and maximum flux can vary significantly for selected observation points along the coast and within the Messina Strait. While a weak correlation has been identified between these tsunami outputs and inputs such as the maximum initial co-seismic vertical displacement, a stronger correlation has been observed with respect to the distance to the centroid of rupture slip. So far, results indicate that the uncertainty in the co-seismic slip along the rupture and the subsequent vertical displacements has a relevant impact on the resulting tsunami, suggesting that this source of uncertainty should not be entirely neglected in models. Using these tsunami outputs to estimate damage to buildings in the area allows us to understand the ultimate final impact on damage and loss calculations, and to develop and test strategies to reduce the resulting computational demand.

How to cite: Nievas, C. I., Androsov, A., and Weatherill, G.: Earthquake-Triggered Tsunamis: Impact of the Uncertainty in the Rupture Slip Distribution on the Resulting Tsunami Wave Heights and Flow Velocities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6291, https://doi.org/10.5194/egusphere-egu23-6291, 2023.

The features of the seismic ruptures, such as the duration of shallow earthquakes in subduction zones, may affect the tsunami generation and the inundation intensity. Numerical and experimental results have shown how the interaction between the shallow part of the fault and the seismic radiation trapped in the hanging wall, can lead to enhanced up-dip rupture propagation. This in turn may result in shallow slip amplification producing larger vertical displacement, and even transient ground motion that is larger than the final static displacement. On the other hand, tsunami modelling for hazard assessment and early warning is generally based on static sea-floor displacement obtained with an instantaneous elastic dislocation (without shallow slip amplification) on a simplified hydrostatic model for tsunami generation and propagation. Here, we aim to analyze the relative importance of these effects and the optimal modelling strategy for the tsunami generation. Using a Tohoku-like setting, we impose time dependent initial conditions as computed from 1-D dynamic rupture simulations, by varying the rupture extent and duration over a wide range of stress-drop, rigidity and average slip values (corresponding to earthquake magnitudes between 7.5 and 9, approximately). We performed 1-D numerical tsunami simulations using both the hydrostatic and the multi-layer non-hydrostatic versions of Tsunami-HySEA. We also account for different coastal morphologies, modelling the presence of shelf and/or fjords and variable slope bathymetry. We address, first, how the time-dependent sea-floor displacement characteristics effects may affect (enhancing or reducing) the tsunamigenic potential. To do this, we investigated the resulting tsunami features, in terms of maximum wave height above sea level (also seaward) and maximum run-up, in relation to the spatial and temporal characteristic scales of the transient sea floor displacement. We also compare the simulations with a time-dependent initial condition against those where a static sea-floor displacement is used. We show that the use of a static source systematically overestimates the tsunami effects on the mainland, with the more realistic tsunami reduced due to the seaward seismic rupture (up-dip) directivity, opposite to the direction of the tsunami propagation. Moreover, the slower the rupture, the larger the overestimation. Conversely, as the rupture slows down, the seismic rupture propagating in the same direction of the tsunami increases the tsunami amplitude toward the open ocean. Second, we wish to assess in which conditions and to what extent it is enough to use a shallow-water tsunami model and when, instead, a more complex tsunami modelling scheme is required. The hydrostatic simulations lead to overestimate the inundation, although less significantly with respect to the static/dynamic comparison. We finally investigate how the discrepancy between simplified and complex modelling is controlled by different trench, shelf, and coastal morphologies.

How to cite: Scala, A., Lorito, S., Romano, F., Abbate, A., Festa, G., Castro Diaz, M. J., Escalante Sanchez, C., and Macias Sanchez, J.: On the relation between seismic source dynamics, tsunami generation and propagation, and numerical modelling complexity in subduction zones, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13511, https://doi.org/10.5194/egusphere-egu23-13511, 2023.

Two prominent near-field tsunamis impacted the nearby coasts of Mexico. The first tsunami was   generated by a major (M_w 8.2) intraplate normal-fault earthquake on 8 September 2017 in the Gulf of Tehuantepec (Chiapas, Mexico). Tsunami waves from this event were measured by a large number of high-resolution coastal tide gauges located along the coasts of California, Mexico and Central America, by three open-ocean DART stations anchored offshore from the affected region and by several distant DARTs. The second tsunami was produced by a thrust fault M_w 7.6 earthquake on 19 September 2022 within the coastal zone of Michoacán, Mexico. The 2022 tsunami was recorded by six coastal tide gauges and a single offshore DART station. All seven instruments were located within 250 km of the source. No tsunami was detected at larger distances along the coasts of North and Central America, but the tsunami signal was detected at the Hawaii and Samoa islands. All available coastal and open-ocean data were used for comprehensive analyses of these two events. Maximum trough-to-crest wave heights for the 2017 tsunami were recorded at Puerto Chiapas (351 cm), Salina Cruz (209 cm), Acapulco (160 cm) and Huatulco (137 cm), while for the 2022 tsunami they were observed at Manzanillo (172 cm) and Zihuatanejo (102 cm). For both events, the “strengths” of the recorded tsunami waves were mostly determined by distance from the source rather than by the specific resonant characteristics of individual sites. Estimates of the frequency content (“colour”) of the two tsunami events revealed that the 2017 tsunami was mostly long-period (“reddish”), with 87% of the total tsunami energy at periods >35 min, while the 2022 tsunami was short-period (“bluish”) with 91% of energy at periods <35 min. A noteworthy feature of the 2022 event was the seismically generated 7 min period seiche observed at Puerto Vallarta that began immediately after the main earthquake shock and persisted for about one hour. Numerical modelling of the events closely reproduced the coastal and offshore tsunami records and demonstrated the markedly different character of the tsunami energy radiation patterns: the 2017 tsunami spread energy widely in a semicircular pattern emanating from the source whereas  the main beam of offshore energy radiating outward from the 2022 event was directed like a “searchlight” oriented normally to the mainland coast.

How to cite: Rabinovich, A. B., Zaytsev, O., Tsukanova, E., and Thomson, R. E.: Two major near-field tsunamis (2017 and 2022) on the coast of Mexico: Observations, spectral properties and numerical modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11090, https://doi.org/10.5194/egusphere-egu23-11090, 2023.

Tsunamis are low-probability phenomena with high-risk potential. Lack of field data emphasizes the need of using simulation software to model the potential devastating effects of a tsunami and use this information to develop safety, sustainable actions and social resilience for the future. These measures may include, among many others, spatial planning; designing of evacuation routes; or the allocation of economic resources through insurance or other instruments to mitigate tsunami impacts. Our work introduces a Monte Carlo-like method for simulating the potential impact of tsunamis on the Spanish coastlines, specifically in the provinces of Huelva and Cádiz for the Atlantic region, and Balearic Islands, Ceuta, Melilla and eastern Iberian coast for the Mediterranean region. The method introduces a pseudo-probabilistic seismic-triggered tsunami simulation approach, by considering a particular selection of active faults with associated probabilistic distributions for some of the source parameters, and a Sobol’s sequences-based sampling strategy to generate a synthetic seismic catalogue. All roughly 4000 crafted seismic events are simulated along the areas of interest in high-resolution grids (five meters pixel resolution) using a two-way nested mesh approach, retrieving maximum water height, maximum mass flow and maximum modulus of the velocity at each grid cell. These numerical simulations are computed in a GPU environment, harnessing resources allocated in several high-performance computing (HPC) centres. The numerical database of retrieved variables generated throughout this study offers an excellent foundation for evaluating various tsunami-related hazards and risks.

The final resulting product focuses on generating frequency distributions for the economic impacts for the Spanish insurance sector (Consorcio de Compensación de Seguros, CCS). The CCS is a public-private entity insuring most natural catastrophic events in Spain. A consistent spatially-distributed economic database regarding insurance building-related values has been constructed and aggregated in conjunction with the numerical tsunami simulations. The proposed procedure allows to associate an economic impact indicator to each source. Further statistical analysis of the economic impact estimators yields to varied conclusions such as an improved definition of worst-case scenario (effect-based rather than worst-triggered), most and least likely economic impact, highest hazardous fault sources overall and locally and many others.

How to cite: Gonzalez del Pino, A., Macías, J., Fernández, M., Llorente, M., Sánchez-Linares, C., García-Mayordomo, J., and Paredes, C.: Estimation of the economic impact of tsunamis on the Spanish coasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12944, https://doi.org/10.5194/egusphere-egu23-12944, 2023.

It is well known that an efficient end-to-end tsunami warning system must not only be fast and robust in delivering alert messages to the authorities, but also ensure that these messages reach the residents and the tourists, and that they are aware of the risk and of the right behavior in case of an alert. One of the most effective tools to reach this goal is through the Tsunami Ready programme, promoted by UNESCO IOC since 2015, and a key contribution to achieving the societal outcome ‘A Safe Ocean’ of the Ocean Decade. The NEAMTWS ICG has solicited Member States efforts towards Tsunami Ready since 2020.

Italy has started to join the Tsunami Ready initiative in 2020. The main steps undertaken in these two years include:

1) The identification of three pilot municipalities that decided enthusiastically to join the programme: Minturno (Lazio), Palmi (Calabria), Marzamemi/Pachino (Sicily) (September 2020)

2) The formal deliberations of the three Local Tsunami Ready Committees ((between December 2020 and April 2021)

3) The establishment of the Italian National Tsunami Ready Board - NTRB (May 4, 2021) and the acknowledgment by IOC Executive Secretary (May 18, 2021).

Since then, several achievements have been reached in all three municipalities, including updating the civil protection plans, improving the local alerting systems, organizing outreach and educational activities in schools and with citizens, also during the World Tsunami Awareness Day (WTAD). At the same time, some criticalities have emerged, due to financial and bureaucratic reasons, that have delayed a full accomplishment until now.

In this contribution, we report on the state of the art in the three municipalities, and discuss the achievements and the criticalities of the programme. We envisage that the first one or two formal candidatures will be advanced later this year to the NTRB.

Finally, we will discuss a proposal to extend the results of this pilot project to all the coastal municipalities in Italy, also based on the analysis of the liability aspects of such recognition in the Italian legal system.

How to cite: Amato, A., Valbonesi, C., Cugliari, L., Graziani, L., and Romano, F.: Tsunami Ready in Italy: towards the UNESCO recognition, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15864, https://doi.org/10.5194/egusphere-egu23-15864, 2023.

The eruption of the Tonga–Hunga underwater volcano in the Central Pacific on 15 January 2022 generated pronounced atmospheric pressure waves that circumvented the globe several times during the next five days. Propagating with a sound speed of ~10 spherical degrees/hour, the pressure waves forced substantial tsunami waves in the Atlantic Ocean that impacted the East Coast of the United States. Almost simultaneously, on 16-17 January 2022, a deep midlatitude cyclone crossed the East Coast. The cyclone, which formed over the northern part of the Gulf of Mexico, began to rapidly intensify as it moved northward. When it reached 40° N, the system produced a pressure change of 36 hPa/24 hours, classifying the cyclone as a “bomb cyclone”. Strong high-frequency (period <4 h) atmospheric pressure disturbances accompanied the cyclone. Both the large-scale atmospheric low and the markedly enhanced pressure disturbance reached their full strengths during the early morning of 17 January 2022 in the proximity of Atlantic City. As a consequence, three hazardous events - storm surge caused by the midlatitude cyclone, a tsunami caused by the Tonga air pressure waves and a meteotsunami caused by the HF atmospheric pressure disturbance struck the US East Coast on 16-17 January 2022, producing cumulative devastating effects in the coastal zone. Severe coastal flooding affected the Atlantic City region, where sea level heights were increased by as much as 150 cm. This unique joint event is examined in detail and the properties of the atmospheric processes and associated sea level response are thoroughly analysed. The contributions from the various sea level components are assessed and their interaction evaluated.

How to cite: Sepic, J., Rabinovich, A. B., Medvedev, I., and Thomson, R. E.: Triple jeopardy: The Tonga tsunami, a storm surge, and a meteotsunami simultaneously hit the US East Coast on 16-17 January 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11778, https://doi.org/10.5194/egusphere-egu23-11778, 2023.

The Hunga Tonga Volcano in the southwest Pacific islands of Tonga erupted in January 2022. The massive explosion resulted in the generation of Lamb waves that propagated globally with a speed of ~ 300m/s and generated a tsunami that has affected numerous Pacific countries. In this study, we use observations and a numerical model to show the impact of this volcanic eruption on the Indian coastline. The Lamb wave took roughly 10 to 11 hours to reach the Indian coast, as observed in atmospheric pressure at mean sea level. Further, the signatures of high-frequency sea-level perturbations were observed from coastal tide-gauge networks along the Indian coastline. Our analysis shows that sea-level oscillations with considerable amplitude (10-20 cm) were observed along the Indian coastline during this period. The predominant frequency and amplitude, and oscillation were different at different locations. Further, an asymmetry between east and west coast stations was observed in the nature of high-frequency oscillations forced by the Hunga Tonga volcanic eruption. Finally, a numerical model was utilised to demonstrate how topography contributes to the observed sea-level disturbances. The model simulations imply that bathymetry is crucial to the observed sea-level variability. Thus, a 12000 km away event has significantly impacted the sea level along the Indian coastline. This work paves the way for understanding the importance of high-frequency variabilities along the Indian coastline and discusses the necessity to enhance the capability of our early warning systems by incorporating these variabilities.

How to cite: Nambiathody, A., Vijayakumaran, V., Balakrishnan, R., Puthiyadath, S., Rose, L., Sabu, A., Kumar B L, S., Krishnapillai Sukumarapillai, K., Anakuzhikkal Sudarsanan, S., and Poikayil Sukumaran, S.: A meteotsunami in the north Indian Ocean triggered by Hunga Tonga volcanic eruption., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-506, https://doi.org/10.5194/egusphere-egu23-506, 2023.

We explored the capability of forecasting meteotsunamis using machine learning (ML) approaches. We selected meteotsunami events along the coast of Portugal where the atmospheric pressure jumps propagate from the south and southwest. Since this type of meteotsunamis is usually observed along the entire coast of Portugal (Kim & Omira, 2021; Kim et al., 2022), the southern tide gauges can act as a meteotsunami precursor for forecasting the northern coastal areas. For training and testing sets of ML, we started with the atmospheric pressure records (18 cases) which induced meteotsunamis, and then performed 1296 numerical simulation by varying the pressure inputs with different strength (jump magnitude), speed and direction. Then, the tidal gauge data from numerical simulations were used to apply neural networks (variational autoencoders and ARIMA) and to demonstrate the capability of meteotsunamis forecast based on one or more tide gauge observations. We observed that the ML models are capable of providing good predictions from short duration observations from the southern tide gauges. This work is supported by the project FAST—Development of new forecast skills for meteotsunamis on the Iberian shelf—ref. PTDC/CTAMET/32004/2017-funded by the Fundação para a Ciência e Tecnologia (FCT), Portugal.

References

Kim J, Omira R (2021) The 6–7 July 2010 meteotsunami along the coast of Portugal: insights from data analysis and numerical modelling. Nat Hazards 106:1397–1419. https://doi.org/10.1007/s11069-020-04335-8

Kim J, Omira R, Dutsch C (2022) Meteotsunamis along the Portugal coast from 2010 to 2019. 2nd World Conference of Meteotsunamis

Liu CM, Rim D, Baraldi R, LeVeque RJ (2021) Comparison of Machine Learning Approaches for Tsunami Forecasting from Sparse Observations. Pure Appl Geophys 178:5129–5153. https://doi.org/10.1007/s00024-021-02841-9

Omira R, Ramalho RS, Kim J, et al (2022) Global Tonga tsunami explained by a fast-moving atmospheric source. Nature 609:734–740. https://doi.org/10.1038/s41586-022-04926-4

How to cite: Kim, J. and Omira, R.: Machine Learning Approaches for Meteotsunami Forecasting on the Coast of Portugal, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16620, https://doi.org/10.5194/egusphere-egu23-16620, 2023.

Subaerial landslides can generate impulsive waves which, in turn, may cause significant damages to the facilities and people on the surrounding coasts. In spite of the numerous studies related to this complex phenomenon in the last decades, there is still a lot to understand, especially physically speaking.

The present work aims at better understanding the energy transformation process from the slide initial potential energy to the final wave train energy. In particular, we would like to emphasize the existence of an optimum energy rate of transformation and investigate the reason for this existence.

To do so, we rely on a Navier-Stokes two or three phases model (OpenFoam) and perform numerical experiments, fixing a few parameters (slope, density, rheology) and studying the effect of the others. The physics of the phenomenon is highly complex, involving liquid phases interaction, transient wave formation, nonlinear wave processes, dispersion, wave breaking, etc. Such a numerical model, despite its inherent uncertainty, is anyway able to provide a rich information, which may be later completed with experimental results. In particular, the model gives access to all the flow variables which allows to characterize in depth the energy processes. The free surface signal analysis is also valuable for wave celerity, and hence generation zone extent and dispersion analysis.

In terms of research strategy, in order to restrict the complexity and allow a better understanding of the phenomenon, the idea is to start with a very simple rheology, the inviscid case, and progressively increase the numbers of rheological parameters (i.e., viscous flows, Bingham and finally Herschell Bulkley).

During the conference, we will first illustrate the existence of an optimum in the rate of energy transformation for the inviscid slide by progressively increasing the slide volume. We will try to relate this optimum with the physical processes at stake (liquid mass interaction, wave breaking types, dispersion, etc.). Next, we will show the influence of the slide rheology in the process of energy transfer and in particular how the energy optimum varies with respect to the rheological parameters.  

How to cite: Parvinashtiani, A., Abadie, S., El Omari, K., and Le Guer, Y.: Energy transfer optimum in subaerial landslide impulse waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6878, https://doi.org/10.5194/egusphere-egu23-6878, 2023.

Under changing climate, coastal regions are increasingly vulnerable to a variety of hazards, including rapid subaerial and submarine landslides. These hazards can generate tsunamis and dense turbidity currents, which threaten both onshore and offshore infrastructure. Due to the complex geomechanics of failure, limited physical modelling has been conducted that encompasses both the triggering of granular landslides and subsequent waves associated with partially and fully submerged mass failures. Further, experimental modelling of submerged failures has primarily focused on the waves generated in the direction of failure (seaward) and not on the waves formed above and behind the failure (shoreward). To this effect, a series of large-scale granular collapse experiments were conducted by releasing 0.75 m and 0.5 m tall columns of 9.25 mm nominal diameter river stone into reservoir depths ranging from 0.20 m to 1.10 m to explore the wave generation and runup processes in both seaward and shoreward directions. The columns were released by a pneumatically-actuated vertically rising gate designed for the 2.10 m wide and 1.20 m high glass-walled flume. The gate lifts rapidly in 0.7 s, which enables the instantaneous loss of support of the source volumes and results in granular collapse. The wave amplitude is measured using wave capacitance gauges and the failure mechanics are captured with high speed cameras. Overall, the wave amplitudes measured in these highly instrumented large-scale physical models are in good agreement with empirical relationships developed in a previous study using smaller-scale models. The large-scale experimental results provide insight and opportunity to develop relationships between the initial column submergence depth and the magnitude of the shoreward propagating waves, which has previously not been explored. Connecting the amplitude of the waves with the tsunamigenic potential for partially to fully submerged granular materials will assist in understanding risk to offshore infrastructure and communities in coastal regions.

How to cite: Treflik-Body, E., Steel, E., Take, A., and Mulligan, R.: Wave generation due to the collapse of partially and fully submerged granular columns in large-scale laboratory experiments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7313, https://doi.org/10.5194/egusphere-egu23-7313, 2023.

Since May 2018, Mayotte island has experienced an important seismic activity linked to the on-going sismo-volcanic crisis. Although variations in the number of earthquakes and in their distribution have been observed since the start of the eruption in early July 2018, a continuous seismicity persists. It could weaken the steep submarine slopes of Mayotte, as highlighted by the high-resolution bathymetry data collected during the MAYOBS cruise in May 2019. This could trigger submarine landslides with associated tsunamis.

To address the hazards associated with such events, we analyzed geomorphological data to define 8 scenarios of potential submarine landslides with volumes ranging from 11,25.10⁶ to 800.10⁶ m³. We simulated the resulting landslide dynamics as well as generated waves (Poulain et al. 2022). In order to estimate the uncertainty associated to the modeling approach, a hierarchy of different model approximations was tested, spanning hydrostatic, non-hydrostatic and multilayer approaches. A sensitivity analysis was also performed by varying the initial released mass, the rheological parameters describing the landslide, its interaction with the water column, the Manning friction coefficient as well as the resolution of the bathymetry description. The combination of all these elements provides an estimate of the uncertainty on simulation results. We show that, in the context of Mayotte, non-hydrostatic effects have the most prominent influence on simulated water elevation and waves velocity. Other key factors include the friction coefficient within the landslide and the resolution of the bathymetry. These results show that landslide-tsunami models should still be improved as well as the estimates of the parameters involved to reduce the related uncertainties on the water wave calculation (water elevation, velocity) that can exceed a factor two.

Poulain, P., et al. (2022). Numerical simulation of submarine landslides and generated tsunamis: application to the on-going Mayotte seismo-volcanic crisis. Comptes Rendus. Géoscience, 354(S2), 1-30.

How to cite: Poulain, P., Le Friant, A., Mangeney, A., Pedreros, R., Grandjean, G., Lemoine, A., Fernandez-Nieto, E., Castro-Diaz, M., and Peruzzetto, M.: Simulation of submarine landslides and generated tsunamis in Mayotte : comparison  of different models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11461, https://doi.org/10.5194/egusphere-egu23-11461, 2023.

How to cite: Dubois, J., Impériale, S., Mangeney, A., and Sainte-Marie, J.: Simulation of the hydro-acoustic and gravity waves generated by a landslide, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15937, https://doi.org/10.5194/egusphere-egu23-15937, 2023.

Tsunamis from landslide and volcanic sources have been responsible for significant destruction and fatalities worldwide as evidenced most recently during the January 2022 Tonga volcanic tsunami (Heidarzadeh et al., 2022: https://doi.org/10.1016/j.oceaneng.2022.112165). Indonesia is a hot spot for such tsunamis from landslide and volcanic sources as the region suffered from destructive events in the past, such as the 1883 tsunami following the Krakatau eruption which costed at least 36,000 lives. More recently the region was struck by the 2018 Anak Krakatau volcanic tsunami with approximately 450 deaths, and the 2018 Palu (Sulawesi) tsunami with more than 4,000 casualties. Therefore, it is vital to further study the generation potential and mechanisms of such tsunamis and to improve hazard knowledge base.

Here, we study three recent tsunamis in Indonesia, two of which occurred following an earthquake while the other one occurred following a volcanic eruption. All three have a landslide component in their sources: the June 2021 Seram Island tsunami (earthquake), the December 2018 Palu tsunami (earthquake), and the December 2018 Anak Krakatau tsunami (volcanic eruption).

A tsunami was observed on 16^th June 2021 in Seram Island following an M_w 5.9 earthquake. The tsunami amplitude was approximately 50 cm at Tehoru tide gauge whereas two other stations showed amplitudes of less than 4 cm. Such a relatively large tsunami (50 cm) is unexpected from a normal-faulting M_w 5.9 earthquake. We hypothesize that that a secondary source (i.e., a landslide) was involved. We applied tsunami modelling and source analysis to examine this hypothesis. Tsunami simulations confirmed that that the earthquake could only have contributed to a few centimeters of the tsunami and thus cannot reproduce the 50 cm waves. However, we could reproduce the tsunami observations using a landslide source. For more information see here: https://doi.org/10.1785/0120210274.   

Regarding the September 2018 Palu tsunami, it is now commonly accepted that a submarine landslide should have most likely contributed to the tsunami generation in addition to the earthquake. However, the nature of the landslide whether submarine or subaerial, and the contribution of the two sources are not clear. We propose a novel dual landslide-earthquake source that explains most of the observation of the 2018 Palu event. Our dual model comprises the USGS earthquake model (length = 264 km, width = 37 km, slip = 0 – 8.5 m) combined with a submarine landslide with a length of 1.0 km, a width of 2.0 km, and a thickness of 80.0 m. For more information see here: https://doi.org/10.1080/21664250.2022.2122293.         

For the December 2018 Anak Krakatau tsunami, we present the results of our field surveys. We surveyed 29 locations and measured tsunami runups from 0.9 m to 5.2 m, tsunami heights from 1.4 to 6.3 m, and inundation distances from 18 to 212 m. For more information, see here: https://doi.org/10.1007/s00024-020-02587-w.

We also discuss future directions towards expanding our limited understanding of tsunamis from landslide and volcanic sources in Indonesia which are often unpredictable and deadly. This research is funded by The Royal Society (UK), grant number CHL/R1/180173.   

How to cite: Heidarzadeh, M., Hilmann Natawidjaja, D., Frederik, M. C. G., Daryono, M. R., Putra, P., Patria, A., Gusman, A. R., and Mulia, I. E.: Significant tsunami hazards in Indonesia from landslide and volcanic sources, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16705, https://doi.org/10.5194/egusphere-egu23-16705, 2023.