Seismic tsunamis are produced from the sea floor dislocation (SFD) due to the earthquake rupture. The size of the SFD depends on the earthquake magnitude, depth and mechanism. The seismic moment, Mot, corresponding to the tsunamigenic SFD, is equal to k∗Mo, where Mo is the entire earthquake moment and k is a coefficient smaller than 1. For a first time, we estimated the coefficient k from published data collected for a set of tsunamigenic earthquakes that occurred in the global ocean from 1990 to 2023. The moment magnitude of these earthquakes ranges from 6.0 to 9.3. No default earthquake mechanism has been adopted. However, all the earthquakes considered are of dip-slip (thrust or normal) or oblique dip-slip types. It has been found that logk increases linearly with the earthquake moment Mo, which implies that the coefficient k increases exponentially with the Mo. For tsunami earthquakes it was found that k has a value larger than its value in regular tsunamis for the same Mo. These results provide a better understanding of the tsunami generation from earthquakes and may open possibilities for estimating the tsunami magnitude at the source.

How to cite: Triantafyllou, I., Imamura, F., and Papadopoulos, G.: Global investigation of the tsunamigenic dislocation of the seismic fault, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14374, https://doi.org/10.5194/egusphere-egu24-14374, 2024.

The initial conditions for the numerical simulation of a seismically-induced tsunami are modeled by transferring the impulse produced by the co-seismic seafloor deformation to the sea-surface. In this process, the water column acts as a hydraulic filter for the smaller wavelengths. The numerical simulation of this process is computationally demanding; this makes  the application of this filter unaffordable in studies that require a large number of simulations, such as the long-term probabilistic tsunami hazard assessment (PTHA). Here, we optimize the numerical modeling of the filter in the case of an instantaneous vertical seafloor deformation, given by an improper Fourier expansion integral in the wave number domain presented by Nosov and Kolesov (PAGEOPH, 2011); the contribution of elementary seafloor displacements can then be linearly combine to obtain a static tsunami initial condition. We first explore the convergence of the integral in one dimension, to identify the range of wavenumbers significantly contributing to the integral. We find that its support can be limited to , being H the sea-depth. We then compare several quadrature formulae, selecting the optimal one in terms of accuracy and efficiency. We grid the domain into cells of equal size and constant depth, and verify that the nonlinear effects are negligible when we recombine them to obtain the initial sea level displacement. In two dimensions, the integral is solved with the optimal quadrature and the results tested on the tsunamigenic Kuril doublet sequence - a megathrust and an outer-rise - occurred in the Central Kuril Islands in late 2006 to early 2007. We also consider the horizontal co-seismic deformation projected on a slope and a simple model of the inelastic deformation of the wedge, on a realistic bathymetry. The approach proposed results accurate and fast enough to be relevant for practical applications, taking a few seconds for solving a single cell, depending on the local depth, and ~3min to recombine ~91k elementary initial conditions. We finally build a database of elementary initial conditions, as a function of the local sea depth, which can be linearly combined to obtain a discretization of any sea floor displacement globally.

How to cite: Abbate, A., González Vida, J. M., Castro Díaz, M. J., Romano, F., Bayraktar, H. B., Babeyko, A., and Lorito, S.: Modeling optimal initial conditions for propagation of seismotectonic tsunamis: a database of smoothed unit sources based on an efficient and accurate numerical integration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8274, https://doi.org/10.5194/egusphere-egu24-8274, 2024.

Tsunami maximum inundation heights (MIH) are normally estimated by modelling the hydrodynamics of the overland flow over a limited area using high resolution long wave models. However, in situations where there one need to simulate inundation over large areas, we demand methods that are less resource intensive. The method of amplification factors represents one such simplified and resource efficient method. It is based on precomputed factors using linear shallow water models modelled over bathymetric transects to estimate the ratio of the offshore maximum surface elevation to that at the shoreline. It has been shown previously that we can get a relatively good estimate of the median value of the MIH at given coastline location using amplification factors. Yet, as this method is associated with considerably higher epistemic uncertainty than e.g. solving the nonlinear shallow water equations (due to the additional simplifications), there is need to include a measure of its uncertainty and bias. The main sources of uncertainty include among others the misfit of the method (towards data or more accurate methods) and local spatial variability of the inundation. Here, we present results from recent advancements of the amplification factor method emphasising a much more elaborate uncertainty analysis than employed previously. To this end, we use a unique set of synthetic data from several hundreds of thousands of massive scale nonlinear shallow simulations as a benchmark for estimating uncertainty concerned the amplification factors. The simulation dataset comprises ensembles of about 50 000 scenarios each for six different sites and includes sensitivity studies related to the Manning friction. When analysing the entire ensembles, we have revised the previous mathematical model for the uncertainty treatment (Glimsdal et al., 2019, PAGEOPH) by normalising the ensemble scenario outputs with the median MIH from each simulation. We further measure the bias of the amplification factor method by measuring its offset towards median MIH output from the shallow water simulations. The model provides input to the probabilistic tsunami hazard (PTHA) map for Italy, and we present related results of the amplification factor uncertainty analysis here. We will also discuss further and advocate the use of the method, both for long term hazard (PTHA) and for rapid post assessment following an event, e.g. through the ARISTOTLE framework. This work is supported by the European Union’s Horizon Europe Research and Innovation Program under grant agreement No 101058129 (DT-GEO, https://dtgeo.eu/). Computational resources were made available through PRACE grant number 2020225386, TsuHazAP.

How to cite: Løvholt, F., Briseid Storrøsten, E., Glimsdal, S., Norderhaug Drøsdal, I., Gibbons, S. J., Magni, V., Romano, F., Lorito, S., Volpe, M., and Brizuela, B.: Uncertainty based amplification factors benchmarked by large ensembles of inundation simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5650, https://doi.org/10.5194/egusphere-egu24-5650, 2024.

Following the 2022 Hunga Tonga - Hunga Ha'apai tsunami, there is a renewed interest in assessing tsunami hazards related to tsunamis triggered by landslides, volcanic eruptions, and atmospheric disturbances. This increased interest suggests an expanding cohort of researchers delving into the assessment of tsunami hazards through numerical modelling. However, mastering a numerical model involving tasks such as input file preparation, simulation execution, and output data generation poses a challenge for inexperienced users. The learning process often demands a substantial time investment, potentially causing workflow delays that may prevent researchers from promptly initiating result analysis. To address this challenge, we introduce standalone applications designed to optimize the efficiency of both tsunami model preparation and post-processing stages. We present two MATLAB-based user-friendly applications designed to efficiently generate input files and output tsunami hazard maps. The applications were designed to align with the required input and expected output files of the Fully Nonlinear Boussinesq Wave (FUNWAVE) model. FUNWAVE is a well-established open-source model that has been extensively validated through analytical solutions and experimental investigations. It also offers options to set up initial conditions, such as landslides and meteotsunamis. To facilitate its useability, the applications incorporate tool tips and context menus that provide a comprehensive guide for users. Within the input-generator application, visual warnings pre-empt potential errors in tsunami simulations. Meanwhile, the output map generator application not only facilitates the creation of maps, but also offers users the convenience of converting these maps into raster files,  animations, KML, or shapefiles. This versatility ensures compatibility with various programming and Geographic Information System (GIS) platforms. We tested the functionality of the applications using the benchmark examples from the FUNWAVE model. Through the development of these applications, we aim to advance tsunami modelling research by enhancing technological accessibility, hence reducing the complexity, especially for individuals new to tsunami modelling.

How to cite: Felix, R., Watanabe, M., Verolino, A., Tan, E., Puah, J. Y., and Switzer, A.: Advancing User-Friendly Tsunami Hazard Mapping:  MATLAB-based Applications for FUNWAVE modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13955, https://doi.org/10.5194/egusphere-egu24-13955, 2024.

     Granular avalanches that enter a body of water present a hazard to coastal communities via their ability to generate tsunami waves. By improving the accuracy of model estimates of tsunami severity and quantifying the error of said models, we can improve the accuracy of tsunami hazard assessments. Sensitivity analyses were performed of the hybrid finite-difference finite-element model HySEA to changes in the number of vertical layers used to discretize the water column (from a single layer up to 5 layers), non-hydrostatic vs hydrostatic fluid pressures, different friction rheological laws (Pouliquen and μ(I)), and various friction coefficients. Flume experiments with matching conditions were used to benchmark simulations. An improved fit between simulation and experiment wave heights and wave frequencies, as well as improved model stability was found with 3 to 5 vertical water discretization layers compared to using a single water layer. We also demonstrate an improved fit between simulation and experiment wave heights, speeds, and form with non-hydrostatic versus hydrostatic conditions, although most simulations performed here did not accurately estimate wave speeds. Only minor changes in fit were observed between the Pouliquen and μ(I) friction laws. We demonstrate that multilayer HySEA can reliably estimate tsunami wave heights with a reasonable certainty (< 38% error) without any fitting across different granular mass volumes, grain sizes, and slopes of flow and can reach errors < 2% with only limited fitting. Furthermore, we detail how to improve model fit using additional rheological information (e.g., grain size, slope, volume of mass) and friction coefficients.

How to cite: Silver, M. M. W., Marboeuf, A., Mangeney, A., Le Friant, A., Fernandez Nieto, E. D., Belieres-Frendo, A., Bougouin, A., Roche, O., Paris, R., and Moatty, A.: Multilayer HySEA granular flow and tsunami model improves results over single layer models: sensitivity analysis, benchmarking against flume experiments, and implications for field scale models., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3383, https://doi.org/10.5194/egusphere-egu24-3383, 2024.

The Shetland Islands (UK) are a seminal location for investigating palaeo-tsunami deposits. Onshore evidence suggests three tsunamis have occurred during the Holocene: the Storegga tsunami ca. 8175 cal yr BP, the Garth tsunami ca. 5500 cal yr BP and the Dury Voe tsunami ca. 1500 cal yr BP. However, to date no research has been published on the impact of tsunami on the subtidal shelf where a large amount of North Sea hydrocarbon infrastructure is located. During the SEACHANGE research cruise DY150 (2022), cores were recovered offshore east Shetland from the Fetlar Basin. The cores contained distinct sand and shell lenses within a Holocene mud sequence, indicating increases in bed shear stress. We test the hypothesis that these lenses represent the subtidal expression of North Sea tsunami. Radiocarbon dates bracketing the sand lenses overlap with the published dates for the Storegga tsunami, suggesting these sand lenses result from processes related to the Storegga tsunami. Dates within the deposit are older than the Storegga tsunami, indicating reworking and deposition of older sediments at the core site by the tsunami. Particle size analysis, ITRAX and MSCL data evidence increases in grain size, a reduction in sorting capacity, increased shell concentrations and peaks in associated elements (log(Ca/Fe), log(Ca/Ti) and Sr) and magnetic susceptibility. These attributes are typical of both palaeo and modern offshore tsunami deposits. No evidence was found within the cores for any later Holocene tsunami, due to either bioturbation, active currents, or lack of initial deposit. The data indicate that sediments in the Fetlar Basin were disturbed by the Storegga tsunami to palaeo-water depths of at least 88 m. This highlights the need to assess the potential impact of any future tsunami on existing or proposed hydrocarbon infrastructure.

How to cite: Earland, J., Scourse, J., Ehmen, T., and Kender, S.: Identification of the Storegga Tsunami offshore Shetland: implications for seabed infrastructure hazard risk assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11569, https://doi.org/10.5194/egusphere-egu24-11569, 2024.

The atmospheric tsunami resulting from the eruption of the Tonga volcano on January 15, 2022 (Tonga22) marked an unprecedented occurrence, encompassing a Volcano-Meteorological Tsunami (VMT) with global ramifications. This study examines the comprehensive effects of Tonga22 on moored vessels, employing a spectral and hydrodynamic analytical framework. The aftermath of the event, including edge waves, resonance phenomena, and wave amplification in specific regions such as La Pampilla port in Peru, revealed substantial maritime challenges. Notably, a vessel in La Pampilla reported the rupture of mooring ropes, a remarkable incident occurring 10,000 kilometers away from the Tonga volcano, manifesting 15 hours post-eruption and resulting in the spillage of over 11,000 barrels of crude oil.

Our research aims to contribute to a nuanced understanding of the Tonga22 event by employing advanced spectral and hydrodynamic analyses. The primary focus lies in assessing its impact on mooring loads within the complex marine port environment. We postulate that atmospheric acoustic waves, a consequence of the volcanic eruption, pose hydrodynamic threats to vessels in port areas, potentially leading to mooring breakage.

Utilizing the Boussinesq model, validated at the local scale in Callao Bay, we establish a foundation for our mooring system model. This model, applied to a vessel analogous to the one docked at La Pampilla Port, aims to discern the nuanced influence of VMT on overstressing and mooring breakage during the Tonga22 event.

Our simulation results underscore the pivotal role of VMT in the displacement and loss of positioning of vessels. Moreover, atmospheric waves are revealed to significantly elevate mooring stresses, with a particular emphasis on the starboard quarter moorings in this specific case.

This research sheds light on a critical realization—the Tonga22 event highlights the inadequacies of existing tsunami early warning systems (TWC) in detecting and managing tsunamis induced by acoustic waves originating from volcanic sources. These findings contribute to the ongoing discourse on maritime safety and hazard preparedness.

How to cite: Padilla Álvarez, S., Quiroga, I. A., Gonzalez, M., Omira, R., Kim, J., and Baptista, M. A.: The Impact of Volcano-Generated Tsunamis on the Safety of Moored Vessels: The 2022 Tonga Incident., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20633, https://doi.org/10.5194/egusphere-egu24-20633, 2024.

Understanding the generation of tsunamis from landslides at volcanic islands is crucial due to their infrequent, yet potentially catastrophic, impact on coastal communities. We present a sensitivity analysis of the effects of different rheological and geometrical landslide parameters on the generation and propagation of tsunamis in the near field. In particular, we employed the MultiLayer-HySEA model to simulate tsunamis generated by landslides occurring along the northwestern flank of the Stromboli volcano, specifically in the area known as Sciara del Fuoco, which is considered most prone to instability. This shallow-water model implements a two-way coupling between a granular material layer representing the landslide and 3 fluid layers representing the water. The parameters investigated include the initial position, density, volume, and shape of the landslide, as well as its friction angles and water-landslide friction coefficients. We varied each landslide parameter to examine its effect on the tsunami wave height and energy at specific locations. We found that the principal parameters of the synthetic waveforms and the landslide volumes are logarithmically correlated when considering subaerial landslides, while they correlated linearly when considering submarine landslides. We than explored the effect of the other source parameters on such analytical relationships. Based on the variability observed in waveform characteristics, we suggest a ranking of importance for the source characteristics that contribute significantly to the uncertainty/variability of the model output. Our study aligns with previous predictions at Stromboli and offers a valuable tool for reconstructing the source parameters of tsunamis based on proximal sea-level measurements, enabling rapid forecasting of subsequent impacts around the volcanic island.

How to cite: Trolese, M., Cerminara, M., Esposti Ongaro, T., de' Michieli Vitturi, M., and Tadini, A.: Modeling tsunami generation and propagation: Insights from sensitivity analysis of landslide parameters at Stromboli, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19260, https://doi.org/10.5194/egusphere-egu24-19260, 2024.

We study experimentally tsunami-induced transport of micro-plastic. The micro-plastic is modelled by spheres of different densities, some of which are lying on the bottom slope, while others are floating. The bottom slope is covered with the sand, which allows us to study micro-plastic interaction with sand of different sizes. The spheres are initially placed at different locations along the slope with respect to the wave breaking point. Experiments are performed in a small wave flume of the Hydrodynamics laboratory of the University of Oslo. It is 3 m long and 0.1 m wide. The water depth is 5 cm. The tsunami is modelled by breaking solitary-like waves with amplitude, normalized by the water depth 𝑎/ = 0.47. The waves propagate towards a sandy beach breaking on the slope, impact the floating and/or lying on the bottom spheres, and the spheres get displaced. Here we study the displacement of the spheres from their initial position with respect to their characteristics (densities), initial positions with respect to the wave breaking point, the number of consecutive waves and parameters of the sand.

How to cite: Su, P.-T., Didenkulova, I., and Jensen, A.: Experimental study of tsunami-driven transport of micro-plastic on sedimentary slope, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9919, https://doi.org/10.5194/egusphere-egu24-9919, 2024.

Meteorological tsunamis - atmospheric ocean waves in the tsunami frequency band - and generally nonseismic sea level oscillations on tsunami timescales attracted a lot of attention in the recent decade due to the global availability of high-resolution sea level and ancillary measurements and advancement of both atmospheric and ocean models. This became even accentuated after the century-level event of the Hunga Tonga-Hunga Ha’apai explosive volcano eruption on 15 January 2022, which created global acoustic-gravity waves in the atmosphere and meteotsunamis in the ocean. In that spirit, the Global Science of Meteorological Tsunamis (GLOMETS) 4-year project has been proposed for funding to the Croatian Science Foundation and launched on New Year’s Eve of 2023 to tackle the following research topics: (1) global meteotsunami hazards from explosive volcanic eruptions and asteroid impacts, (2) meteotsunami hazards at the sub-kilometre scale from both weather- and explosive volcano-induced events, (3) reproducibility of meteotsunami hazard by climate models, for their eventual assessment in the future climate, (4) eventual optimization and improvement of the meteotsunami monitoring, and (5) developing stochastic techniques for meteotsunami uncertainty quantification. To achieve these objectives, state-of-the-art tools will be used, like (1) global quality-checked high-frequency sea level analyses, (2) coupled atmosphere-ocean global and (sub-)kilometre models, (3) climate simulations, reanalyses, and products, and (4) uncertainty quantification techniques and optimal experimental design methods. This presentation will overview state-of-the-art in the quoted topics, with planned work-to-do and research activities, hopefully to initiate fruitful discussions and new research directions and to establish new collaborations around the project.

How to cite: Vilibić, I., Dogan, G. G., Dominović Novković, I., Huan, X., Jordà, G., Pranić, P., Tojčić, I., Villalonga, J., Yalciner, A. C., and Zemunik Selak, P.: Global Science of Meteorological Tsunamis: From Planetary to Mesoscale Processes (GLOMETS), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4024, https://doi.org/10.5194/egusphere-egu24-4024, 2024.

Atmospherically-driven tsunamis or meteotsunamis are generated by atmospheric disturbances with steep gradients of pressure and/or wind. In recent years, meteotsunamis have received more attention from the tsunami modelling community. Although their destructive potential might be less severe than for earthquake or landslide triggered tsunamis, their frequency is much higher. The two main processes driving the most extreme meteotsunami events are the offshore amplification of the ocean long-waves due to Proudman or Greenspan resonances (i.e., when the atmospheric disturbance travels at the same speed than the long-waves) and, nearshore, the amplification factor of the shelfs, bays or inlets (i.e., resonance frequency associated to the nearshore geometry). As meteotsunamis have a high-dependence on the nearshore geometric characteristics, they often occur at known hotspot locations such as along the coastlines of Croatia, the Balearic Islands, Sicily, Malta, the Nagasaki Bay or the Baltic Sea. One of the most devastating meteotsunami events took place in Menorca (Balearic Islands) in 2006, where tsunami-like oscillations caused an economic loss of several tens millions of euros.

The EDANYA group from the university of Málaga is widely known for its GPU-accelerated tsunami simulation codes, such as Tsunami-HySEA (earthquake source) or Landslide-HySEA (landslide source). Here, we present our brand-new code for simulating meteotsunamis following the same philosophy as the previous codes. Meteotsunami-HySEA incorporates the atmospheric forcing together with additional terms such as Coriolis and the wind drag. The PDE system is written in spherical coordinates and implemented in CUDA. An additional feature related to preserving a linear version of the quasi-geostrophic equilibrium is added to the numerical scheme in order to preserve the structure of geostrophic flows, as large scale geophysical flow are often perturbations of this steady state.

To demonstrate the Meteotsunami-HySEA reliability, we first applied the code to some carefully-crafted benchmark tests, where Proudman resonance is exhibited and precisely capture, enabling accurate measures of the amplification gain due to the coupling of the propagation velocities. Then we followed the NTHMP’s guidelines for simulating a pilot study in the region of the Gulf of Mexico with a real topobathymetry. Further work will be focused on testing real scenarios, incorporating real atmospheric data and bathymetry for reliable forecast.

Acknowledgments: This contribution was supported by the EU project “A Digital Twin for Geophysical Extremes” (DT-GEO) (No: 101058129) and by the Center of Excellence for Exascale in Solid Earth (ChEESE-2P) funded by the European High Performance Computing Joint Undertaking (JU) under grant agreement No 101093038.

How to cite: Gonzalez del Pino, A., Macías Sánchez, J., Castro Díaz, M., and Lumina Denamiel, C.: Meteotsunami-HySEA: A GPU accelerated code for simulating atmospherically-driven tsunamis on real bathymetries. Benchmark tests., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8317, https://doi.org/10.5194/egusphere-egu24-8317, 2024.

Volcanic meteo-tsunamis are rare and potentially devastating natural phenomena. In physical terms, they are comparable to meteorological tsunamis, where a relatively high atmospheric pressure disturbance leads to the formation of tsunami-like waves. A significant recorded example of volcanic meteo-tsunami is the one produced by the Hunga Tonga – Hunga Ha’apai (HT-HH) eruption in January 2022. Volcanic meteo-tsunamis have the peculiarity to generate waves that propagate beyond landmasses, due to the interaction between the air pressure wave and water, such as those observed in the Gulf of Mexico following the HT-HH eruption; they also move much faster than tsunamis generated by other mechanisms. These features increase the hazard potential of a given volcano, and expose countries that are generally protected by landmasses to tsunami waves. The South China Sea (SCS), for example, is relatively protected to the west and south from Indonesia, to the north from Taiwan, and to the east from the Philippines. However, volcanic meteo-tsunamis may be generated from a volcanic eruption from regions such as southern Japan, and affect the SCS and its surrounding coastlines. Southeast Asia (SEA) presents several records in the literature of volcano-induced tsunami events, including as source mechanisms landslides, Pyroclastic Density Currents, lava dome collapse, and underwater explosions. There are also two instances from Taal, Philippines, and Krakatau, Indonesia, where airwaves have been inferred as a possible tsunami source mechanism, with waves reported also across the Indian and Pacific Oceans, in the latter case. Here, we selected four potential candidates for a submarine or near-surface volcanic eruption, both outside (Kikai and Fukutoku-Oka-no-Ba, Japan) and inside the SCS (Banua Wuhu, Indonesia, and KW-23612, Vietnam), capable of generating volcanic meteo-tsunamis, with the aim to have a first-time assessment from such natural phenomena on SEA countries surrounding the SCS. At this stage, we focused on the general wave propagation in the region, based on the different source locations, and offshore wave maximum height (observed at 16 synthetic tide gauges placed around the SCS, at the 50-m water depth contour, to avoid shallow water complexities near coastlines that cannot be resolved through public bathymetry datasets). We modelled three potential scenarios for each selected seamount, with 100%, 66% and 33% of the HT-HH eruption intensity, respectively. This choice allows us to investigate a broad range of explosion intensities expressed through a perturbance of the atmospheric pressure field, following previous works. Initial results from this first assessment show that bathymetry has a strong control on tsunami wave propagation, being rather fast in deep waters (e.g. northern South China Sea) and much slower in shallow waters (e.g. Sunda Shelf). The higher waves are recorded at offshore stations 11 (Hong Kong) and 16 (West Philippines), with ~10 and 20 cm respectively, in both cases generated from within the SCS from seamount KW-23612. Work is ongoing to integrate higher resolution grids near these locations closer to coastlines, and also to assess the hazard at other areas on the Sunda Shelf where water depth is larger in proximity of coastlines (e.g. Singapore Strait).

How to cite: Verolino, A., Watanabe, M., Felix, R., Conway, C., and Switzer, A.: Volcanic meteo-tsunamis in Southeast Asia: A first-time assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4947, https://doi.org/10.5194/egusphere-egu24-4947, 2024.

Meteotsunamis are sea waves with frequencies ranging from 2 minutes to 2 hours (the same frequency band than seismically generated tsunamis) that are generated by high frequency atmospheric perturbations. Extreme meteotsunamis can cause high frequency sea level oscillations of a few meters at the coast which are dangerous for coastal populations. The meteotsunami generation involves both the amplification of the inverse barometer response by Proudman resonance and also harbour resonance. For this reason, meteotsunami generation is highly dependent on the bathymetric and topographic characteristics of the basins, some of them being more suitable than others for the meteotsunamis occurrence. This is the case of the Ciutadella harbour, in the Balearic Islands (Western Mediterranean), where several meteotsunamis of >1 meter of amplitude (difference in the sea level elevation between consecutive maximum and minimum) occur every year. This hotspot for meteotsunamis has been studied for four decades and several forecasting systems have been implemented in the region. However, the accuracy of those systems, in particular in terms of predicting the amplitude at the coast, is still limited.

In a previous work (Villalonga et al., 2022), a new set of ultra dense atmospheric observations allowed a detailed characterization of the atmospheric disturbances generating meteotsunamis.  We found that these disturbances are highly heterogeneous both in time and space, and that heterogeneity is very difficult to reproduce with atmospheric models. With the aim of understanding what is the impact of that heterogeneity in the final meteotsunami amplitude, we have conducted several experiments with the SYMPHONIE model (Estournel et al., 2021). The model has been configured to cover the whole Balearic Islands with a variable spatial resolution reaching up to 5 meters inside Ciutadella harbour. It has been forced with different kinds of atmospheric disturbances, namely analytical functions and observed time series with tuneable propagation velocities over the model’s domain. Random noise with different spectral and spatial characteristics has also been added.

The model has been able to accurately reproduce the amplitude and spectra of real meteotsunamis events when forced with the observed atmospheric pressure time series. We have also tested the sensitivity of the model outputs to different model configurations by changing the friction parameters and comparing 2D to 3D simulations. The results suggest that 3D simulations provide a more realistic energy dissipation by friction, particularly in the higher frequencies. Finally, the experiments forcing the model with random spatially correlated noise have allowed understanding the impact of atmospheric spatial heterogeneities. In particular, the results show that the size of the random structures is a key parameter that determines the amplification of sea level oscillations. Namely,  the structures with a spatial scale of 10-30 km generate more signal amplification than larger structures.

All in all, these simulations have provided new results that will allow a better understanding of the generation of meteotsunamis in the Balearic Islands and the limits of their predictability.

How to cite: Villalonga, J., Marsaleix, P., Gomis, D., and Jordà, G.: Influence of spatially correlated noise in the generation of meteotsunamis in Ciutadella, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16257, https://doi.org/10.5194/egusphere-egu24-16257, 2024.

Tsunamis have the potential to cause catastrophic damage to coastal communities. In Aotearoa New Zealand, where 3.5 million people reside within 5 km of the coast, the threat of experiencing a tsunami within their lifetime is a stark reality. Although these events are infrequent with recurrence intervals of hundreds of years, New Zealand faces an elevated risk due to its location within the tectonically active Pacific, where over 80% of the world's tsunamis occur. The region has experienced over seventy tsunamis in the past two-hundred years, with five of these causing devastating impacts to coastal communities and leaving an indelible mark on the landscape due to wave amplitudes surpassing 5 m at the coast. Recent studies, while crucial, have predominantly focused on assessing the tsunami hazard from local sources, recognising their immediate threat. However, to comprehensively assess the overall tsunami hazard to Aotearoa, we must fully account for the regional and distant sources also. This is informed by the harsh reality that some events, such as the 1877 Northern Chile and the 2004 Indian Ocean tsunamis, have inflicted staggering death tolls in distant locations, emphasising their paramount significance in our hazard assessment efforts.

In this talk, I will present our innovative hybrid tsunami hazard model designed for Aotearoa New Zealand. We use observations of accumulated earthquake slip on active faults in the Pacific alongside established earthquake laws to ensure that we capture a wide variability of seismogenic tsunami sources to complement the limited historical and instrumental records. Due to recent computational advancements, we can now calculate the seafloor deformation generated from hundreds of synthetic tsunami sources across twenty subduction zones and simulate the tsunami wave propagation to the coast of New Zealand. For each source, we can estimate the wave amplitudes and timing of potential tsunamis and use these metrics to calculate the hazard that these regional and distant sources pose over common return periods. Each part of the model, from the source characteristics to the wave propagation has been independently tested and benchmarked with recorded events to ensure the rigor of the research.

Our hybrid approach of blending observation-driven, physics-based, and probabilistic methodologies offers a comprehensive approach to assessing the full range of earthquakes that could cause a tsunami at the shores of New Zealand. Our work, alongside the recent research carried out on the local tsunami sources will accelerate Aotearoa New Zealand’s natural hazard resilience from Pacific earthquake-generated tsunami sources and will pave the way for other tsunami mechanisms to be incorporated into the model analysis, an urgent need given that these hazardous events do not occur independently. We look forward to having the opportunity to share our Aotearoa New Zealand tsunami hazard model with the wider tsunami community in Europe and discuss pathways that our combined research could follow to help build safer and more resilient coastal communities, globally. 

How to cite: OKane, A., Fry, B., King, C., and Nicol, A.: Building resilient coastlines: A comprehensive physics-based tsunami hazard model for Aotearoa New Zealand, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14623, https://doi.org/10.5194/egusphere-egu24-14623, 2024.

Scenario-based tsunami risk assessment provides valuable insights into the socio-economic consequences of a specific scenario. Although it is focused on a specific scenario or a range of scenarios, this type of analysis can still encompass uncertainty characterisation. 

Hazard: Focusing on Coquimbo Bay, which was affected by the 2015 Ilapel tsunami, we demonstrate how the uncertainties are quantified and propagated from the tsunami source level all the way towards risk metrics such as the economic losses.  We have chosen a range of near-field tsunami scenarios with moment magnitude between 8.6 <Mw <9.3 from a subduction interface zone on the Nazca–South American plate interface running parallel to the Chilean coastline. We have worked with a large set of stochastic scenarios generated compatible with the scaling laws, with variable slip distribution according to a prescribed correlation structure. We have estimated the seismicity rate through different sources: paleo seismic data, historical catalogue, and moment balancing and have combined the resulting probability distributions through a logic tree approach. The weights of the logic tree are assigned through a Bayesian model class selection procedure as related to the log-evidence calculated for each model. This will lead to scenario-based hazard curves with confidence intervals for different points of interest in the port city of Coquimbo.

Vulnerability: Based on the exposure model for Coquimbo, we have identified two different pre-dominant building categories in Coquimbo, namely the low-rise mixed (wood and masonry) building type and the high-rise residential buildings. For the first category, we have used empirical fragility curves for a similar building type in Dichato and damaged by the Chile 2010 and have characterised the epistemic uncertainty for these fragility curves. We have derived the vulnerability curves and their confidence band through convolution of the fragility curves and the consequence models. 

Risk: We demonstrate how hazard and vulnerability curves and their confidence intervals can be convolved to obtain loss curves for certain locations of interest and how this information can be processed to derive scenario-based risk maps for Coquimbo for different return periods. 

We conclude by demonstrating the importance of a thorough characterization of uncertainties and their propagation from the tsunami source towards the estimation of the economic losses. This provides important insights about the relative sensitivity of tsunami risk to different sources of uncertainty.

How to cite: Jalayer, F., Ebrahimian, H., Catalan, P., Zamora, N., and Soltani, S.: Scenario-based Probabilistic Tsunami Risk Analysis for Coquimbo Bay, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17621, https://doi.org/10.5194/egusphere-egu24-17621, 2024.

The digital twin is recognized as digital copies of the physical world's objects stored in digital(cyber) space and utilized to simulate the sequences and consequences of target phenomena. Users can fully view the target through real-time feedback by incorporating the physical world's data into the digital twin. Given the importance of the digital twin, the authors propose "Tsunami Digital Twin (TDT)" as a new paradigm in tsunami science and engineering to enhance tsunami disaster resilience.

The components of TDT are the transformation from "Data" to "Information" by integrating sensing, monitoring, and simulation; "Interpretation" of data and information; and "Inference" by using available data and information to draw conclusions and consequences and decide policies and responses for social resilience. Fusing these components is the key to gaining knowledge and insight for optimal solutions in the physical world.

In the session, the authors focus on two functionalities of TDT: Real-time tsunami modeling and forecast capability and Dynamic exposure estimation to verify through the 2024 Noto Peninsula Earthquake Tsunami Disaster.

The rapid tsunami hazard assessment by real-time tsunami modeling implied that severe impacts were expected around Noto Peninsula (Shika to Nanao), and the directivity of tsunami energy was also toward the Japan sea coasts, especially Joetsu city, Niigata Prefecture. We also found that the specific bathymetric features (continental shelf of Noto Peninsula) are responsible for high tsunamis in Suzu city.

The exposure analysis was performed using Mobile Spatial Statistics (the population estimates using mobile phone data) to elucidate population change after the earthquake by elevation (tsunami affected or not).

How to cite: Koshimura, S., Adriano, B., Mas, E., Nagata, S., and Takeda, Y.: Tsunami Digital Twin – Concept, Progress, and Application to the 2024 Noto Peninsula Earthquake Tsunami Disaster, Japan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14673, https://doi.org/10.5194/egusphere-egu24-14673, 2024.

On January 1, 2024, a large earthquake (Mw7.6) occurred along the northern coast of the Noto Peninsula, Japan. Because the faults of the earthquakes were located beneath both land and sea, the large strong motion and tsunami were generated and caused severe disasters near the source area. More than 200 people were killed by the earthquake.

The earthquake occurred on the Eastern Margin of the Japan Sea where several great earthquakes and tsunamis occurred previously such as the 1993 Hokkaido Nansei-oki earthquake (Mw7.8), the 1983 Japan Sea earthquake (Mw7.7), and the 1964 Niigata earthquake (Mw7.6), The 2024 Noto earthquake also occurred on the same Eastern Margin of the Japan Sea where a large number of submarine active faults were identified by the undersea structure surveys and also the GNSS surveys indicted a convergence rate of approximately 1cm/year along the margin. 

The aftershock activity and the seismological analysis by the Japan Meteorological Agency (JMA) and the co-seismic deformation analysis using GNSS data by the Geospatial Information Authority of Japan (GSI) of the 2024 Noto earthquake showed that the fault length is about 150 km. Particularly, a northeast part of the fault was extended to the Japan Sea where the Noto peninsula was terminated. The co-seismic deformation due to the faulting generated a large tsunami observed at several tide gauges along the Japan Sea coast.

How to cite: Tanioka, Y. and Yamanaka, Y.: Characteristics of the 2024 Noto Earthquake and Tsunami occurred in the Eastern Margin of the Japan Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22502, https://doi.org/10.5194/egusphere-egu24-22502, 2024.

Forecasting the impact of a tsunami on coastal areas requires accurate location of the source of the tsunami. This is particularly challenging because tsunamis often originate far from seismological and geodetic networks. However, tsunami waves often induce total electron content (TEC) perturbations in the ionosphere, which can be detected using Global Navigation Satellite Systems (GNSS). Tracking the source of these perturbations makes it possible to determine the tsunami source area. Previous studies have confirmed this approach. However, this is usually done by (1) using a "quasi-homogeneous" model to propagate the disturbances in the atmosphere and (2) arbitrarily fixing the height of the ionosphere. These approximations lead to relatively large uncertainties in the location of the tsunami source. Therefore, in this study we try to reduce these uncertainties by using a 1D model of the atmospheric structure and by including the search for the optimal height of the ionosp here in the inverse problem. To do this, we use a Bayesian approach to invert the onset times of the TEC disturbances. First, we test our method on synthetic data to determine the potential gain in accuracy between using a "quasi-homogeneous" and a 1D model of the atmosphere. We then apply our approach to study the 2010 M7.7 Mentawai tsunami earthquake and discuss the strength and limitations of the method as well as its usability for tsunami warning. We finally show preliminary results for the 2024 M7.5 Noto peninsula earthquake, and related tsunami offshore Japan.

How to cite: Twardzik, C., Rolland, L., Munaibari, E., and Mikesell, T. D.: Constraining the tsunami initiation area associated with the 2010 M7.7 Mentawai and 2024 M7.5 Noto peninsula earthquakes from first-arrival measurements on TEC data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21337, https://doi.org/10.5194/egusphere-egu24-21337, 2024.

The Vanuatu Trench hosts tsunamigenic earthquakes exceeding magnitude 7. For these source locations, current tsunami early warning systems in Aotearoa - New Zealand are based on earthquake point-source parameters and as tsunamis propagate, initial forecasts are refined by DART (Deep-ocean Assessment and Reporting of Tsunamis) sea-level analysis. This study, which is part of the R-CET (The Rapid Characterization of Earthquakes and Tsunami) project led by GNS Science, aims to seismologically characterize the spatio-temporal behavior of the regional tsunamigenic ruptures in near real-time. This transition from basic point sources to 4-D rupture propagation could enhance the accuracy of initial tsunami threat maps and hazard response. A new array of broadband seismic stations, designated as the R-CET array, has been strategically deployed in New Zealand to support this analysis for events in the South Pacific.

In this proof-of-concept study, we applied a beamforming array seismological technique to analyze the R-CET array recordings from a recent tsunamigenic earthquake (M_w 7.7) in the Vanuatu region, the Loyalty Islands, on May 19, 2023. We use sliding window fk-analysis beamforming, which can simultaneously measure backazimuth and slowness. Incorporating the sliding window, in conjunction with the fk diagrams, helps to observe temporal azimuthal changes, which facilitates tracking the rupture length and direction over time. Preliminary results showed the azimuthal and temporal variation of the rupture is in alignment with post-processing estimates of the finite fault solution for this event reported by USGS. We test the utility of this analysis in tsunami forecasts by comparing threat maps generated from the beamforming source and initial response maps used in real-time response on the day. We further compare our results to actual measured coastal cancellation gauges (tide gauges) and DART observations. We show that this analysis has the potential to improve initial tsunami forecasts prior to the onset of tsunami waves at deep ocean tsunamimeters. We further present the technique as an enticing path to meet UN Ocean Decade tsunami warning goals within our framework of ensemble and time-dependent forecasting.

How to cite: Aghaee-Naeini, A., Fry, B., and D.Eccles, J.: Spatial-Temporal Rupture Characterization of Potential Tsunamigenic Earthquakes Using Beamforming: Faster and More Accurate Tsunami Early Warning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6824, https://doi.org/10.5194/egusphere-egu24-6824, 2024.

Continuous seismic and environmental monitoring at remote seabed sites always faced a major challenge due to technical, logistical and financial effort. Commercial Telecommunication submarine cables continuously expand the coverage of ocean seafloor following society's needs to increase connectivity between distant countries and remote sites. Cables over thousands of kilometres long are equipped with in-line repeaters which compensate for optical losses due to  such long distances. 

A Science Monitoring And Reliable Telecommunications (SMART) Subsea Cables, designed by a Joint Task Force (JTF) across the International Telecommunication Union, World Meteorological Organization, the UNESCO Intergovernmental Oceanographic Commission,  may host, inside repeaters, scientific sensors for seismic, ocean and climate monitoring and disaster risk reduction in cases of tsunamis. 

The recent successful deployment at the Western Ionian Sea, one of EMSO (European Multidisciplinary Seafloor and water column Observatory) Regional Facilities, of the InSEA Wet Demo SMART Cable displays a world first demonstrating the feasibility of such installation using standard cable-laying techniques to show proof of concept. Commercial viability for these systems relies on the cable being laid as if the scientific element did not exist, thereby minimising additional deployment costs and reducing barriers to cooperation with cable laying companies. Güralp Systems Ltd and INGV deployed three seismometer-accelerometer pairs housed in inline repeaters along the 21km cable long. Each repeater also provides temperature and pressure devices which respectivley enable the real time monitoring of sea environment state and of sea surface level for tsunami detection.

This pioneering installation demonstrates the feasibility of smart cable initiative which may lead to global coverage of ocean seafloor with a network of scientific sensors enabling the  real time monitoring of seismicity and tsunami events at remote locations thanks to a collaboration between scientific and commercial parties.

How to cite: Marinaro, G., D'Amico, S., Embriaco, D., Giuntini, A., Simeone, F., O'Neill, J., Nicholson, B., Watkiss, N., and Restelli, F.: A 21 km SMART Cable for earthquakes and tsunami detection operating in the Ionian Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16261, https://doi.org/10.5194/egusphere-egu24-16261, 2024.

During the last decades, trans-oceanic tsunamis have been captured by satellite altimeters on several occasions. The largest event ever measured by an altimeter was the 2004 Indian Ocean tsunami, captured by Jason-1, Topex/Poseidon, GFO and Envisat altimetry missions flying at that time 

The new altimetry mission SWOT (Surface Water and Ocean Topography) developed by NASA and CNES, the US and French Space agency respectively, was launched in December 2022. SWOT embarks a novel instrument, a Ka-band Radar INterferometer (KaRIN), providing a 120 km wide swath Sea Level. On 19 May 2023, SWOT was able to measure the tsunami generated by the Mw 7.7 earthquake which occurred southeast of the Loyalty Islands (southwest Pacific Ocean) at 02:57:03 (UTC). SWOT flew over the region about 1 hour after the earthquake and captured the tsunami signature in several locations. For the first time, a 2D mapview image of the height of tsunami wavetrain was measured by a satellite.

The tsunami generation and propagation have been simulated using COMCOT model, using source parameters derived from seismic observations and empirical laws. Preliminary simulation results show that a simple fault plane with uniform coseismic slip allows to reproduce the regional coastal gauge and oceanic DART station records with a relatively good level of confidence, considering that the earthquake rupture was strongly not-double couple according to USGS. An array of virtual gauges was designed to cover the satellite pathway, allowing to extract the dynamic representation of the tsunami wavefield corresponding to the satellite propagation time (i.e., the sea surface deformation is observed over a period of time of several minutes, instead of being static at a given time). Comparison between the SWOT sea surface measurement and the simulation result is satisfactory, showing a good agreement between the location of the first wave peaks (propagating toward the southwest and the northeast, respectively), their amplitude and phase.

The objective of this study is first to present this unprecedented observation, and to analyze the level of consistency with simulations.

How to cite: Faugère, Y., Roger, J., Delepoulle, A., Dibarboure, G., and Hebert, H.: The 19 May 2023 tsunami near the Loyalty Islands captured by the new SWOT satellite, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15140, https://doi.org/10.5194/egusphere-egu24-15140, 2024.

The high-frequency (HF) ocean radar system is a shore-based remote sensing system that monitors sea surface currents, waves, and wind over large areas. It can measure tsunami-induced surface current velocity and provide information for tsunami early warning. An HF ocean radar system, installed by the Ministry of Land, Infrastructure, Transport, and Tourism in Japan, measured the tsunami velocity in the Kii Channel during the 2011 Tohoku earthquake. The Kii Channel is a strait that separates the Japanese island of Shikoku from the Kii Peninsula on the main island of Honshu. It connects the Osaka Bay with the Pacific Ocean.

We adopted the tsunami data assimilation approach to predict coastal tsunami waveforms. It is a method that reconstructs the tsunami wavefield using offshore data without the need for source information (Maeda et al., 2015). To process the HF radar data as the input, we initially converted the current velocity along the beam direction to into u, v directions (i.e., EW, NS directions). This process also involved the spatial interpolation of observational points from the beam of two HF radar land stations. In addition, recognizing the tradeoff between the sampling rate and velocity resolution, we applied a 10-min moving average to enhance data quality. The processed velocity data exhibited consistency with numerical simulations derived from the source model of Satake et al. (2013). The data assimilation started at 08:05 (UTC, hereafter) on March 11, 2011.

We predicted coastal tsunami waveform at Kobe, located in the Osaka Bay, and compared it with real observation recorded by the tide gauge. The forecast at 08:10 underestimated the tsunami amplitude, achieving an accuracy of 50.1% with a mean squared prediction error (MSPE) of 0.0101. However, the forecast at 08:20 matched well with the real observation, boasting an accuracy of 82.9% and a reduced MSPE of 0.0098. At 08:30, it continued to perform similarly, maintaining consistency between the predicted and observed waveforms. The accuracy was 81.3% and the MSPE further decreased to 0.0093. Given that the tsunami arrived in Kobe at 09:10, our approach can make an accurate prediction at least 50 min before its arrival.

To summarize, we demonstrated the effectiveness of the HF ocean radar system in tsunami early warning. The case study of the 2011 Tohoku tsunami yielded a remarkable accuracy of over 80% at Kobe station. In the future, we will investigate the relationship between the number and location of HF radar observational points and the forecast accuracy.

How to cite: Wang, Y. and Imai, K.: Tsunami early warning using high-frequency ocean radar system in the Kii Channel, Japan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9348, https://doi.org/10.5194/egusphere-egu24-9348, 2024.

Tsunami Early Warning Systems (TEWS) play a crucial role in minimizing the impact of tsunamis on coastal communities globally. In the NEAM region (North-East Atlantic, the Mediterranean, and connected Seas), historical approaches involve using Decision Matrices and precomputed databases due to the short time between tsunami generation and coastal impact. Overcoming real-time simulation challenges, the EDANYA group at the University of Málaga developed Tsunami-HySEA, a GPU code enabling Faster Than Real Time (FTRT) tsunami simulations. This code is successfully implemented and tested in TEWS of countries like Spain, Italy, and Chile, this code has undergone rigorous verification and validation processes.

In collaboration with the National Geographic Institute of Spain, we have extended the work previously done where we take advantage of the machine learning techniques and proposed a first approach to the use of neural networks (NN) to predict the maximum wave height and arrival time of tsunamis in the context of TEWS with very good results. This approach offers the advantage of minimal inference time and can be executed on any computer. It accommodates uncertain input data, delivering results within seconds.

As tsunamis are rare events, numerical simulations using the Tsunami-HySEA are used to train the NN model. This phase demands numerous simulations, necessitating substantial High-Performance Computing (HPC) resources. Approximately 300,000 simulations have been done to cover different faults in the Atlantic Ocean.

The goal is to develop neural network models for predicting the maximum wave height of such tsunamis at multiple coastal locations simultaneously.  To cover Huelva and Cádiz coast, 78 points in the coastline have been selected for their predictions. The main importance of this work is that the models developed will be implemented in the Spanish TEWS which will produce an estimation of the tsunami impact in seconds.

Acknowledgements

• This project has received funding from the European High-Performance Computing Joint Undertaking (JU) through the projects eFlows4HPC (No 955558) and ChEESE-2P (No 101093038) and by the EU project DT-GEO (No: 101058129).
• Spanish Network for Supercomputing (RES) grants AECT-2022-1-002, AECT-2022-3-0015 and AECT-2023-1-0028.

How to cite: Rodríguez Gálvez, J. F., Macías Sánchez, J., Gaite, B., Castro Díaz, M. J., Cantavella, J. V., and Puertas, L. C.: Optimizing Maximum Height Inferences through Neural Networks for the Spanish Tsunami Early Warning System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8319, https://doi.org/10.5194/egusphere-egu24-8319, 2024.