On July 29, 2025, a great mega-thrust earthquake of magnitude Mw 8.8 occurred near the Kamchatka Peninsula, Russia, generating a local tsunami comparable to that of its larger 1952 (Mw 9.0) predecessor. In contrast, the 1952 event caused a larger tsunami on the far-field Pacific shorelines. In addition, the 2025 tsunami was also smaller than anticipated by the life-saving tsunami warning issued for the far-field Pacific shorelines (e.g., Japan, Hawaii, South America). Here, we investigate the tsunami source of the 2025 event by jointly inverting the SWOT (Surface Water and Ocean Topography) and DART tsunami data with the static coseismic deformation measured by InSAR and GNSS. The tsunami Green’s functions are computed by considering the Kurils-Japan subduction interface parameterised by means of triangular subfaults, and the JAGURS code that solves the nonlinear shallow water equations with Boussinesq terms and also takes into account seawater density stratification, elastic loading, and gravitational potential change. Geodetic Green’s functions, as well as the tsunami initial conditions, are computed through the analytical formulation proposed by Nikkhoo and Walter (2015) for triangular dislocations considering also the contribution of the horizontal displacement. The slip model obtained after the inversion using the Simulated Annealing algorithm, highlights a southwestern unilateral rupture whose pattern partially overlaps the 1952 source zone consistently with the stress that had enough time to build up again since 1952. We show that the smaller earthquake magnitude (Mw 8.8 vs 9.0) and the overall relatively deep slip generated smaller tsunami potential energy, thus explaining the moderate far-field impact. Conversely, some shallow tsunamigenic displacement, reaching the trench, explains the enhanced local run-up comparable to the 1952 run-up, despite the smaller 2025 earthquake magnitude. Finally, we show that our findings are supported by a comparison with tsunami sources and observations for the 2010 Mw 8.8 Maule (Chile), and the 2005 Mw 8.5 Nias (Indonesia) earthquakes.

Issuing a tsunami warning rapidly is crucial to maximize the lead time for evacuation and other response actions. However, in the first minutes after an earthquake, no tsunami observations are available, and forecasts rely on rapid but uncertain estimates of earthquake location and magnitude. As more data become available, forecasts can be updated for more distant coastlines or near-source immediate post-event assessments. Here we present a method for real-time updating of Probabilistic Tsunami Forecasting that continuously integrates new seismic and tsunami observations. We apply it to the 2010 Mw8.8 Maule event and to several synthetic and real Mw 6.5-8.5 events in the Mediterranean Sea, demonstrating that it consistently preserves accuracy and generally reduces uncertainty, especially when using local tsunami data rather than focal mechanisms. Based on reweighting the initial scenario ensemble, this evolutionary approach enables fast updates and synchronization of forecast uncertainty with evolving information.

Probabilistic Tsunami Forecasting (PTF) uses the initial magnitude and location of a seismic event to forecast the tsunami intensity at coastal locations as a probability distribution (Selva et al., 2021). The PTF workflow can be summarized in the following steps: 1) select the ensemble of scenarios and the probability of a scenario coinciding with the actual earthquake; 2) for each scenario, compute a tsunami intensity measure (e.g., maximum inundation height) at coastal locations of interest – either by running shallow water tsunami propagation models with the code Tsunami-HySEA, or by retrieving it from a precomputed database of scenarios; 3) combine the intensity measure with scenario probabilities to compute hazard curves; 4) convert the probabilities into alert levels according to a predefined rule; and 5) visualise the results. More recently, developments in the context of the eFlows4HPC project have allowed for the possibility of updating the probabilities of the ensemble elements based on new data (focal mechanism and sea level data) to make the forecast more precise and/or reduce the uncertainties. Building on these new developments, we present the first results using an even more general PTF workflow here, implementing dynamically the assimilation of new data, such as new estimates of the earthquake magnitude and location, focal mechanism, GNSS displacements, and sea level data. In particular, new estimates of the source will be used to compute a new ensemble, new probabilities, and will trigger Tsunami-HySEA simulations of the new scenarios in the ensemble. If sea level and/or GNSS data are available, we compute the misfit between the data and the results of the simulations to further update the probabilities and reduce the overall uncertainty in the forecast. We use the 2020 Samos earthquake as a first test of the new workflow that includes data assimilation, but further testing will be done for other events in the Mediterranean Sea and Pacific Ocean. Implementing a continuous update of the results within the above outlined dynamic workflow triggered by the arrival of new data represents a crucial element in transforming the PTF into a digital twin.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/).

The Global Tsunami Model (GTM) global-scale Probabilistic Tsunami Hazard Assessment (PTHA) is one of the Pilot Demonstrators (PD) of the EU ChEESE-2P project, which would represent an update of the previous global tsunami hazard model proposed by Davies et al. (2018). Since it is a PTHA for earthquake-generated tsunamis, it is important that its input seismic model is consistent with the one used for Probabilistic Seismic Hazard Analysis (PSHA) at comparable scales and affecting the same locations.The GTM and the Global Earthquake Model (GEM) organizations then started collaborating to improve the interoperability of the tools used for PTHA and PSHA, and of the input and output data and models. This could benefit the end-users since both the shaking and the inundation result from the same causative phenomenon - the earthquake in this case.Moreover, the GEM OpenQuake (OQ) engine for seismic hazard and risk assessment provides an opportunity to compare the GTM tools with a well-tested software platform that uses accepted standards.In this contribution we present the first results of a sensitivity analysis of the PTHA results to the use of different earthquake occurrence models for the same seismogenic source zone, and to the use of different tools and codes for the generation of earthquake rupture catalogues, for the tsunami propagation, and for the aggregation of the hazard results. To this end, we use different combinations of the data, tools and codes from those of Davies et al. (2018) and the Australian PTHA (Davies, 2019), the GTM ones (e.g. Gibbons et al., 2020), and the OQ ones (Pagani et al., 2014). Davies G., et al., 2018. A global probabilistic tsunami hazard assessment from earthquake sources. Geological Society, London, Special Publications 456, 219–244. doi: 10.1144/sp456.5Davies G., 2019. "A new probabilistic tsunami hazard assessment for Australia." Australasian Coasts and Ports 2019 Conference: Future directions from 40 S and beyond, Hobart, 10-13 September 2019.Gibbons S.J., et al., 2020. “Probabilistic Tsunami Hazard Analysis: High Performance Computing for Massive Scale Inundation Simulations”. Front. Earth Sci. 8:591549. doi: 10.3389/feart.2020.591549Pagani M., et al., 2014. OpenQuake Engine: An open hazard (and risk) software for the Global Earthquake Model, Seismol. Res. Lett., 85, 3, 692-702, doi:10.1785/0220130087.

The EU ChEESE-2P project (Centre of Excellence for Exascale in Solid Earth, second Phase, https://cheese2.eu/) aims to developing Pilot Demonstrators (PD) in different areas of Solid Earth (SE) addressing 12 SE Exascale Computational Challenges. One of these is a new Probabilistic Tsunami Hazard Assessment (PTHA) for earthquake-generated tsunamis at the global scale, in the framework of the GTM (Global Tsunami Model) initiative. The GTM PTHA model is meant to be an update of the previous one of its kind (Davies et al., 2018, Geological Society of London). The new model will present enhanced source variability (e.g. stochastic slip) and spatially higher resolution of the calculation points. “Capacity” simulations will involve on the order of several 100k unit sources, using grids with a 30 arc-sec resolution. The offshore simulations will require on the order of a few million GPU hrs. Inundation simulations for some pilot localities may need up to 5-10 million GPU hrs. They encompass tens of millions of global tsunami scenarios and create high-resolution inundation maps for 10-20 hotspot locations. The global and local models will be distributed through EPOS-TCS Tsunami, showcasing EuroHPC resource utilization for local hazard and risk analysis.Further than representing a new global reference hazard model, some tools will be provided to allow to:Take the global model as an input to perform local PTHA anywhere globally; Recalculate the hazard using a custom source treatment, including probability, rates, fault data, and earthquake source models with dynamic and heterogeneous slip, using pre-calculated or on-the-fly HPC-based tsunami modelling with the Tsunami-HySEA GPU code; Publish results via the EPOS-TCS Tsunami service delivery framework. The GTM PTHA model and tools will be interoperable with the other seismic source models and risk calculation tools (e.g. OpenQuake), thus establishing a connection between the Global Tsunami Model (GTM) and Global Earthquake Model (GEM).We will also seek to establish compatibility and potential coupling with the Digital Twins from different EU projects (DT-GEO, DT-Ocean) towards DestinE.

Site-specific Probabilistic Tsunami Hazard Assessment (PTHA) is a powerful tool for coastal planning against tsunami risk. However, its typically high computational demands led to the introduction of a Monte Carlo Stratified Importance Sampling (SIS) approach, which selects a representative subset of scenarios for numerical inundation simulations. We here empirically validate this sampling approach, for the first time to our knowledge, using an existing extensive data set of numerical inundation simulations for two coastal sites in the Mediterranean Sea (Catania and Siracusa, both located in Sicily, Italy). Moreover, we propose a modified importance sampling function to prioritize seismic tsunami scenarios based on their arrival time at an offshore point near the target site, in addition to their wave amplitude and occurrence rate as leveraged in the previous work. This sampling function is applied separately in each earthquake magnitude bin, and allows denser sampling of near-field earthquakes to whose variations tsunamis are very sensitive. We compare the confidence intervals of the offshore PTHA estimates obtained with the new and the original importance sampling functions. Then, we benchmark our onshore PTHA estimates obtained with both functions against the inundation PTHA calculated using the full set of scenarios. We also test the assumption that onshore random errors follow a normal distribution, as found previously for the offshore case. As a result of the benchmarks, we find that the SIS approach works satisfactorily. Introducing the arrival time as an additional sampling factor enhances the precision of the estimates of both the mean and the percentiles for the two coastal sites considered. With this modification it is possible to deal efficiently with heterogeneous near-field earthquake sources involving coastal deformation at Catania and Siracusa, in addition to regional crustal and subduction sources. By comparing the sampling errors with the model (epistemic) uncertainty, an optimal trade-off between the number of simulations employed and the uncertainty of the PTHA model can be found, even for such a complex situation. A relatively small number of scenarios, on the order of a few thousand, is sufficient to perform site-specific PTHA for practical applications. These numbers correspond to 4-8 per cent of the already reduced ensembles used in previous assessments at the same sites.

Probabilistic tsunami hazard analysis (PTHA) introduces potential biases in tsunami risk assessment if it assumes static coastlines. Global warming, in addition to geological and local factors, may affect sea-level rise in the next few decades. Here, we provide a method that integrates the expected sea-level rise into existing PTHA, updating regional models without further tsunami simulations. We perform the tsunami hazard analysis at the densely populated Mediterranean coasts, which are highly exposed to tsunami inundations, as reported by historical and instrumental evidence. PTHA and related epistemic uncertainties significantly change when we include the time-dependent components, such as: (1) vertical land movements along the coasts, and (2) future sea-level changes based on the expected climate scenarios described by the IPCC AR6 Report. Probability maps show that the mean probability of exceeding the 1 m and 2 m maximum inundation heights in 2070 has a general increase differentiating locally, with percent variations mainly in the range 10-30% of the updated time-dependent PTHA compared with the current PTHA.

Tsunami urgent computing procedures quantify the potential hazard due to a seismically-induced tsunami right after an earthquake, that is from minutes to a few hours. The hazard is quantified by simulating the tsunami from source to shore, taking into account the uncertainty in the source parameters and the uncertainty associated with the wave generation, propagation, and inundation. In the European eFlows4HPC project, an HPC workflow for urgent computing of tsunami hazard assessment is currently being developed, consisting of the following steps: 1) retrieval of parameters for the tsunamigenic earthquake (magnitude, hypocentre and associated uncertainties), 2) definition of a seismic source ensemble, 3) simulation of the tsunami generated by each scenario in the ensemble, 4) aggregation of the results to produce an estimate of tsunami hazard, which also incorporates a basic treatment of uncertainty modelling and 5) update of the ensemble based on incoming data. Initially implemented on the Power-9 machine at BSC, the workflow has been fully embedded into a PyCOMPSs framework that enables parallel task execution and integrates full tsunami simulations for the first time. The tsunami numerical model (Tsunami-HySEA) computes the tsunami from the source to coastal impact using nested grids with resolution from kilometres to meters. To limit the number of simulations and converge faster towards stable hazard estimates, new methods for defining the seismic source ensembles have been developed. When applied to several past earthquakes and tsunamis (e.g., the 2003 Boumerdes and the 2017 Kos-Bodrum earthquakes), our new sampling strategy yielded a reduction of 1 or 2 orders of magnitude for ensemble size, allowing a drastic reduction in the computational effort. This reduction may be exploited to improve tsunami simulation accuracy, increasing the computational effort available for each simulation for the same overall cost. The workflow also allows the integration of new incoming data (focal mechanism, seismic or tsunami records) for an “on the fly” update of the PTF based on this new information. The improvement of the workflow through a well-defined ensemble of scenarios, realistic simulations and integration of incoming data, strongly reduces the uncertainty and yields to an update of the probabilistic forecasts without compromising theiraccuracy. This can be crucial in mitigating the risk far from the seismic source, and in improving risk management by better informing decision-making in an emergency framework.

Local hazard models for evacuation planning should accurately describe the probability of exceeding a certain intensity (e.g. flow depth, current velocity, etc.) over a period of years. Computational-based probabilistic forecasting for earthquake-generated tsunamis deals with tens of thousands to millions of scenarios to be simulated over very large domains and with sufficient spatial resolution of the bathymetry model. The associated high computational cost can be tackled by means of workflows that take advantage of HPC facilities and numerical models specifically designed for multi-GPU architectures. For the sake of feasibility, Seismic Probabilistic Tsunami Hazard Assessment (S-PTHA) at local scale exploits some approximations in both source and tsunami modeling, but uncertainty quantification is still lacking in the estimates. Here, we propose a possible approach to reduce the computational cost of local-scale S-PTHA, while providing uncertainty quantification. The algorithm performs an efficient selection of scenarios based on the tsunami impact on a site. The workflow is thought to take advantage of parallel execution on HPC clusters. Hence, as a first step, the whole ensemble of scenarios is split into a finite number of regions defined by the tectonic regionalization; then the procedure selects the scenarios mainly contributing to the hazard at an offshore point (in front of the target site) and for specific intensity levels. Finally, for each intensity level, the totality of synthetic tsunamigenic earthquakes is optimally sampled with replacement in a Monte Carlo Importance Sampling scheme. The tsunamis potentially triggered by the selected scenarios are explicitly simulated with the GPU-based Tsunami-HySEA nonlinear shallow water code on high spatial resolution grids (up to 10 m) and subsequently the Monte Carlo errors are finally propagated to the onshore estimates. This procedure allows for lessening the computational cost of local S-PTHA by reducing the number of simulations to be conducted while quantifying the epistemic uncertainties associated with the inundation modeling without appreciable losses of information content.

The 2023 February 6 Mw 7.8 earthquake was the first one of a doublet which shook Türkiye and Syria causing, as per the estimates at the time of writing of this abstract, more than 45,000 casualties.The current standard operating procedures of the NEAMTWS (Tsunami Warning System in the North-Eastern Atlantic, the Mediterranean and connected seas, coordinated by UNESCO/IOC) for the initial tsunami warning message following an earthquake are based on a Decision Matrix (DM), whose input parameters are hypocentre and magnitude of the earthquake. Since the epicentre of this earthquake was located at a depth between 15-35 km at almost 100 km from the coast, both KOERI (Türkiye) and INGV (Italy) Tsunami Service Providers (TSPs) of the NEAMTWS issued a Tsunami Watch message (i.e., runup expected to exceed 1 m) for the whole Mediterranean Sea. NOA (Greece) did not issue any alert, because its initial location was more than 100 km from the coast.In response to the tsunami warning, trains were stopped in different locations in Southern Italy for several hours, and evacuation of some coastal areas was enforced. However, only a relatively small tsunami was recorded by Turkish close-by tide-gauges in the Eastern Mediterranean, with a maximum recorded amplitude of less than 50 cm. Based on these measurements and on others showing little to no tsunami at increasing distances, the alert was then ended after 5 and 9 hours by INGV and KOERI, respectively, based on the available tide-gauge recordings and interaction with Civil Protection Officers.This event has highlighted that NEAMTWS is an asset for the coastal communities. It can provide rapid alerts, which can save lives if the last-mile of the procedures is in place and the communities are “Tsunami Ready”, that is aware and prepared to respond with evacuations and other appropriate countermeasures. On the other hand, while it is reasonable – and dutiful based on current standard operation procedures – to issue a basin-wide, or at least a local alert, for an inland earthquake of unknown mechanism and of such a large magnitude, it is perhaps possible to improve the DM, which is totally heuristic and characterized by hard-thresholds, with consideration of numerical tsunami simulations and quantitative uncertainty treatment with more continuous variations. Moreover, there is no procedure currently in place to differentiate among locations where the expected time of arrival differs by many hours across the Mediterranean basin, nor a sufficient instrumental coverage that could make cancellation/ending faster due to a more solid observational basis.We will discuss some of the scientific and operational aspects with the aim of identifying which lessons can be learned to improve the NEAMTWS efficiency. We will also compare the DM-based alerts with those that would be produced with the recently introduced Probabilistic Tsunami Forecasting (PTF, Selva et al., 2021, Nature Communications), presently in pre-operational testing at INGV.

The EU Center of Excellence for Exascale in Solid Earth (ChEESE) develops exascale transition capabilities in the domain of Solid Earth, an area of geophysics rich in computational challenges embracing different approaches to exascale (capability, capacity, and urgent computing). The first implementation phase of the project (ChEESE-1P; 2018–2022) addressed scientific and technical computational challenges in seismology, tsunami science, volcanology, and magnetohydrodynamics, in order to understand the phenomena, anticipate the impact of natural disasters, and contribute to risk management. The project initiated the optimisation of 10 community flagship codes for the upcoming exascale systems and implemented 12 Pilot Demonstrators that combine the flagship codes with dedicated workflows in order to address the underlying capability and capacity computational challenges. Pilot Demonstrators reaching more mature Technology Readiness Levels (TRLs) were further enabled in operational service environments on critical aspects of geohazards such as long-term and short-term probabilistic hazard assessment, urgent computing, and early warning and probabilistic forecasting. Partnership and service co-design with members of the project Industry and User Board (IUB) leveraged the uptake of results across multiple research institutions, academia, industry, and public governance bodies (e.g. civil protection agencies). This article summarises the implementation strategy and the results from ChEESE-1P, outlining also the underpinning concepts and the roadmap for the on-going second project implementation phase (ChEESE-2P; 2023–2026).

<p>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. </p> <p>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<sub>w</sub> 8.1 Iquique event.</p> <p>Here, we evaluate the variability of the tsunami forecasting for the Chilean coast as resulting <em>i)</em> from the coseismic slip model obtained by geophysical data inversion and <em>ii)</em> from an expeditious method for the tsunami source estimation, based on an extension of the well-known spectral approach. </p> <p>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.</p> <p>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.</p> <p>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.</p> <p> </p>

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.

<p>The coasts of the Mediterranean Sea are densely populated and exposed to tsunami inundations as reported by historical evidence. Measures to mitigate the tsunami risk in this region are based on Probabilistic Tsunami Hazard Assessments (PTHA) computed considering present coastal morphologies. However, mean sea level projections for the 21st century indicated a general sea level rise which can be substantially modified if uplift or subsidence may occur locally due to other geological factors. In order to reduce the potential impact of tsunamis all factors (climatic or not) should be included in the tsunami hazard analysis. In this study we focus on the Eastern Sicily and we examine how the PTHA can significantly change when the general trend of sea level rise, based on AR-5 and AR-6 IPCC climate scenarios and rates of Vertical Land Movements, are included in the region. Moreover, we take into account associated epistemic uncertainties related to the future sea level rise under different conditions of low- and high-emission representative concentrations. </p>

A probabilistic volcanic hazard assessment (PVHA) for Ceboruco volcano (Mexico) is reported using PyBetVH, an e-tool based on the Bayesian Event Tree (BET) methodology. Like many volcanoes, Ceboruco is under-monitored. Despite several eruptions in the late Holocene and efforts by several university and government groups to create and sustain a monitoring network, this active volcano is monitored intermittently rather than continuously by dedicated groups. With no consistent monitoring data available, we look at the geology and the eruptive history to inform prior models used in the PVHA. We estimate the probability of a magmatic eruption within the next time window (1 year) of ~ 0.002. We show how the BET creates higher probabilities in the absence of monitoring data, which if available would better inform the prior distribution. That is, there is a cost in terms of higher probabilities and higher uncertainties for having not yet developed a sustained volcano monitoring network. Next, three scenarios are developed for magmatic eruptions: i) small magnitude (effusive/explosive ), ii) medium magnitude (Vulcanian/sub-Plinian) and iii) large magnitude (Plinian). These scenarios are inferred from the Holocene history of the volcano, with their related hazardous phenomena: ballistics, tephra fallout, pyroclastic density currents, lahars and lava flows. We present absolute probability maps (unconditional in terms of eruption size and vent location) for a magmatic eruption at Ceboruco volcano. With PyBetVH we estimate and visualize the uncertainties associated with each probability map. Our intent is that probability maps and uncertainties will be useful to local authorities who need to understand the hazard when considering the development of long-term urban and land-use planning and short-term crisis management strategies, and to the scientific community in their efforts to sustain monitoring of this active volcano.

In seismically active regions with variable dominant focal mechanisms, there is considerable tsunami inundation height uncertainty. Basic earthquake source parameters such as dip, strike, and rake affect significantly the tsunamigenic potential and the tsunami directivity. Tsunami inundation is also sensitive to other properties such as bottom friction. Despite their importance, sensitivity to these basic parameters is surprisingly sparsely studied in literature. We perform suites of systematic parameter searches to investigate the sensitivity of inundation at the towns of Catania and Siracusa on Sicily to changes both in the earthquake source parameters and the Manning friction. The inundation is modelled using the Tsunami-HySEA shallow water code on a system of nested topo-bathymetric grids with a finest spatial resolution of 10 m. This GPU-based model, with significant HPC resources, allows us to perform large numbers of high-resolution tsunami simulations. We analyze the variability of different hydrodynamic parameters due to large earthquakes with uniform slip at different locations, focal depth, and different source parameters. We consider sources both near the coastline, in which significant near-shore co-seismic deformation occurs, and offshore, where near-shore co-seismic deformation is negligible. For distant offshore earthquake sources, we see systematic and intuitive changes in the inundation with changes in strike, dip, rake, and depth. For near-shore sources, the dependency is far more complicated and co-determined by both the source mechanisms and the coastal morphology. The sensitivity studies provide directions on how to resolve the source discretization to optimize the number of sources in Probabilistic Tsunami Hazard Analysis, and they demonstrate a need for a far finer discretization of local sources than for more distant sources. For a small number of earthquake sources, we study systematically the inundation as a function of the Manning coefficient. The sensitivity of the inundation to this parameter varies greatly for different earthquake sources and topo-bathymetry at the coastline of interest. The friction greatly affects the velocities and momentum flux and to a lesser but still significant extent the inundation distance from the coastline. An understanding of all these dependencies is needed to better quantify the hazard when source complexity increases.

We present a refined methodological procedure for computationally efficient local SPTHA based on regional SPTHA. The adopted procedure extracts from the regional SPTHA the most impacting tsunami sources at the investigated site, and reconstructs hazard curves on high-resolution topobathymetric models based on a reduced set of inundation simulations. This procedure enhances the original workflow for local SPTHA quantification described by Volpe et al. (2019), applying some significant upgrades to simplify its application and improve the accuracy of the results. In particular, the description of local sources has been refined through a more detailed discretization of the natural variability (aleatory uncertainty), eventually reducing the epistemic uncertainty. Then, a more efficient filtering procedure, based on the strategy proposed by Williamson et al. (2020), is adopted to select a subset of scenarios to be modelled at high resolution, eventually reducing the epistemic uncertainty introduced by this selection. This allows to perform only coarse-grid simulations after the regional source filtering and local source refinement, and then combine coarse-grid results with fine-grid topography. Overall, the resulting method simplifies the original one, improving accuracy and decreasing uncertainty. The newly developed procedure is applied to an illustrative case study for the harbour of Ravenna (Northern Adriatic Sea, Italy).

Tsunamis constitute a significant hazard for European coastal populations, and the impact of tsunami events worldwide can extend well beyond the coastal regions directly affected. Understanding the complex mechanisms of tsunami generation, propagation, and inundation, as well as managing the tsunami risk, requires multidisciplinary research and infrastructures that cross national boundaries. Recent decades have seen both great advances in tsunami science and consolidation of the European tsunami research community. A recurring theme has been the need for a sustainable platform for coordinated tsunami community activities and a hub for tsunami services. Following about three years of preparation, in July 2021, the European tsunami community attained the status of Candidate Thematic Core Service (cTCS) within the European Plate Observing System (EPOS) Research Infrastructure. Within a transition period of three years, the Tsunami candidate TCS is anticipated to develop into a fully operational EPOS TCS. We here outline the path taken to reach this point, and the envisaged form of the future EPOS TCS Tsunami. Our cTCS is planned to be organised within four thematic pillars: (1) Support to Tsunami Service Providers, (2) Tsunami Data, (3) Numerical Models, and (4) Hazard and Risk Products. We outline how identified needs in tsunami science and tsunami risk mitigation will be addressed within this structure and how participation within EPOS will become an integration point for community development.