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Probabilistic Tsunami Forecasting (PTF) combines early estimates of earthquake parameters with large ensembles of urgent shallow water tsunami propagation simulations using the GPU based Tsunami-HySEA model (Selva et al. 2021, Nature Commun.). The present version of the PTF is initialised by the earthquake information, but not updated further with new data. In the recently started Horizon Europe project DT-GEO, this PTF is presently being upgraded to a Digital Twin. The first and essential upgrade to realise the Digital Twin is continuous data assimilation enabling a close to real time synthesis of data products and a set of numerical models that allow an updating of the model forecast as new data are continuously assimilated into the model. In DT-GEO, an extended set of data sources, including improved earthquake solutions, sea level tsunami data, and GNSS, will be integrated. A second objective of the PTF is to implement a modularised Digital Twin Component that allows for the inclusion of improved wave and source physics through dispersion, non-hydrostatic tsunami generation, inundation, improved earthquake physics, and cascading earthquake triggered landslide tsunamis. The model will be tested at site demonstrators, in the Mediterranean Sea for eastern Sicily and Samos, and in the Pacific Ocean for Chile and eastern Japan. The presentation will explain how the PTF as it works today, followed by an outline of the design of the components in the Digital Twin, as well as briefly describing initial improvements and plans for further development, including potential integration into Destination Earth. 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/).

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

Destination Earth initiative pursues the implementation of a digital model of the Earth. With the aim to help understand and simulate the evolution and behavior of the Earth system components, to aid in better forecasting the impacts on human system processes, ecosystem processes and their interaction. The current state of the art technologies in numerical computations (HPC), data infrastructures (involving data storage, data access, data analysis), enable the possibility of developing numerical clones mimicking Earth’s geophysical extreme phenomena.A Digital Twin for GEOphysical extremes (DT-GEO),is a new EU project funded under the Horizon Europe programme (2022-2025), with the objective of developing a prototype for a digital twin on geophysical extremes including earthquakes, volcanoes, tsunamis, and anthropogenic-induced extreme events. It will enable analyses, forecasts, and responses to “what if” scenarios for natural hazards from their genesis phases and across their temporal and spatial scales. The project consortium brings together world-class computational and data Research Infrastructures (RIs), operational monitoring networks, and leading-edge research and academia partnerships in various fields of geophysics. It mergesthe latest outcomes from other European projects and, Centers of Excellence. DT-GEO will deploy and test 12 Digital Twin Components (DTCs). These will be self-contained entities embedding flagship simulation codes, Artificial Intelligence layers, large volumes of (real-time) data streams from and into data-lakes, data assimilation methodologies, and overarching workflows for deployment and execution of single or coupled DTCs in centralized HPC and virtual cloud computing Ris. (DT-GEO: A Digital Twin for GEOphysical extremes, project ID 101058129)

Local Probabilistic Tsunami Hazard Analysis (PTHA) aims to quantify the likelihood of a given metric of tsunami inundation at a given location over a given time interval. Seismic PTHA can require the simulation of thousands to tens of thousands of earthquake scenarios and can become computationally intractable when inundation over high-resolution grids is required. The numerical tsunami simulations write out time-series at offshore locations to simulate the wave height that would be recorded on tide gauges at selected locations. The offshore time-series can be calculated at a fraction of the cost of the full inundation calculations. For a stretch of the coast of Eastern Sicily, we explore the extent to which a machine learning procedure trained on a small fraction of the total number of scenarios can predict the inundation map associated with a given offshore time-series. We exploit a set of over 30000 numerical tsunami simulations to train and evaluate the ML-procedure. The ML-based inundation predictions for locations close to the water's edge, which are flooded in many of the scenarios, show excellent correspondence with the numerical simulation results. Predicting inundation at locations further inland, which are flooded in only a small number of the simulations, is more challenging. Mitigating this shortcoming is a priority in the ongoing study.

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.

Landslide-induced tsunamis are one of the most important cascading multi-hazard risks in light of landslide disasters. During the Fifth World Landslide Forum, a World Tsunami Awareness Day Special Event was held in hybrid mode on 5 November 2021. This article presents the outcome of the panel discussion organized across America, Europe, and Asia, as well as a review of the special event for understanding and reducing the disaster risk of landslide-induced tsunamis.

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)., Future Generation Computer Systems, 146, ISSN:0167-739X, ISSN:1872-7115

Special issue EPOS a Research Infrastructure in solid Earth: open science and innovation .-- 21 pages, 8 figures, 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, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

A World Tsunami Awareness Day Special Event was held in hybrid mode on 5 November 2021, during the Fifth World Landslide Forum, in Kyoto, Japan. In this context, a panel discussion was organized across America, Europe, and Asia, with the goal to better understand and reduce the disaster risk of landslide-induced tsunamis, consistent with the Kyoto Landslide Commitment 2020. This article presents a short summary of this panel discussion.

On June 17, 2017, a 40 Mm3 rock avalanche generated a tsunami that impacted several coastal communities in Karrat Fjord, Central West Greenland. The tsunami run-up was 10-12 m high in the nearest village 30 kilometres away from the rock avalanche and caused four fatalities. The two villages most heavily affected are still evacuated. In the aftermath of this event, several unstable rock slopes have been discovered proximal to the 2017 rock avalanche. One of these volumes, coined Karrat 1, has a volume of about 0.5 km3 and is hence at least an order of magnitude larger than the volume involved in the 2017 event. To put this in perspective, it has a volume 2-3 times larger than the 2018 Anak Krakatau tsunami that led to more than 400 fatalities in Sunda Strait, Indonesia (which is also much more heavily populated). Hence, the Karrat 1 worst case scenario poses a threat to a much larger area than the event that took place in 2017 and could potentially affect the whole fjord system. In this study, we quantify the tsunami hazard from this unstable rock slope as well as the 2017 event. We first provide a set of landslide tsunami simulations using a frictional-collisional Voellmy type model coupled to a tsunamis model for the event in 2017 and compare it with observations. We found that the model results agree closely with observations of the tsunami run-up heights, observations of the tsunami arrival times, and the wave periods. The 2017 tsunami model was then used to calibrate the landslide source model for the future hazard, simulating the Karrat 1 landslide tsunami with an included uncertainty range. Extreme run-up heights (10-70 m) are found for the nearest villages, as well as complete inundation of entire low-lying villages, some more than 100 km away from the landslide release area. The large modelled run-up heights, involving extreme run-up heights and relatively short arrival times for the nearby villages, demonstrate the need for better understanding of the risk as well as risk-reducing measures. With few or no previous subaerial events that have taken place historically of this scale, the possible implications of a catastrophic release are widespread, but they also imply substantial uncertainties.

Subaerial landslides are among the most complex sources for tsunamis, as several complex processes occur simultaneously in various regimes, with multiple phases interacting. The simulation and prediction of these events is respectively difficult.We will present a three-dimensional multiphase model (granules, air, water) that considers the effects and properties that we deem most important: (i) a sharp water-air interface with low diffusivity, (ii) granular rheology for the landslide, (iii) differentiation between effective pressure and pore pressure, as well as (iv) porosity, dilatancy and permeability. No depth-integration or other form of simplification is applied. The resulting mathematical model is solved with the fluid dynamics toolkit OpenFOAM.Many effects and processes that are lost in depth-integrated models are directly simulated in our approach. This allows the simulation of complex events with a relatively simple model, however for a large computational cost. The model parameters are widely intrinsic material parameters, which promises a prediction of events without significant parameter optimizations.We will show results for small scale experiments as well as for a well documented real scale event and will give an outlook on further developments and remaining problems.

Probabilistic tsunami hazard and risk analysis (PTHA/PTRA) is an emerging scientific discipline within the tsuanmi community and allows potentially to incorporate the diverse sources of uncertainty into disaster prevention, preparedness, and mitigation activities. While there are a number of successful applications of this paradigm, it is still an emerging field with a number of unresolved research questions. In a collaborative effort members of the COST Action AGITHAR assessed the existing research gaps for PTHA/PTRA and identified almost 50 different topics worth of further research. An ad hoc expert judgement was conducted to weight these open questions with respect to their expected impact on the quality of the PTHA/PTRA results and their difficulty to be answered. The results of this collaborative effort will be reported highlighting the most challenging and most severe research gaps.The presentation is based on the following publication:J. Behrens, F. Løvholt, F. Jalayer, et al. (2021): Probabilistic Tsunami Hazard and Risk Analysis – A Review of Research Gaps, Frontiers in Earth Science, 9:114, DOI:10.3389/feart.2021.628772.

Supplementary data: The following is the Supplementary data to this article: Acrobat PDF file (2MB) available at: https://ars.els-cdn.com/content/image/1-s2.0-S2212420921007329-mmc1.pdf Copyright © 2022 The Authors. Very large tsunamis are associated with low probabilities of occurrence. In many parts of the world, these events have usually occurred in a distant time in the past. As a result, there is low risk perception and a lack of collective memories, making tsunami risk communication both challenging and complex. Furthermore, immense challenges lie ahead as population and risk exposure continue to increase in coastal areas. Through the last decades, tsunamis have caught coastal populations off-guard, providing evidence of lack of preparedness. Recent tsunamis, such as the Indian Ocean Tsunami in 2004, 2011 Tohoku and 2018 Palu, have shaped the way tsunami risk is perceived and acted upon. Based on lessons learned from a selection of past tsunami events, this paper aims to review the existing body of knowledge and the current challenges in tsunami risk communication, and to identify the gaps in the tsunami risk management methodologies. The important lessons provided by the past events call for strengthening community resilience and improvement in risk-informed actions and policy measures. This paper shows that research efforts related to tsunami risk communication remain fragmented. The analysis of tsunami risk together with a thorough understanding of risk communication gaps and challenges is indispensable towards developing and deploying comprehensive disaster risk reduction measures. Moving from a broad and interdisciplinary perspective, the paper suggests that probabilistic hazard and risk assessments could potentially contribute towards better science communication and improved planning and implementation of risk mitigation measures. COST (European Cooperation in Science and Technology); Royal Society, UK (grant number CHL\R1\180173); Severo Ochoa Centers of Excellence Program (CEX 2018-000797-S) funded by MCIN/ AEI /10.13039/501100011033; Lloyd's Tercentenary Research Foundation, the Lighthill Risk Network, and the Lloyd's Register Foundation-Data Centric Engineering Programme of the Alan Turing Institute.

41 pages, 23 figures, 3 tables, Submarine landslides occur across all continental margins worldwide. They are diverse in nature and can involve tremendous volumes and long run-out distances. This paper provides an overview of the geographical extent and mechanisms of dense flowing submarine landslides. To this end, the review describes occurrence of past landslides, preconditioning and triggers, geophysical and geomechanical characterization, landslide dynamics, tsunami-genesis, and hazard analysis. Given the huge complexity submarine landslides encompass, an emphasis is here given on quantitative and methodological aspects such as landslide dynamics modeling and tsunami generation

Destructive tsunamis are most often generated by large earthquakes occurring at subduction interfaces, but also other “atypical” sources—defined as crustal earthquakes and non-seismic sources altogether—may cause significant tsunami threats. Tsunamis may indeed be generated by different sources, such as earthquakes, submarine or coastal landslides, volcano-related phenomena, and atmospheric perturbations. The consideration of atypical sources is important worldwide, but it is especially prominent in complex tectonic settings such as the Mediterranean, the Caribbean, or the Indonesian archipelago. The recent disasters in Indonesia in 2018, caused by the Palu-Sulawesi magnitude Mw 7.5 crustal earthquake and by the collapse of the Anak-Krakatau volcano, recall the importance of such sources. Dealing with atypical sources represents a scientific, technical, and computational challenge, which depends on the capability of quantifying and managing uncertainty efficiently and of reducing it with accurate physical modelling. Here, we first introduce the general framework in which tsunami threats are treated, and then we review the current status and the expected future development of tsunami hazard quantifications and of the tsunami warning systems in Italy, with a specific focus on the treatment of atypical sources. In Italy, where the memory of historical atypical events like the 1908 Messina earthquake or the relatively recent 2002 Stromboli tsunami is still vivid, specific attention has been indeed dedicated to the progressive development of innovative strategies to deal with such atypical sources. More specifically, we review the (national) hazard analyses and their application for coastal planning, as well as the two operating tsunami warning systems: the national warning system for seismically generated tsunamis (SiAM), whose upstream component—the CAT-INGV—is also a Tsunami Service Provider of the North-eastern Atlantic, the Mediterranean and connected seas Tsunami Warning System (NEAMTWS) coordinated by the Intergovernmental Coordination Group established by the Intergovernmental Oceanographic Commission (IOC) of UNESCO, and the local warning system for tsunamis generated by volcanic slides along the Sciara del Fuoco of Stromboli volcano. Finally, we review the state of knowledge about other potential tsunami sources that may generate significant tsunamis for the Italian coasts, but that are not presently considered in existing tsunami warning systems. This may be considered the first step towards their inclusion in the national tsunami hazard and warning programs.

Purpose Using risk-related data often require a significant amount of upfront work to collect, extract and transform data. In addition, the lack of a consistent data structure hinders the development of tools that can be used with more than one set of data. The purpose of this paper is to report on an effort to solve these problems through the development of extensible, internally consistent schemas for risk-related data. Design/methodology/approach The consortia coordinated their efforts so the hazard, exposure and vulnerability schemas are compatible. Hazard data can be provided as either event footprints or stochastic catalogs. Exposure classes include buildings, infrastructure, agriculture, livestock, forestry and socio-economic data. The vulnerability component includes fragility and vulnerability functions and indicators for physical and social vulnerability. The schemas also provide the ability to define uncertainties and allow the scoring of vulnerability data for relevance and quality. Findings As a proof of concept, the schemas were populated with data for Tanzania and with exposure data for several other countries. Research limitations/implications The data schema and data exploration tool are open source and, if widely accepted, could become widely used by practitioners. Practical implications A single set of hazard, exposure and vulnerability schemas will not fit all purposes. Tools will be needed to transform the data into other formats. Originality/value This paper describes extensible, internally consistent, multi-hazard, exposure and vulnerability schemas that can be used to store disaster risk-related data and a data exploration tool that promotes data discovery and use.

Submarine landslides have been identified in almost all ocean basins worldwide. The largest submarine landslides occur on very shallow slopes and can be far larger than any terrestrial landslide. Submarine landslides can produce tsunami whose far-reaching effects can rival those produced by earthquake-tsunamis and threaten increasingly populated coastlines. Even small landslides can damage very expensive and critically important offshore infrastructure, such as pipelines used for oil and gas recovery, and telecommunication cables that now carry over 95% of digital data traffic. A better understanding of submarine landslide processes, including triggering mechanisms, preconditioning factors, timing, and frequency as well as dynamics of submarine landslide, and their consequences are of clear societal and economic importance. Despite their importance, many fundamental submarine landslide processes are still poorly understood. We currently have many studies that have mapped and sampled submarine landslide deposits; however, in order to fill outstanding but key knowledge gaps, future studies may have to go beyond this in order to unravel processes governing submarine landslides with even more interdisciplinary approaches. This chapter provides a very short review about submarine landslide studies, with emphasis on the emerging needs in future landslide research.

Tsunami generation from sub‐aqueous landslides is controlled by landslide kinematics, which in turn is governed by the material properties of the slide mass. Yet, the effect of the material properties on tsunami‐genesis is poorly understood. Geomorphological observations of landslide run‐out put constraints on the landslide dynamics. In addition, observations of tsunami run‐up heights can improve our understanding of how the landslide material transforms from initiation to final run‐out. The giant prehistoric Storegga Slide off the mid‐Norwegian coast caused a well documented ocean‐wide tsunami that offers a unique setting for coupling landslide material models to tsunami generation models. In this study we simulate the dynamics of the Storegga Slide and tsunami using the depth‐averaged landslide model BingClaw, which implements visco‐plastic rheology and remolding, and couple it to a standard tsunami propagation model. A broad sensitivity study varying the landslide material strength parameters in BingClaw showed that the initial soil yield strength and remolding rate are most important for the tsunami‐genesis, but that the residual strength determined the final run‐out distance. BingClaw parameters were further optimized to obtain the observed run‐out distance and to minimize the relative error of the tsunami run‐up heights. As detailed time‐dependent three‐dimensional representations of landslide parameters cannot be determined field investigation of the landslide itself, these simulations of the Storegga Slide and tsunami can help in the selection of plausible parameter ranges for prognostic modeling in quantitative hazard assessments. Landslide Material Control on Tsunami Genesis ‐ The Storegga Slide and Tsunami (8100 y BP)

Regional and global tsunami hazard analysis requires simplified and efficient methods for estimating the tsunami inundation height and its related uncertainty. One such approach is the amplification factor (AF) method. Amplification factors describe the relation between offshore wave height and the maximum inundation height, as predicted by linearized plane wave models employed for incident waves with different wave characteristics. In this study, a new amplification factor method is developed that takes into account the offshore bathymetry proximal to the coastal site. The present AFs cover the North-Eastern Atlantic and Mediterranean (NEAM) region. The model is the first general approximate model that quantifies inundation height uncertainty. Uncertainty quantification is carried out by analyzing the inundation height variability in more than 500 high-resolution inundation simulations at six different coastal sites. The inundation simulations are undertaken with different earthquake sources in order to produce different wave period and polarity. We show that the probability density of the maximum inundation height can be modeled with a log-normal distribution, whose median is quite well predicted by the AF. It is further demonstrated that the associated maximum inundation height uncertainties are significant and must be accounted for in tsunami hazard analysis. The application to the recently developed TSUMAPS-NEAM probabilistic tsunami hazard analysis (PTHA) is presented as a use case.

Inundation maps are a fundamental tool for coastal risk management and in particular for designing evacuation maps and evacuation planning. These in turn are a necessary component of the tsunami warning systems’ last-mile. In Italy inundation maps are informed by a probabilistic tsunami hazard model. Based on a given level of acceptable risk, Italian authorities in charge for this task recommended to consider, as design hazard intensity, the average return period of 2500 years and the 84th percentile of the hazard model uncertainty. An available, regional-scale tsunami hazard model was used that covers the entire Italian coastline. Safety factors based on analysis of run-up variability and an empirical coastal dissipation law on a digital terrain model (DTM) were applied to convert the regional hazard into the design run-up and the corresponding evacuation maps with a GIS-based approach. Since the regional hazard cannot fully capture the local-scale variability, this simplified and conservative approach is considered a viable and feasible practice to inform local coastal risk management in the absence of high-resolution hazard models. The present work is a first attempt to quantify the uncertainty stemming from such procedure. We compare the GIS-based inundation maps informed by a regional model with those obtained from a local high-resolution hazard model. Two locations on the coast of eastern Sicily were considered, and the local hazard was addressed with the same seismic model as the regional one, but using a higher-resolution DTM and massive numerical inundation calculations with the GPU-based Tsunami-HySEA nonlinear shallow water code. This study shows that the GIS-based inundation maps used for planning deal conservatively with potential hazard underestimation at the local scale, stemming from typically unmodeled uncertainties in the numerical source and tsunami evolution models. The GIS-based maps used for planning fall within the estimated “error-bar” due to such uncertainties. The analysis also demonstrates the need to develop local assessments to serve very specific risk mitigation actions to reduce the uncertainty. More in general, the presented case-studies highlight the importance to explore ways of dealing with uncertainty hidden within the high-resolution numerical inundation models, e.g., related to the crude parameterization of the bottom friction, or the inaccuracy of the DTM.

Advances in GPU-based High-Performance Computing (HPC) facilities, combined with improvements in GPU-optimized shallow water models for tsunami inundation, allow us to perform large numbers of numerical simulations of earthquake-generated tsunamis on high-resolution numerical grids. Large numbers of simulations are necessary to investigate the multi-dimensional parameter space that defines the tsunami hazard, including situations where the tsunami is generated outside major tectonic structures, where fault geometry is uncertain and can take widely different orientations. With over 1500 numerical simulations, 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 in the specification of the Manning friction coefficient. The inundation is modelled using the GPU-based Tsunami-HySEA code on a system of nested topo-bathymetric grids with a finest spatial resolution of 10 meters. We consider tsunamigenesis by large earthquakes with uniform slip where the location, focal depth, fault dimensions and slip, together with the angles of strike, dip, and rake, are defined by the standard Okada parameters. We consider sources both close to the shore, in which significant co-seismic deformation occurs, and offshore, where co-seismic deformation is negligible. For the offshore earthquake sources, we see systematic and intuitive changes in the inundation with changes in strike, dip, rake, and depth. For the near-shore sources, the dependency is far more complicated and co-seismic deformation becomes significant in determining the inundation. The sensitivity studies provide clear guidelines as to the necessary resolution for source discretization for Probabilistic Tsunami Hazard Analysis, with 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 Friction Coefficient. The sensitivity of the inundation to this parameter varies greatly for different earthquake sources and topo-bathymetry at the coastline of interest. An understanding of all these dependencies is needed to better understand the consequences of tsunamigenic earthquake models with more complex geometries, and in quantifying the epistemic uncertainty in the tsunami hazard. This work is partially funded by the European Union’s Horizon 2020 Research and Innovation Program under grant agreement No 823844 (ChEESE Center of Excellence, www.cheese-coe.eu). Computational resources made available through Sigma2/UNINETT on Saga at NTNU, Trondheim, Norway (in project nn5008k) and through PRACE on Marconi-100 at CINECA, Rome, Italy (through PRACE grant Pra21_5386/TsuHazAP).

Landslide tsunamis, despite their importance for the overall tsunami hazard, is not as well understood as earthquake tsunamis. Several uncertain factors contribute to the lack of understanding, such as the variability in the source mechanisms, the dynamics of the landslide and the tsunami generation, as well as the temporal probability of occurrence of landslide events. Here, we present an overview of research activities on landslide tsunami analyses in the H2020 ITN-SLATE project. This research originates from two PhD student projects within SLATE, which have so far resulted in at least six publications with several more in the pipeline. In the SLATE project, we show that both translational and rotational dynamic attributes of the landslide are good indicators of the tsunamigenic potential of slumps using the visco-plastic landslide model BingClaw, by correlating the acceleration times mass and also angular momentum with the induced tsunami height. Moreover, we have employed Navier-Stokes simulations to hindcast model experiments of subaerial landslide tsunamis. By using the experience modelling this benchmark to model tsunamis in many other geometrical settings, the Navier-Stokes model is further employed to test generality and discuss several existing parametric relationships from literature so far available only empirically. New 3D formulations for granular landslide dynamics have further been established. Numerical models have also been set up to simulate real cases such as Anak Krakatoa. Finally, a broad parametric study that constrain the landslide dynamics for a landslide probabilistic hazard analysis is undertaken, to show how using past observations can effectively reduce uncertainties related to landslide dynamics. Combining an overview of the study with some highlights, we show how SLATE has contributed to increasing our understanding of landslide tsunamis and their hazard. We also discuss how the outcome of this project provides a platform for further research. This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 721403.

Infrasound and audible sound at very low frequency from sources such as military aircrafts, explosions and wind mills can induce building vibration involving both rattling and whole-body vibration strong enough to cause annoyance. Sonic boom is of special interest in this context due to its very low frequency content that coincides with the most important frequency range for both building vibration and human perception. This paper presents results from field tests with measurements of noise and building vibrations from sonic booms performed at the Tretyakovo airport in Russia. Transmission loss from outdoor to indoor noise, noise induced floor vibration and whole building vibration are determined. Furthermore, the measured acoustic vibration admittance is used to estimate vibration values in the same building from low boom flight passages using synthesized sound pressure time series. Boom induced floor vibration both from the measured flight passages in Russia and from synthesized low boom time series are estimated also for a lightweight wooden building, using previously measured acoustic vibration admittances. The results clearly show perceptible levels of vibrations from sonic boom along with a great influence of the building type which indicates that there can be a big difference between the European countries depending on the building tradition. Finally, it is shown that outdoor sound levels weighted with the C-curve correlates best with frequency weighted floor vibration values. Building vibration induced by sonic boom - field test in Russia

Probabilistic Tsunami Hazard Analysis (PTHA) quantifies the probability of exceeding a specified inundation intensity at a given location within a given time interval. PTHA provides scientific guidance for tsunami risk analysis and risk management, including coastal planning and early warning. Explicit computation of site-specific PTHA, with an adequate discretization of source scenarios combined with high-resolution numerical inundation modelling, has been out of reach with existing models and computing capabilities, with tens to hundreds of thousands of moderately intensive numerical simulations being required for exhaustive uncertainty quantification. In recent years, more efficient GPU-based High-Performance Computing (HPC) facilities, together with efficient GPU-optimized shallow water type models for simulating tsunami inundation, have now made local long-term hazard assessment feasible. A workflow has been developed with three main stages: 1) Site-specific source selection and discretization, 2) Efficient numerical inundation simulation for each scenario using the GPU-based Tsunami-HySEA numerical tsunami propagation and inundation model using a system of nested topo-bathymetric grids, and 3) Hazard aggregation. We apply this site-specific PTHA workflow here to Catania, Sicily, for tsunamigenic earthquake sources in the Mediterranean. We illustrate the workflows of the PTHA as implemented for High-Performance Computing applications, including preliminary simulations carried out on intermediate scale GPU clusters. We show how the local hazard analysis conducted here produces a more fine-grained assessment than is possible with a regional assessment. However, the new local PTHA indicates somewhat lower probabilities of exceedance for higher maximum inundation heights than the available regional PTHA. The local hazard analysis takes into account small-scale tsunami inundation features and non-linearity which the regional-scale assessment does not incorporate. However, the deterministic inundation simulations neglect some uncertainties stemming from the simplified source treatment and tsunami modelling that are embedded in the regional stochastic approach to inundation height estimation. Further research is needed to quantify the uncertainty associated with numerical inundation modelling and to properly propagate it onto the hazard results, to fully exploit the potential of site-specific hazard assessment based on massive simulations.

The Center of Excellence for Exascale in Solid Earth (ChEESE; https://cheese-coe.eu) is promoting the use of upcoming Exascale and extreme performance computing capabilities in the area of Solid Earth by harnessing institutions in charge of operational monitoring networks, tier-0 supercomputing centers, academia, hardware developers and third parties from SMEs, Industry and public-governance. The scientific challenging ambition is to prepare 10 European open-source flagship codes to solve Exascale problems on computational seismology, magnetohydrodynamics, physical volcanology, tsunamis, and data analysis. Preparation to Exascale is considering code inter-kernel aspects of simulation workflows like data management and sharing following the FAIR principles, I/O, post-process and visualization. The project is articulated around 12 Pilot Demonstrators (PDs) in which flagship codes are used for near real-time seismic simulations and full-wave inversion, ensemble-based volcanic ash dispersal forecasts, faster than real-time tsunami simulations and physics-based hazard assessments for earthquakes, volcanoes and tsunamis. This is a first step towards enabling of operational e-services requiring of extreme HPC on urgent computing, early warning forecast of geohazards, hazard assessment and data analytics. Additionally, and in collaboration with the European Plate Observing System (EPOS), ChEESE will promote and facilitate the integration of HPC services to widen the access to codes and fostering transfer of know-how to Solid Earth user communities. In this regard, the project aims at acting as a hub to foster HPC across the Solid Earth Community and related stakeholders and to provide specialized training on services and capacity building measures.

The 2018 Anak Krakatoa volcano flank collapse and tsunami caused several hundred fatalities. There was no early warning system in place for the landslide triggered tsunami, and there is a lack in understanding on how the failure mechanism affected landslide dynamics and tsunami generation, which we focus on in this study. While researchers previously have modelled the collapse as an instantaneous release, we here illuminate how different landslide failure scenarios, including a gradually released flank failure, influence the tsunami generation. We simulate the material movement by using a viscoplastic flow model with Herschel-Bulkley rheology and we employ a depth-averaged model to both the landslide and the tsunami propagation. A sensitivity study to the gradual mass release, total release volume, the material yield strength, the remoulding coefficient, and landslide directivity is used to shed light on the tsunami generation. Our analysis indicates that an instantaneous mass release in 125 degree SW direction fits the observed waveforms at coastal gauge stations best. In our simulations, we observe, as many other authors, discrepancies between simulated and observed arrival times and wave periods offshore Sumatra. Hence, we have also investigated sensitivity to the bathymetric depth by varying the water depth in areas near the source. Finally, we simulate the tsunami inundation at two coastal sites in southwestern Java using open-source topographic data. Given the limitations in the topographic data, a reasonably good agreement between the simulations and observations are obtained.

Tsunami simulation in the framework of Tsunami Early Warning Systems (TEWS) is a quite recent achievement, but still limited regarding the size of the problem and restricted to tsunami wave propagation. Faster Than Real Time (FTRT) tsunami simulations require greatly improved and highly efficient computational methods to achieve extremely fast and effective calculations. HPC facilities have the role to bring this efficiency to a maximum possible and drastically reducing computational times. Putting these two ingredients together is the aim of Pilot Demonstrator 2 (PD2) in ChEESE project. This PD will comprise both earthquake and landslide sources. Earthquake tsunami generation is to an extent simpler than landslide tsunami generation, as landslide generated tsunamis depend on the landslide dynamics which necessitate coupling dynamic landslide simulation models to the tsunami propagation. In both cases, FTRT simulations in several contexts and configurations will be the final aim of this pilot.Among the objectives of our work in ChEESE project are achieving unprecedented FTRT tsunami computations with existing models and investigate the scalability limits of such models; increasing the size of the problems by increasing spatial resolution and/or producing longer simulations while still computing FTRT, dealing with problems and resolutions never done before; developing a TEWS including inundation for a particular target coastal zone, or numerous scenarios allowing PTHA (PD7) and PTF (PD8), an aim unattainable at present or including more physics in shallow water models for taking into account dispersive effects.Up to now, the two European tsunami flagship codes selected by ChESEE project (Tsunami-HySEA and Landslide-HySEA) have been audit and efficiency further improved. The improved code versions have been tested in three European 0-Tier HPC facilities: BSC (Spain), CINECA (Italy) and Piz Daint (Switzerland) using up to 32 NVIDIA Graphic Cards (P100 and V100) for scaling purposes. Computing times have been drastically reduced and a PTF study composed by around 10,000 scenarios (4 nested grids, 12 M cells, 8 hours simulations) have been computed in 6 days of wall-clock computations in the 64 GPUs available for us at the BSC. Acknowledgements. This research has been partially supported by the Spanish Government Research project MEGAFLOW (RTI2018-096064-B-C21), Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech and ChEESE project (EU Horizon 2020, grant agreement Nº 823844), https://cheese-coe.eu/

On 28 September 2018 a Mw 7.5 strike-slip earthquake occurred on the Palu-Koro fault system in the Sulawesi Island. Immediately after the earthquake a powerful tsunami hit the Palu Bay causing large damages and numerous fatalities.Several works, inverting seismic or geodetic data, clearly estimated the slip distribution of this event, but the causative source of the tsunami is still not completely understood; indeed, the strike-slip mechanism of the seismic source alone might not be sufficient to explain the large runups observed (> 6 m) along the coast of the Palu Bay, and thus one or more additional non-seismic sources like a landslide could have contributed to generate the big tsunami. An insight of that can be found in an extraordinary collection of amateur videos, and on the only available tide gauge in the Bay, at Pantoloan, that showed evidence for a short period wave of at least 2-3 minutes, compatible with a landslide.In this study, we attempt to discriminate the contribution in the tsunami generation of both the seismic source and some supposed landslides distributed along the coast of the Bay.In particular, we attempt to estimate the causative source of the tsunami by means of a nonlinear joint inversion of geodetic (InSAR) and runup data. We use a fault geometry consistent with the Sentinel-2 optical analysis results and analytically compute the geodetic Green’s functions. The same fault model is used to compute the initial condition for the seismic tsunami Green’s functions, including the contribution of the horizontal deformation due to the gradient of the bathymetry (10 m spatial resolution); the landslide tsunami Green’s functions are computed the software BingClaw by placing several hypothetical sources in the Bay. In both the cases the tsunami propagation is modelled by numerically solving the nonlinear shallow water equations.In this work we also attempt to address the validity of Green’s functions approach (linearity) for earthquake and landslide sources as well as the wave amplitude offshore as predictor of nearby runup.

The tsunami disasters of 2004 in the Indian Ocean and of 2011 along the Tohoku coast of Japan revealed severe gaps between the anticipated risk and consequences, with resulting loss of life and property. A similar observation is also relevant for the smaller, yet disastrous, tsunamis with unusual source characteristics such as the recent events in Palu Bay and Sunda Strait in 2018. The severe consequences were underestimated in part due to the lack of rigorous and accepted hazard analysis methods and large uncertainty in forecasting the tsunami sources. Population response to small recent tsunamis in the Mediterranean also revealed a lack of preparedness and awareness. While there is no absolute protection against large tsunamis, a more accurate analysis of the potential risk can help to minimize losses. The tsunami community has made significant progress in understanding tsunami hazard from seismic sources. However, this is only part of the inputs needed to effectively manage tsunami risk, which should be understood more holistically, including non-seismic sources, vulnerability in different dimensions and the overall societal effects, in addition to its interaction with other hazards and cascading effects. Moreover, higher standards need to be achieved to manage and quantify uncertainty, which govern our basis for tsunami risk decision making. Hence, a collective community effort is needed to effectively handle all these challenges across disciplines and trades, from researchers to stakeholders. To coordinate and streamline these activities and make progress towards implementing the Sendai Framework of Disaster Risk Reduction (SFDRR) the Global Tsunami Model network (GTM) was initiated in 2015 towards enhancing our understanding of tsunami hazard and risk from a local to global scale. Here, we focus on coordinated European efforts, sharing the same goals as GTM, towards improving standards and best practices for tsunami risk reduction. The networking initiative, AGITHAR (Accelerating Global science In Tsunami HAzard and Risk Analysis), is a European COST Action, aims to assess, benchmark, improve, and document methods to analyse tsunami hazard and risk, understand and communicate the uncertainty involved, and interact with stakeholders in order to understand the societal needs and thus contribute to their effort to minimize losses. In this presentation, we provide an overview of the suite of methodologies used for tsunami hazard and risk analysis, review state of the art in global tsunami hazard and risk analysis, dating back to results from the Global Risk Model in 2015, and highlight possible gaps and challenges. We further discuss how AGITHAR and GTM will address how to tackle these challenges, and finally, discuss how global and regional structures such as the European Plate Observing System (EPOS) and the UNDRR Global Risk Assessment Framework (GRAF) can facilitate and mutually benefit towards an integrated framework of services aiding improved understanding of multiple hazards.

Sediment slumps are known to have generated important tsunamis such as the 1998 Papua New Guinea (PNG) and the 1929 Grand Banks events. Tsunami modellers commonly use solid blocks with short run-out distances to simulate these slumps. While such methods have the obvious advantage of being simple to use, they offer little or no insight into physical processes that drive the events. The importance of rotational slump motion to tsunamigenic potential is demonstrated in this study by employing a viscoplastic landslide model with Herschel-Bulkley rheology. A large number of simulations for different material properties and landslide configurations are carried out to link the slump's deformation, rheology, its translational and rotational kinematics, to its tsunami-genesis. The yield strength of the slump is shown to be the primary material property that determines the tsunami-genesis. This viscoplastic model is further employed to simulate the 1929 Grand Banks tsunami using updated geological source information. The results of this case study suggest that the viscoplastic model can be used to simulate complex slump induced tsunami. The simulations of the 1929 Grand Banks event also indicate that a pure slump mechanism is more tsunamigenic than a corresponding translational landslide mechanism.

The 2018 Anak Krakatoa volcano flank collapse generated a tsunami that impacted the Sunda Strait coastlines. In the absence of a tsunami early warning system, it caused several hundred fatalities. There are ongoing discussions to understand how the failure mechanism of this event affected landslide dynamics and tsunami generation. In this paper, the sensitivity to different failure scenarios on the tsunami generation is investigated through numerical modelling. To this end, the rate of mass release, the landslide volume, the material yield strength, and orientation of the landslide failure plane are varied to shed light on the failure mechanism, landslide evolution, and tsunami generation. We model the landslide dynamics using the depth-averaged viscoplastic flow model BingClaw, coupled with depth-averaged long wave and shallow water type models to simulated tsunami propagation. We are able to match fairly well the observed tsunami surface elevation amplitudes and inundation heights in selected area with the numerical simulations. However, as observed by other authors, discrepancies in simulated and observed arrival times for some of the offshore gauges are found, which raises questions related to the accuracy of the available bathymetry. For this purpose, further sensitivity studies changing the bathymetric depth near the source area are carried out. With this alteration we are also able to match better the arrival times of the waves.

Landslide tsunamis and impulse waves are hazardous events with severe socioeconomic impacts. A long standing problem with simulations of these events is the generation stage, where landslides and water interact. Depth-averaged models like the Saint-Venant or Boussinesq Equations lose their validity for such applications. Therefore, we have to rely on a full treatment of the hydrodynamics, for instance by applying the Navier-Stokes Equations and Computational Fluid Dynamics (CFD). However, applications of fully three-dimensional methods to landslide tsunamis are sparse, and have often been outperformed by depth averaged models when compared to experimental data. In this work, we evaluate the multiphase Navier-Stokes Equations as implemented in OpenFOAM® in terms of impulse wave generation. We focus on a simplified two-dimensional setup where the landslide consists of water, in order to circumvent additional complexities due to treatment of landslide rheologies. We conduct a thorough grid refinement study and compare results to experiments to investigate model convergence, stability, and accuracy. The simulations display good agreement with the experimental data if the Courant-Friedrichs-Lewy (CFL) condition is modified to account for the specific properties of the multiphase system. Further, we use the validated model for sensitivity studies and to review various scaling relations for landslide generated tsunamis. The application of numerical models allows us to perform broad parametric tests and dissect the underlying physics of these predictive equations systematically. We found that the first wave crest may be well estimated by solely the landslide mass in our setting. Including additional properties related to landslide momentum can improve the predictive skill, while other parameters lead to no substantial improvement.

Applying probabilistic methods to infrequent but devastating natural events is intrinsically challenging. For tsunami analyses, a suite of geophysical assessments should be in principle evaluated because of the different causes generating tsunamis (earthquakes, landslides, volcanic activity, meteorological events, and asteroid impacts) with varying mean recurrence rates. Probabilistic Tsunami Hazard Analyses (PTHAs) are conducted in different areas of the world at global, regional, and local scales with the aim of understanding tsunami hazard to inform tsunami risk reduction activities. PTHAs enhance knowledge of the potential tsunamigenic threat by estimating the probability of exceeding specific levels of tsunami intensity metrics (e.g., run-up or maximum inundation heights) within a certain period of time (exposure time) at given locations (target sites); these estimates can be summarized in hazard maps or hazard curves. This discussion presents a broad overview of PTHA, including (i) sources and mechanisms of tsunami generation, emphasizing the variety and complexity of the tsunami sources and their generation mechanisms, (ii) developments in modeling the propagation and impact of tsunami waves, and (iii) statistical procedures for tsunami hazard estimates that include the associated epistemic and aleatoric uncertainties. Key elements in understanding the potential tsunami hazard are discussed, in light of the rapid development of PTHA methods during the last decade and the globally distributed applications, including the importance of considering multiple sources, their relative intensities, probabilities of occurrence, and uncertainties in an integrated and consistent probabilistic framework.

Landslides are the second-most important cause of tsunamis after earthquakes, and their potential for generating large tsunamis depend on the slide process. Among the world's largest submarine landslides is the Storegga Slide that generated an ocean-wide catastrophic tsunami, while no traces of a tsunami generated from the similar and nearby Traenadjupet Slide have been found. Previous models for such landslide tsunamis have not been able to capture the complexity of the landslide processes, and are at odds with geotechnical and geomorphological data that reveal retrogressive landslide development. The tsunami generation from these massive events are here modeled with new methods that incorporate complex retrogressive slide motion. We show that the tsunamigenic strength is closely related to the retrogressive development, and explain for the first time, why similar giant landslides can produce very different tsunamis, sometimes smaller than anticipated. Because these slide mechanisms are common for submarine landslides, modeling procedures for dealing with their associated tsunamis should be revised.

Aircraft, supersonic flights, blasts, and explosions emit sound with substantial energy below 100 Hz. When the low frequency sound is transmitted inside a building, it generates vibration and rattling that may lead to annoyance. Our understanding of these low frequency phenomena is presently limited. In this paper, we attempt to improve our computational capabilities related to the low frequency sound transmission. For this purpose, a finite element methodology that incorporates a two-way coupled fluid-structure interaction, has been developed. Results from a broad experimental investigation of low frequency sound transmission are compared with the numerical finite element simulations. Plain walls, and walls with windows are studied. Close agreement between the simulations and the laboratory measurement data is obtained in the frequency range investigated (10–100 Hz). It was found that structural connections were of large importance for modeling the vibration and sound transmission. The windows control the low frequency transmission from 15 to 30 Hz, whereas the walls control the sound transmission from 30 to 100 Hz. Mitigation of vibrations and rattling induced by low frequency sound therefore needs to consider both wall and window construction.

A large rockslide was released from the inner Askja caldera into Lake Askja, Iceland, on 21 July 2014. Upon entering the lake, it caused a large tsunami that traveled about ∼3 km across the lake and inundated the shore with vertical runup measuring up to 60–80 m. Following the event, comprehensive field data were collected, including GPS measurements of the inundation and multibeam echo soundings of the lake bathymetry. Using this exhaustive data set, numerical modeling of the tsunami has been conducted using both a nonlinear shallow water model and a Boussinesq-type model that includes frequency dispersion. To constrain unknown landslide parameters, a global optimization algorithm, Differential Evolution, was employed, resulting in a parameter set that minimized the deviation from measured inundation. The tsunami model of Lake Askja is the first example where we have been able to utilize field data to show that frequency dispersion is needed to explain the tsunami wave radiation pattern and that shallow water theory falls short. We were able to fit the trend in tsunami runup observations around the entire lake using the Boussinesq model. In contrast, the shallow water model gave a different runup pattern and produced pronounced offsets in certain areas. The well-documented Lake Askja tsunami thus provided a unique opportunity to explore and capture the essential physics of landslide tsunami generation and propagation through numerical modeling. Moreover, the study of the event is important because this dispersive nature is likely to occur for other subaerial impact tsunamis., Nordic Centre of Excellence on Resilience and Societal Security (NORDRESS) Research Council of Norway -231252 Icelandic Avalanche and Landslide Fund Vatnajokull National Park

The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-East Atlantic, the Mediterranean, and connected Seas (NEAM). In this online data product, the hazard results are provided by hazard curves calculated at 2,343 Points of Interest (POI), distributed in the North-East Atlantic (1,076 POIs), the Mediterranean Sea (1,130 POIs), and the Black Sea (137 POIs) at an average spacing of ~20 km. For each POI, hazard curves are given for the mean, 2nd, 16th, 50th, 84th, and 98th percentiles. Maps derived from hazard curves are Probability maps for Maximum Inundation Heights (MIH) of 1, 2, 5, 10, 20 meters; Hazard maps for Average Return Periods (ARP) of 500, 1,000, 2,500, 5,000, 10,000 years. For each map, precalculated displays are provided for the mean, the 16th percentile, and the 84th percentile. All data are also made accessible through an interactive web mapper and through Open Geospatial Consortium standard protocols. The model was prepared in the framework of the European Project TSUMAPS-NEAM (http://www.tsumaps-neam.eu/) funded by the mechanism of the European Civil Protection and Humanitarian Aid Operations (grant no. ECHO/SUB/2015/718568/PREV26)., Co-financed by the European-Union Civil Protection Mechanism Agreement Number: ECHO/SUB/2015/718568/PREV26