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Tsunamis are unpredictable and infrequent but potentially large impact natural disasters. To prepare, mitigate and prevent losses from tsunamis, probabilistic hazard and risk analysis methods have been developed and have proved useful. However, large gaps and uncertainties still exist and many steps in the assessment methods lack information, theoretical foundation, or commonly accepted methods. Moreover, applied methods have very different levels of maturity, from already advanced probabilistic tsunami hazard analysis for earthquake sources, to less mature probabilistic risk analysis. In this review we give an overview of the current state of probabilistic tsunami hazard and risk analysis. Identifying research gaps, we offer suggestions for future research directions. An extensive literature list allows for branching into diverse aspects of this scientific approach. © Copyright © 2021 Behrens, Løvholt, Jalayer, Lorito, Salgado-Gálvez, Sørensen, Abadie, Aguirre-Ayerbe, Aniel-Quiroga, Babeyko, Baiguera, Basili, Belliazzi, Grezio, Johnson, Murphy, Paris, Rafliana, De Risi, Rossetto, Selva, Taroni, Del Zoppo, Armigliato, Bureš, Cech, Cecioni, Christodoulides, Davies, Dias, Bayraktar, González, Gritsevich, Guillas, Harbitz, Kânoǧlu, Macías, Papadopoulos, Polet, Romano, Salamon, Scala, Stepinac, Tappin, Thio, Tonini, Triantafyllou, Ulrich, Varini, Volpe and Vyhmeister. This article is based upon work from COST Action CA18109 AGITHAR, supported by COST (European Cooperation in Science and Technology). VB and PC obtained support through the VES20 Inter-Cost LTC 20020 project. MS-G obtained support through the Severo Ochoa Centers of Excellence Program (Ref. CEX 2018–000797-S). TU acknowledges funding from the European Union’s Horizon 2020 research and innovation program (ChEESE project, Grant Agreement No. 823844).

The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-eastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a three-phase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decision-making process based on a multiple-expert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step-1) probabilistic earthquake model; Step-2) tsunami generation and modeling in deep water; Step-3) shoaling and inundation; Step-4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level-0 always describes the input data; the other Levels describe the intermediate results needed to proceed from one Step to another. Alternative datasets and models were considered in the implementation. The epistemic hazard uncertainty was quantified through an ensemble modeling technique accounting for alternative models’ weights and yielding a distribution of hazard curves represented by the mean and various percentiles. Hazard curves were calculated at 2,343 Points of Interest (POI) distributed at an average spacing of ∼20 km. Precalculated probability maps for five maximum inundation heights (MIH) and hazard intensity maps for five average return periods (ARP) were produced from hazard curves. In the entire NEAM Region, MIHs of several meters are rare but not impossible. Considering a 2% probability of exceedance in 50 years (ARP≈2,475 years), the POIs with MIH >5 m are fewer than 1% and are all in the Mediterranean on Libya, Egypt, Cyprus, and Greece coasts. In the North-East Atlantic, POIs with MIH >3 m are on the coasts of Mauritania and Gulf of Cadiz. Overall, 30% of the POIs have MIH >1 m. NEAMTHM18 results and documentation are available through the TSUMAPS-NEAM project website (http://www.tsumaps-neam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute in-depth analyses at local scales, it represents the first action to start local and more detailed hazard and risk assessments and contributes to designing evacuation maps for tsunami early warning.

Site-specific seismic probabilistic tsunami hazard analysis (SPTHA) is a computationally demanding task, as it requires, in principle, a huge number of high-resolution numerical simulations for producing probabilistic inundation maps. We implemented an efficient and robust methodology using a filtering procedure to reduce the number of numerical simulations needed while still allowing for a full treatment of aleatory and epistemic uncertainty. Moreover, to avoid biases in tsunami hazard assessment, we developed a strategy to identify and separately treat tsunamis generated by near-field earthquakes. Indeed, the coseismic deformation produced by local earthquakes necessarily affects tsunami intensity, depending on the scenario size, mechanism and position, as coastal uplift or subsidence tends to diminish or increase the tsunami hazard, respectively. Therefore, we proposed two parallel filtering schemes in the far- and the near-field, based on the similarity of offshore tsunamis and hazard curves and on the similarity of the coseismic fields, respectively. This becomes mandatory as offshore tsunami amplitudes can not represent a proxy for the coastal inundation in the case of near-field sources. We applied the method to an illustrative use case at the Milazzo oil refinery (Sicily, Italy). We demonstrate that a blind filtering procedure can not properly account for local sources and would lead to a nonrepresentative selection of important scenarios. For the specific source–target configuration, this results in an overestimation of the tsunami hazard, which turns out to be correlated to dominant coastal uplift. Different settings could produce either the opposite or a mixed behavior along the coastline. However, we show that the effects of the coseismic deformation due to local sources can not be neglected and a suitable correction has to be employed when assessing local-scale SPTHA, irrespective of the specific signs of coastal displacement.

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.

The Italian Tsunami Alert Center based at the Istituto Nazionale di Geofisica e Vulcanologia (CAT-INGV) has been monitoring the Mediterranean seismicity in the past 8 yr to get fast and reliable information for seismically induced tsunami warnings. CAT-INGV is a tsunami service provider in charge of monitoring the seismicity of the Mediterranean Sea and of alerting Intergovernmental Oceanographic Commission (IOC)/UNESCO subscriber Member States and the Italian Department of Civil Protection of a potentially impending tsunami, in the framework of the Tsunami Warning and Mitigation System in the North-eastern Atlantic, the Mediterranean and connected seas (NEAMTWS). CAT-INGV started operating in 2013 and became operational in October 2016. Here, after describing the NEAMTWS in the framework of the global effort coordinated by IOC/UNESCO, we focus on the tsunami hazard in the Mediterranean Sea. We then describe CAT-INGV mandate, functioning, and operational procedures. Furthermore, the article discusses the lessons learned from past events occurring in the Mediterranean Sea, such as the Kos-Bodrum in 2017 (Mw 6.6) and the Samos-Izmir in 2020 (Mw 7.0) earthquakes, which generated moderately damaging tsunamis. Based on these lessons, we discuss some potential improvements for the CAT-INGV and the NEAMTWS, including better seismic and sea level instrumental coverage. We emphasize the need for tsunami risk awareness raising, better preparation, and full implementation of the tsunami warning “last-mile” to foster the creation of a more integrated, interoperable, and sustainable risk reduction framework. If we aim to be better prepared for the next tsunami, these important challenges should be prioritized in the agenda of the IOC/UNESCO Member States and the European Commission.

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.

Finite-fault models for the 2010Mw8.8 Maule, Chile earthquake indicate bilateral rupture with large-slip patches located north and south of the epicenter. Previous studies also show that this event features significant slip in the shallow part of the megathrust, which is revealed through correction of the forward tsunami modeling scheme used in tsunami inversions. The presence of shallow slip is consistent with the coseismic seafloor deformation measured off the Maule region adjacent to the trench and confirms that tsunami observations are particularly important for constraining far-offshore slip. Here, we benchmark the method of Optimal Time Alignment (OTA) of the tsunami waveforms in the joint inversion of tsunami (DART and tide-gauges) and geodetic (GPS, InSAR, land-leveling) observations for this event. We test the application of OTA to the tsunami Green’s functions used in a previous inversion. Through a suite of synthetic tests we show that if the bias in the forward model is comprised only of delays in the tsunami signals, the OTA can correct them precisely, independently of the sensors (DART or coastal tide-gauges) and, to the first-order, of the bathymetric model used. The same suite of experiments is repeated for the real case of the 2010 Maule earthquake where, despite the results of the synthetic tests, DARTs are shown to outperform tide-gauges. This gives an indication of the relative weights to be assigned when jointly inverting the two types of data. Moreover, we show that using OTA is preferable to subjectively correcting possible time mismatch of the tsunami waveforms. The results for the source model of the Maule earthquake show that using just the first-order modeling correction introduced by OTA confirms the bilateral rupture pattern around the epicenter, and, most importantly, shifts the inferred northern patch of slip to a shallower position consistent with the slip models obtained by applying more complex physics-based corrections to the tsunami waveforms. This is confirmed by a slip model refined by inverting geodetic and tsunami data complemented with a denser distribution of GPS data nearby the source area. The models obtained with the OTA method are finally benchmarked against the observed seafloor deformation off the Maule region. We find that all of the models using the OTA well predict this offshore coseismic deformation, thus overall, this benchmarking of the OTA method can be considered successful.

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.

SUMMARY The behaviour of tsunami waves at any location depends on the local morphology of the coasts, the encountered bathymetric features, and the characteristics of the source. However, the importance of accurately modelling the geometric properties of the causative fault for simulations of seismically induced tsunamis is rarely addressed. In this work, we analyse the effects of using two different geometric models of the subduction interface of the Calabrian Arc (southern Italy, Ionian Sea) onto the simulated tsunamis: a detailed 3-D subduction interface obtained from the interpretation of a dense network of seismic reflection profiles, and a planar interface that roughly approximates the 3-D one. These models can be thought of as representing two end-members of the level of knowledge of fault geometry. We define three hypothetical earthquake ruptures of different magnitudes (Mw 7.5, 8.0, 8.5) on each geometry. The resulting tsunami impact is evaluated at the 50-m isobath in front of coastlines of the central and eastern Mediterranean. Our results show that the source geometry imprint is evident on the tsunami waveforms, as recorded at various distances and positions relative to the source. The absolute differences in maximum and minimum wave amplitudes locally exceed one metre, and the relative differences remain systematically above 20 per cent with peaks over 40 per cent. We also observe that tsunami energy directivity and focusing due to bathymetric waveguides take different paths depending on which fault is used. Although the differences increase with increasing earthquake magnitude, there is no simple rule to anticipate the different effects produced by these end-member models of the earthquake source. Our findings suggest that oversimplified source models may hinder our fundamental understanding of the tsunami impact and great care should be adopted when making simplistic assumptions regarding the appropriateness of the planar fault approximation in tsunami studies. We also remark that the geological and geophysical 3-D fault characterization remains a crucial and unavoidable step in tsunami hazard analyses.

We report on the first ‘real-time' application of the BET_UNREST (Bayesian Event Tree for Volcanic Unrest) probabilistic model, during a VUELCO Simulation Exercise carried out on the island of Dominica, Lesser Antilles, in May 2015. Dominica has a concentration of nine potentially active volcanic centers and frequent volcanic earthquake swarms at shallow depths, intense geothermal activity and recent phreatic explosions (1997) indicate the region is still active. The exercise scenario was developed in secret by a team of scientists from University of West Indies (Trinidad and Tobago) and University of Auckland (New Zealand). The simulated unrest activity was provided to the exercise's Scientific Team in three ‘phases' through exercise injects comprising processed monitoring data. We applied the newly created BET_UNREST model through its software implementation PyBetUnrest, to estimate the probabilities of having i) unrest of ii) magmatic, hydrothermal or tectonic origin, which may or may not lead to iii) an eruption. The probabilities obtained for each simulated phase raised controversy and intense deliberations among the members of the scientific team. The results were often considered to be ‘too high', and were not included in any of the reports presented to ODM (Office for Disaster Management) revealing interesting crisis communication challenges. We concluded that the PyBetUnrest application itself was successful and brought the tool one step closer to a full implementation. However, as with any newly proposed method it needs more testing, and in order to be able to use it in the future we make a series of recommendations for future applications. This article is protected by copyright. All rights reserved.

Although most of volcanic hazard studies focus on magmatic eruptions, volcanic hazardous events can also occur when no migration of magma can be recognized. Examples are tectonic and hydrothermal unrest that may lead to phreatic eruptions. Recent events (e.g., Ontake eruption on September 2014) have demonstrated that phreatic eruptions are still hard to forecast, despite being potentially very hazardous. For these reasons, it is of paramount importance to identify indicators that define the condition of nonmagmatic unrest, in particular for hydrothermal systems. Often, this type of unrest is driven by movement of fluids, requiring alternative monitoring setups, beyond the classical seismic-geodetic-geochemical architectures. Here we present a new version of the probabilistic BET (Bayesian Event Tree) model, specifically developed to include the forecasting of nonmagmatic unrest and related hazards. The structure of the new event tree differs from the previous schemes by adding a specific branch to detail nonmagmatic unrest outcomes. A further goal of this work consists in providing a user-friendly, open-access, and straightforward tool to handle the probabilistic forecast and visualize the results as possible support during a volcanic crisis. The new event tree and tool are here applied to Kawah Ijen stratovolcano, Indonesia, as exemplificative application. In particular, the tool is set on the basis of monitoring data for the learning period 2000-2010, and is then blindly applied to the test period 2010-2012, during which significant unrest phases occurred.

Co-auteur étranger; International audience; A probabilistic hazard analysis of tsunami generated by subaqueous volcanic explosion is applied to the Campi Flegrei caldera (Campania, Italy). An event tree is developed to quantify the tsunami hazard due to the submarine explosions by: i) defining potential size classes of explosion magnitude on the basis of past volcanic activity in the Campi Flegrei caldera and sites in the underwater part of the caldera; ii) simulating the generation and propagation of the consequent tsunami waves able to reach the coasts of the Campania region for all combinations of tsunami-generating vents and sizes; and iii) quantifying the tsunami probability and relative uncertainty, conditional upon the occurrence of an underwater eruption at Campi Flegrei. Tsunami hazard generated by subaqueous volcanic explosions is considered crucial because of its potential high impact on the densely populated coastal areas of the Pozzuoli Bay and Gulf of Naples even if the probability for eruptions in the submarine part of the caldera is certainly low. The tsunami hazard analysis is presented using conditional hazard curves and maps, that is calculating the probability (and relative uncertainties) of exceeding given tsunami intensity thresholds (wave amplitudes at the coast), given the occurrence of a subaqueous eruption. The results indicate that a significant tsunami hazard exists in many areas of the Bay of Naples.

Phreatic eruptions, although posing a serious threat to people in crater proximity, are often underestimated and have been comparatively understudied. The detailed eruption catalogue for Ruapehu Volcano (New Zealand) provides an exceptional opportunity to study the statistics of recurring phreatic explosions at a crater lake volcano. We performed a statistical analysis on this phreatic eruption database, which suggests that phreatic events at Ruapehu do not follow a Poisson process. Instead they tend to cluster, which is possibly linked to an increased heat flow during periods of a more shallow-seated magma column. Larger explosions are more likely to follow shortly after smaller events, as opposed to longer periods of quiescence. The absolute probability for a phreatic explosion to occur at Ruapehu within the next month is about 10%, when averaging over the last 70 years of recording. However, the frequency of phreatic explosions is significantly higher than the background level in years prior to magmatic episodes. Combining clast ejection simulations with a Bayesian event tree tool (PyBetVH) we perform a probabilistic assessment of the hazard due to ballistic ejecta in the summit area of Ruapehu, which is frequently visited by hikers. Resulting hazard maps show that the absolute probability for the summit to be affected by ballistics within the next month is up to 6%. The hazard is especially high on the northern lakeshore, where there is a mountain refuge. Our results contribute to the local hazard assessment as well as the general perception of hazards due to steam-driven explosions.

Nowadays numerical models are a powerful tool in tsunami research since they can be used (i) to reconstruct modern and historical events, (ii) to cast new light on tsunami sources by inverting tsunami data and observations, (iii) to build scenarios in the frame of tsunami mitigation plans, and (iv) to produce forecasts of tsunami impact and inundation in systems of early warning. In parallel with the general recognition of the importance of numerical tsunami simulations, the demand has grown for reliable tsunami codes, validated through tests agreed upon by the tsunami community. This paper presents the tsunami code UBO-TSUFD that has been developed at the University of Bologna, Italy, and that solves the non-linear shallow water (NSW) equations in a Cartesian frame, with inclusion of bottom friction and exclusion of the Coriolis force, by means of a leapfrog (LF) finite-difference scheme on a staggered grid and that accounts for moving boundaries to compute sea inundation and withdrawal at the coast. Results of UBO-TSUFD applied to four classical benchmark problems are shown: two benchmarks are based on analytical solutions, one on a plane wave propagating on a flat channel with a constant slope beach; and one on a laboratory experiment. The code is proven to perform very satisfactorily since it reproduces quite well the benchmark theoretical and experimental data. Further, the code is applied to a realistic tsunami case: a scenario of a tsunami threatening the coasts of eastern Sicily, Italy, is defined and discussed based on the historical tsunami of 11 January 1693, i.e. one of the most severe events in the Italian history.

Most volcanic hazard studies focus on magmatic eruptions and their accompanying phenomena. However, hazardous volcanic events can also occur during non-magmatic unrest, defined as a state of volcanic unrest in which no migration of magma is recognised. Examples include tectonic unrest, and hydrothermal unrest that may lead to phreatic eruptions. Recent events (e.g. Ontake eruption, September 2014) have demonstrated that the successful forecasting of phreatic eruptions is still very difficult. It is therefore of paramount importance to identify indicators that define the state of non-magmatic unrest. Often, this type of unrest is driven by fluids-on-the-move, requiring alternative monitoring setups, beyond the classical seismic-geodetic-geochemical architectures. Here we present a new version of the probabilistic model BET (Bayesian Event Tree), called BET_UNREST, specifically developed to include the forecasting of non-magmatic unrest and related hazards. The structure of BET_UNREST differs from the previous BET_EF (BET for Eruption Forecasting) by adding a dedicated branch to detail non-magmatic unrest outcomes. Probabilities are calculated at each node by merging prior models and past data with new incoming monitoring data, and the results can be updated any time new data has been collected. Monitoring data are weighted through pre-defined thresholds of anomaly, as in BET_EF. The BET_UNREST model is introduced here, together with its software implementation PyBetUnrest, with the aim of creating a user-friendly, open-access, and straightforward tool to support short-term volcanic forecasting (already available on the VHub platform). The BET_UNREST model and PyBetUnrest tool are tested through three case studies in the frame of the EU VUELCO project.

The 2011 Tohoku earthquake produced an unexpected large amount of shallow slip greatly contributing to the ensuing tsunami. How frequent are such events? How can they be efficiently modelled for tsunami hazard? Stochastic slip models, which can be computed rapidly, are used to explore the natural slip variability; however, they generally do not deal specifically with shallow slip features. We study the systematic depth-dependence of slip along a thrust fault with a number of 2D dynamic simulations using stochastic shear stress distributions and a geometry based on the cross section of the Tohoku fault. We obtain a probability density for the slip distribution, which varies both with depth, earthquake size and whether the rupture breaks the surface. We propose a method to modify stochastic slip distributions according to this dynamically-derived probability distribution. This method may be efficiently applied to produce large numbers of heterogeneous slip distributions for probabilistic tsunami hazard analysis. Using numerous M9 earthquake scenarios, we demonstrate that incorporating the dynamically-derived probability distribution does enhance the conditional probability of exceedance of maximum estimated tsunami wave heights along the Japanese coast. This technique for integrating dynamic features in stochastic models can be extended to any subduction zone and faulting style.

Assessment of volcanic hazards is necessary for risk mitigation. Typically, hazard assessment is based on one or a few, subjectively chosen representative eruptive scenarios, which use a specific combination of eruptive sizes and intensities to represent a particular size class of eruption. While such eruptive scenarios use a range of representative members to capture a range of eruptive sizes and intensities in order to reflect a wider size class, a scenario approach neglects to account for the intrinsic variability of volcanic eruptions, and implicitly assumes that inter-class size variability (i.e. size difference between different eruptive size classes) dominates over intra-class size variability (i.e. size difference within an eruptive size class), the latter of which is treated as negligible. So far, no quantitative study has been undertaken to verify such an assumption. Here, we adopt a novel Probabilistic Volcanic Hazard Analysis (PVHA) strategy, which accounts for intrinsic eruptive variabilities, to quantify the tephra fallout hazard in the Campania area. We compare the results of the new probabilistic approach with the classical scenario approach. The results allow for determining whether a simplified scenario approach can be considered valid, and for quantifying the bias which arises when full variability is not accounted for. This work was partially supported by the MED-SUV Project funded by the European Union (FP7 Grant Agreement n.308665), the “Futuro in Ricerca 2008 FIRB” ByMuR Project [RBFR0880SR] funded by MIUR (the Italian Ministry of Education, University and Research), and the DPC-INGV V1 (2013–2015) Project, funded by DPC (the Italian Department of Civil Protection) within the agreement between DPC and INGV. Background roadmap, terrain or satellite images in all the pictures were downloaded from Google Maps. The FALL3D simulations have been run at BSC using the MareNostrum supercomputer. We are very grateful to Mary Anne Thompson for providing us with helpful comments. We wish to thank the anonymous reviewers for their suggestions that significantly improved the quality of the manuscript.

We propose a procedure for uncertainty quantification in Probabilistic Tsunami Hazard Analysis (PTHA), with a special emphasis on the uncertainty related to statistical modelling of the earthquake source in Seismic PTHA (SPTHA), and on the separate treatment of subduction and crustal earthquakes (treated as background seismicity). An event tree approach and ensemble modelling are used in spite of more classical approaches, such as the hazard integral and the logic tree. This procedure consists of four steps: (1) exploration of aleatory uncertainty through an event tree, with alternative implementations for exploring epistemic uncertainty; (2) numerical computation of tsunami generation and propagation up to a given offshore isobath; (3) (optional) site-specific quantification of inundation; (4) simultaneous quantification of aleatory and epistemic uncertainty through ensemble modelling. The proposed procedure is general and independent of the kind of tsunami source considered; however, we implement step 1, the event tree, specifically for SPTHA, focusing on seismic source uncertainty. To exemplify the procedure, we develop a case study considering seismic sources in the Ionian Sea (central-eastern Mediterranean Sea), using the coasts of Southern Italy as a target zone. The results show that an efficient and complete quantification of all the uncertainties is feasible even when treating a large number of potential sources and a large set of alternative model formulations. We also find that (i) treating separately subduction and background (crustal) earthquakes allows for optimal use of available information and for avoiding significant biases; (ii) both subduction interface and crustal faults contribute to the SPTHA, with different proportions that depend on source-target position and tsunami intensity; (iii) the proposed framework allows sensitivity and deaggregation analyses, demonstrating the applicability of the method for operational assessments.

Eruption forecasting is a major goal in volcanology. Logically, but unfortunately, forecasting hazards related to non-magmatic unrest is too often overshadowed by eruption forecasting, although many volcanoes often pass through states of non-eruptive and non-magmatic unrest for various and prolonged periods of time. Volcanic hazards related to non-magmatic unrest can be highly violent and/or destructive (e.g., phreatic eruptions, secondary lahars), can lead into magmatic and eventually eruptive unrest, and can be more difficult to forecast than magmatic unrest, for various reasons. The duration of a state of non-magmatic unrest and the cause, type and locus of hazardous events can be highly variable. Moreover, non-magmatic hazards can be related to factors external to the volcano (e.g., climate, earthquake). So far, monitoring networks are often limited to the usual seismic-ground deformation-gas network, whereas recognizing indicators for non-magmatic unrest requires additional approaches. In this study we summarize non-magmatic unrest processes and potential indicators for related hazards. We propose an event-tree to classify non-magmatic unrest, which aims to cover all major hazardous outcomes. This structure could become useful for future probabilistic non-magmatic hazard assessments, and might reveal clues for future monitoring strategies. © 2014 Rouwet et al.

Tsunami warning centres face the challenging task of rapidly forecasting tsunami threat immediately after an earthquake, when there is high uncertainty due to data deficiency. Here we introduce Probabilistic Tsunami Forecasting (PTF) for tsunami early warning. PTF explicitly treats data- and forecast-uncertainties, enabling alert level definitions according to any predefined level of conservatism, which is connected to the average balance of missed-vs-false-alarms. Impact forecasts and resulting recommendations become progressively less uncertain as new data become available. Here we report an implementation for near-source early warning and test it systematically by hindcasting the great 2010 M8.8 Maule (Chile) and the well-studied 2003 M6.8 Zemmouri-Boumerdes (Algeria) tsunamis, as well as all the Mediterranean earthquakes that triggered alert messages at the Italian Tsunami Warning Centre since its inception in 2015, demonstrating forecasting accuracy over a wide range of magnitudes and earthquake types., Probabilistic tsunami forecasting (PTF) defines an approach to tsunami early warning based on uncertainty quantification, enhancing forecast accuracy and enabling rational decision making. PTF is here developed for near-source tsunami warning, and tested in hindcasting mode over a wide range of past earthquakes.