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

A methodology for a comprehensive probabilistic tsunami hazard analysis is presented for the major sources of tsunamis (seismic events, landslides, and volcanic activity) and preliminarily applied in the Gulf of Naples (Italy). The methodology uses both a modular procedure to evaluate the tsunami hazard and a Bayesian analysis to include the historical information of the past tsunami events. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0001 the submarine earthquakes and the submarine mass failures are initially identified in a gridded domain and defined by a set of parameters, producing the sea floor deformations and the corresponding initial tsunami waves. Differently volcanic tsunamis generate sea surface waves caused by pyroclastic density currents from Somma‐Vesuvius. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0002 the tsunami waves are simulated and propagated in the deep sea by a numerical model that solves the shallow water equations. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0003 the tsunami wave heights are estimated at the coast using the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0004's amplification law. The selected tsunami intensity is the wave height. In the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0005 the probabilistic tsunami analysis computes the long‐term comprehensive Bayesian probabilistic tsunami hazard analysis. In the prior analysis the probabilities from the scenarios in which the tsunami parameter overcomes the selected threshold levels are combined with the spatial, temporal, and frequency‐size probabilities of occurrence of the tsunamigenic sources. The urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0006 probability density functions are integrated with the urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0007 derived from the historical information based on past tsunami data. The urn:x-wiley:jgrc:media:jgrc23818:jgrc23818-math-0008 probability density functions are evaluated to produce the hazard curves in selected sites of the Gulf of Naples. Plain Language Summary Probabilistic analyses are essential to estimate the natural hazards caused by infrequent and devastating events and to elaborate risk assessments aiming to mitigate and reduce the impact of the natural disasters on society. Probabilistic tsunami hazard analyses use procedures that trace and weight the different tsunami sources (submarine earthquakes, aerial/submarine slides, volcanic activity, meteorological events, and asteroid impacts) with varying probability of occurrence. The scope of the present methodology is the reduction of possible biases and underestimations that arise by focusing on a single tunamigenic source. The multisource probabilistic tsunami hazard analysis is applied to a real case study, the Gulf of Naples (Italy), where relevant threats due to natural events exist in a high densely populated district. The probabilistic hazard procedure takes into account multiple tsunamigenic sources in the region and provides a first‐order prioritization of the different sources in a long‐term comprehensive analysis. The methodology is based on a Bayesian approach that merges computational hazard quantification (based on source‐tsunami simulations) and past data, appropriately including in the quantification the epistemic uncertainty. For the first time a probabilistic analysis of the tsunami hazard in the region is presented taking into consideration multiple tsunamigenic sources.

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.

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.

We present a first detailed tsunami risk assessment for the city of Messina where one of the most destructive tsunami inundations of the last centuries occurred in 1908. In the tsunami hazard evaluation, probabilities are calculated through a new general modular Bayesian tool for Probability Tsunami Hazard Assessment. The estimation of losses of persons and buildings takes into account data collected directly or supplied by: (i) the Italian National Institute of Statistics that provides information on the population, on buildings and on many relevant social aspects; (ii) the Italian National Territory Agency that provides updated economic values of the buildings on the basis of their typology (residential, commercial, industrial) and location (streets); and (iii) the Train and Port Authorities. For human beings, a factor of time exposition is introduced and calculated in terms of hours per day in different places (private and public) and in terms of seasons, considering that some factors like the number of tourists can vary by one order of magnitude from January to August. Since the tsunami risk is a function of the run-up levels along the coast, a variable tsunami risk zone is defined as the area along the Messina coast where tsunami inundations may occur. © 2012 Author(s). CC Attribution 3.0 License.

A recent paper in Reviews of Geophysics describes a probabilistic method for evaluating tsunami location, size, and risk to human populations.

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.

In this paper, a Bayesian procedure is implemented for the Probability Tsunami Hazard Assessment (PTHA). The approach is general and modular incorporating all significant information relevant for the hazard assessment, such as theoretical and empirical background, analytical or numerical models, instrumental and historical data. The procedure provides the posterior probability distribution that integrates the prior probability distribution based on the physical knowledge of the process and the likelihood based on the historical data. Also, the method deals with aleatory and epistemic uncertainties incorporating in a formal way all sources of relevant uncertainty, from the tsunami generation process to the wave propagation and impact on the coasts. The modular structure of the procedure is flexible and easy to modify and/or update as long as new models and/or information are available. Finally, the procedure is applied to an hypothetical region, Neverland, to clarify the PTHA evaluation in a realistic case. © Springer Science+Business Media B.V. 2009.

This technical report describes the climate scenario simulations provided by the INGV-CMCC to the Intergovernmental Panel on Climate Change (IPCC). The model used to run these simulations is SINTEX-G (INGV-SXG), a fully coupled Atmosphere Ocean General Circulation Model (AOGCM) able to run experiments driven by radiative forcing (Ozone, Sulfate Aerosols, Greenhouse Gases) as specified in the protocol for the IPCC standard experiments.Six experiments have been performed: preindustrial simulation, XX century simulaton, XXI century simulatons under different IPCC radiative forcing conditions (IPCC scenarios A1B and A2), CO2 increase simulations to doubling and quadrupling.Scenario Simulations settings and model data availability are fully explained in the text.

This technical report summarizes the SINTEX-G (INGV-SXG) model technical structure. INGV-SXG is an Atmosphere Ocean sea-ice General Circulation Model (AOGCM) developed at INGV with the aim of investigating the features and the mechanisms of theclimate variability and change.This model was used to produce the INGV-CMCC Intergovernmental Panel on Climate Change (IPCC) scenario simulations. INGV-SXG is an evolution of SINTEX and SINTEX-F and this report indicates the improvement with respect to these previous INGV AOCM model: the new model includes a thermodynamic-dynamic sea-ice model and the capabilities to use external radiative forcings (Ozone, Sulfate Aerosols, Greenhouse Gases) as specified in the protocol for the IPCC standard experiments.The description of model components, coupling methods, compiling and running environments is a guideline for model users.

Multivariate vertical Empirical Orthogonal Functions (EOFs) have been calculated in the Adriatic Sea in order to implement a System for the Ocean Forecasting and Analysis (SOFA) which will release weekly forecasts of currents. The EOFs define spatially coherent, multivariate modes of variability which are physically meaningful and enable truncation of the problem of modelling error correlations to some order leading to the best convergence. The model vertical EOFs indicate the regions of greater spatial and seasonal variability of temperature and salinity. Also the analysis shows that a higher number of modes is necessary in the Northern Adriatic.