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Tsunami urgent computing procedures quantify the potential hazard due to an earthquake right after its occurrence, that is within a few hours. The hazard is quantified by simulating the propagation of the tsunami waves in the sea, accounting for the uncertainty due to the scarce knowledge of the source parameters and wave modelling uncertainty.In the context of the European project eflows4HPC, a workflow is currently in development for tsunamis hazard urgent computing, which consists of the following steps: 1) Retrieval of information about the tsunamigenic seismic event (magnitude, hypocentre and their uncertainties); 2) Definition of an ensemble of seismic sources; 3) Simulation of seismic/tsunamigenic waves propagation for each scenario in the ensemble; 4) Results aggregation to produce an estimate of seismic and tsunami hazard, which also incorporates a basic treatment of modelling uncertainty. The ensembles cover the uncertainty on source characteristics and may consequently be very large (generally 10,000 to 100,000 of scenarios; Selva et al., Nat. Comm.), requiring very high computational resources for the urgent computing context. It is thus necessary to reduce the size of these ensembles to limit the number of simulations and to converge faster towards stable results of hazard calculation.We developed and tested several sampling procedures aiming to reduce the number of scenarios in the ensemble and, at the same time, to integrate the new incoming information as they become available (e.g. solutions for focal mechanisms, seismic or tsunami records). When applied to several past earthquakes and tsunamis (e.g., the 2003 Boumerdes and the 2017 Kos-Bodrum earthquakes), our novel sampling strategies yielded a reduction of 1 or 2 order of magnitudes of the ensemble size, allowing a drastic reduction of the computational effort. Also, the update of the ensemble based on the incoming of new data, which strongly reduce the uncertainty, yields to an update of the probabilistic forecasts without compromising its accuracy. This may result very important for mitigating the risk far from the seismic source, as well as improving the risk management by better informing decision making in a frame of urgency.

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

The destructive tsunami on 22 December 2018 due to the flank collapse of the Anak Krakatau volcano was a bitter reminder of large tsunami risks and of the shortcomings of the existing tsunami warning systems for atypical (non-seismic) sources. In the Mediterranean, several tsunamis were generated by landslides associated with volcanic systems in the past.The volcanic unrest experienced in 2011-2012 on the Santorini volcanic island in the Southern Aegean Sea pointed out the need to identify and quantify tsunami hazard and risk due to possible flank instability which may be triggered as a result of volcanic unrest or nearby seismotectonic activities. Inspired from this need, in this study we examined three possible landslide scenarios in Santorini Island with tsunamigenic potential. The results show that the scenarios considered in our study are able to generate significant local tsunamis impacting Santorini and the nearby islands, as well as producing significant impact along the coasts of the Southern Aegean Sea. While maximum tsunami amplitudes/arrival time ranges are 1.2m/30-90min for locations in the Greek-Turkish coasts in the far field, they are in the order of ≈60m/1-2min for some locations at the Santorini Island. The extreme tsunami amplitudes and short arrival times for locations inside the Santorini Island is a major challenge in terms of tsunami hazard warning and mitigation. As an effort to address this challenge, a discussion on the requirements for local tsunami warning system addressing atypical sources in the context of multi-hazard disaster risk reduction is also provided., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

The creation of hazard maps relative to volcanic phenomena requires taking into account the intrinsic complexity and variability of eruptions. Here we present an example of how HPC can allow producing a high resolution multi-source probabilistic hazard assessment due to tephra fallout over a domain covering Southern Italy. The three active volcanoes in the Neapolitan area, Somma-Vesuvius, Campi Flegrei and Ischia, were considered as volcanic sources. For each one, we explored three explosive size classes (Small, Medium and Large) for Somma Vesuvius and Campi Flegrei, and one explosive size class (Large) for Ischia. For each size class, we performed 1500 numerical simulations of ash dispersion (total of 10500) using the Fall3D (V8.0) model over a computational domain covering Southern Italy with a 0.03° ⨉ 0.03° (~3 km ⨉ 3 km) resolution. Within each size class, the eruptive parameters have been randomly sampled from well-suited probability distributions and with different meteorological conditions, obtained by randomly sampling a day between 1990 and 2020 and retrieving the relative data from the ECMWF ERA5 database. This allowed exploring the intra-class variability and to quantify aleatoric uncertainty. The results of these simulations have been post-processed with a statistical approach by assigning a weight to each eruption (based on its eruption magnitude) and the annual eruption rate of each size class. For the case of Campi Flegrei, the variability in the eruptive vent position has also been explored by constructing a grid of possible vent locations with different spatial probability. By merging the results obtained for each source and size class we produced a portfolio of hazard maps showing the expected mean annual frequency of overcoming selected thresholds in ground tephra load. A disaggregation analysis has also been performed in order to understand which particular source and/or size class had the greater impact on a particular area. The completion of this work, considering both numerical simulations and the statistical elaboration of the results has required a total of more than 5000 core hours and the processing of more than 2TB of data, an effort that wouldn’t have been possible without the access to high level HPC resources.

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.

The main purpose of this article is to emphasize the importance of clarifying the probabilistic framework adopted for volcanic hazard and eruption forecasting. Eruption forecasting and volcanic hazard analysis seeks to quantify the deep uncertainties that pervade the modeling of pre-, sin- and post-eruptive processes. These uncertainties can be differentiated into three fundamental types: (1) the natural variability of volcanic systems, usually represented as stochastic processes with parameterized distributions (aleatory variability); (2) the uncertainty in our knowledge of how volcanic systems operate and evolve, often represented as subjective probabilities based on expert opinion (epistemic uncertainty); and (3) the possibility that our forecasts are wrong owing to behaviors of volcanic processes about which we are completely ignorant and, hence, cannot quantify in terms of probabilities (ontological error). Here we put forward a probabilistic framework for hazard analysis recently proposed by Marzocchi & Jordan (2014), which unifies the treatment of all three types of uncertainty. Within this framework, an eruption forecasting or a volcanic hazard model is said to be complete only if it (a) fully characterizes the epistemic uncertainties in the model's representation of aleatory variability and (b) can be unconditionally tested (in principle) against observations to identify ontological errors. Unconditional testability, which is the key to model validation, hinges on an experimental concept that characterizes hazard events in terms of exchangeable data sequences with well-defined frequencies. We illustrate the application of this unified probabilistic framework by describing experimental concepts for the forecasting of tephra fall from Campi Flegrei. Eventually, this example may serve as a guide for the application of the same probabilistic framework to other natural hazards.

Volcanic activity typically switches between high-activity states with many eruptions and low-activity states with few or no eruptions. We present a simple two-regime physics-informed statistical model that allows interpreting temporal modulations in eruptive activity. The model enhances comprehension and comparison of different volcanic systems and enables homogeneous integration into multivolcano hazard assessments that account for potential changes in volcanic regimes. The model satisfactorily fits the eruptive history of the three active volcanoes in the Neapolitan area, Italy (Mt. Vesuvius, Campi Flegrei, and Ischia) which encompass a wide range of volcanic behaviors. We find that these volcanoes have appreciably different processes for triggering and ending high-activity periods connected to different dominant volcanic processes controlling their eruptive activity, with different characteristic times and activity rates (expressed as number of eruptions per time interval). Presently, all three volcanoes are judged to be in a low-activity state, with decreasing probability of eruptions for Mt. Vesuvius, Ischia, and Campi Flegrei, respectively.

The b-value of the Gutenberg-Richter law is one of the most widely studied parameters regarding the distribution of earthquakes’ magnitude. The estimation of such a parameter and its uncertainty is critical, and it may become complex in the case of seismic catalogs with a non-uniform magnitude of completeness, or when different shapes of the Gutenberg-Richter relation (e.g. the tapered one) are adopted. Here we review recent results on the b-value estimation, also in the case of catalogs with multiple completeness levels, including the application of the weighted likelihood methodology, a method particularly suitable for spatial b-value mapping and for studying its temporal variations. These new techniques are applied to both global and regional seismic catalogs, in order to unveil the peculiarities of the b-value.

The effects of volcanic hazards can be quantified by applying new methods to provide support for rational decision-making. Mt Etna is one the most active volcanoes in the world, producing both effusive and explosive eruptions together with a very intense seismic activity, which significantly affect the territory and human society. We present the preliminary results obtained in the framework of the PANACEA project (INGV’s project “Pianeta Dinamico”, funded by the Italian Ministero dell’Università e la Ricerca, MUR) regarding the multi-hazard assessment around Mt Etna. These include: (i) the production of an updated spatio-temporal probability map of vent opening at Etna, using a procedure exploiting different Kernel functions (e.g. the exponential, Cauchy, and Gaussian functions), and testing volcanic deformation patterns to explore possible dynamic, structural conditioning on the vent opening process; (ii) the identification of a set of cascading effects scenarios that account for volcanic phenomena (i.e., volcanic unrest, seismicity, volcanic explosions, volcanic effusive events, lava flows, tephra/ballistic fall, and PDC), as well as other external hazards potentially linked in such chains (e.g., flooding, forest fires, etc.); and (iii) the identification of scenarios involving systemic impacts (e.g., impacts on the functionality or connectivity of networks).

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.

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

For active volcanoes, knowledge about probabilities of eruption and impacted areas becomes valuable information for decision-makers to develop short- and long-term emergency plans, for which probabilistic volcanic hazard assessment (PVHA) is needed. High-resolution or spatially extended PVHA requires extreme-scale high-performance computing systems. Within the framework of ChEESE (Center of Excellence for Exascale in Solid Earth; www.cheese-coe.eu), an effort was made to generate exascale-suitable codes and workflows to collect and process in some hours the large amount of data that a quality PVHA requires. To this end, we created an optimized HPC-based workflow coined PVHA_HPC-WF to develop PVHA for a volcano. This tool uses the Bayesian event tree methodology to calculate eruption probabilities, vent-opening location(s), and eruptive source parameters (ESPs) based on volcano history, monitoring system data, and meteorological conditions. Then, the tool interacts with the chosen hazard model, performing a simulation for each ESP set or volcanic scenario (VS). Finally, the resulting information is processed by proof-of-concept-subjected high-performance data analytics (HPDA) scripts, producing the hazard maps which describe the probability over time of exceeding critical thresholds at each location in the investigated geographical domain. Although PVHA_HPC-WF can be adapted to other hazards, we focus here on tephra (i.e., lapilli and ash) transport and deposition. As an application, we performed PVHA for Campi Flegrei (CF), Italy, an active volcano located in one of the most densely inhabited areas in Europe and under busy air traffic routes. CF is currently in unrest, classified as being in an attention level by the Italian Civil Protection. We consider an approximate 2,000 × 2,000 × 40 km computational domain with 2 km grid resolution in the horizontal and 40 vertical levels, centered in CF. To explore the natural variability and uncertainty of the eruptive conditions, we consider a large number of VSs allowing us to include those of low probability but high impact, and simulations of tephra dispersal are performed for each of them using the FALL3D model. Results show the potential of HPC to timely execute a vast range of simulations of complex numerical models in large high-resolution computational domains and analyze great volumes of data to obtain quality hazard maps., The research leading to these results has been supported by the European Commission’s CORDIS [ChEESE (grant no. 823844)]. Part of this work was supported by European Community’s Horizon 2020 POP project under grant agreements (no. 824080). Multi-year PRACE Project Access “Volcanic ash hazard and forecast” (ID 2019215114). This work was granted access to the HPC/AI resources of TGCC under the allocation 2020-RA2020235114 made by GENCI.

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.

Recent natural disasters that seriously affected critical infrastructure (CI) with significant socio-economic losses and impact revealed the need for the development of reliable methodologies for vulnerability and risk assessment. In this paper, a risk-based multi-level stress test method that has been recently proposed, aimed at enhancing procedures for evaluation of the risk of critical non-nuclear infrastructure systems against natural hazards, is specified and applied to six key representative CIs in Europe, exposed to variant hazards. The following CIs are considered: an oil refinery and petrochemical plant in Milazzo, Italy, a conceptual alpine earth-fill dam in Switzerland, the Baku–Tbilisi–Ceyhan pipeline in Turkey, part of the Gasunie national gas storage and distribution network in the Netherlands, the port infrastructure of Thessaloniki, Greece, and an industrial district in the region of Tuscany, Italy. The six case studies are presented following the workflow of the stress test framework comprised of four phases: pre-assessment phase, assessment phase, decision phase and report phase. First, the goals, the method, the time frame and the appropriate stress test level to apply are defined. Then, the stress test is performed at component and system levels and the outcomes are checked and compared to risk acceptance criteria. A stress test grade is assigned, and the global outcome is determined by employing a grading system. Finally, critical components and events and risk mitigation strategies are formulated and reported to stakeholders and authorities. © 2019, Springer Nature B.V.

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).

The emission of volcanic gases can occur both during volcanic eruptions and in quiescent stages of the volcanic activity. This process can affect the air quality in the areas downwind; in fact, many gas species can be a threat to human health and even life at concentrations and doses above species-specific thresholds. Gas emissions can be of different types, the two main categories being dilute passive degassing and heavy gas flow. The former occurs when the gas concentration is low and/or temperature is high, hence its density is lower than the atmospheric density; the latter takes place when the gas density is higher than the atmosphere and the gas accumulates onto the ground and may flow as a gravity current more or less affected by the wind. Examples of the first and second types of emissions are fumaroles and limnic explosions, respectively.Numerical modelling is one of the approaches used to quantify the hazard related to these processes. Ideally, for hazard quantification purposes numerous simulations originating from varying the most important input parameters (e.g. wind field, emission rates, etc.) in their range of uncertainty should be carried out. The whole process of gas dispersion modelling is time consuming, since it starts with the assessment of the wind field with an ad-hoc meteorological model, proceeds with the actual gas dispersion simulation and concludes with the post-processing stage. In order to simplify the whole workflow with the final aim to manage numerous simultaneous simulations for hazard assessment applications, we created APVGDM (Automatic Probabilistic Volcanic Gas Dispersion Modelling), a simulation tool made of a collection of Python scripts. APVGDM is interfaced with two dispersion models that can be selected by the user depending on the application of interest: a dilute (DISGAS) and a dense gas (TWODEE) dispersion model. The post-processing script is capable of building Empirical Cumulative Distribution Functions (ECDF) of the gas concentrations combining the outputs of multiple simulations; the ECDF can be interrogated by the user to produce outputs at the desired exceedance probability. Here we present APVGDM and some application examples showing the wide range of options that the tool offers.

In recent decades, reliable computational models have significantly advanced, and now represent a valuable tool to make quantitative and testable predictions, supporting gas dispersal forecasting and hazard assessments for public safety. In this study, we carried out a number of tests aimed to validate the modelling of gas dispersal at La Soufrière de Guadeloupe volcano (Lesser Antilles), which has shown quasi-permanent degassing of low-temperature hydrothermal nature since its last magmatic eruption in 1530 AD. In particular, we focused on the distribution of CO2 and H2S discharged from the three main present-day fumarolic sources at the summit, using the MultiGAS measurements of continuous gas concentrations collected during March-April 2017. We implemented the open-source Eulerian code DISGAS-2.0 for passive gas dispersion coupled with the mass consistent Diagnostic Wind Model (DWM), using wind measurements and atmospheric stability information from a local meteorological station and the ECMWF-ERA5 reanalysis data. We found that model outputs are highly dependent on the resolution of the topographic data, which affect mainly the reliability of DWM meteorological fields, especially on and around the steep dome. Our results satisfactory reproduce the observed data, indicating the potential usefulness of DISGAS-2.0 as a tool for quantifying gas hazard and reproducing the fumarolic degassing and at La Soufrière de Guadeloupe.

Reducing the impact of volcanic eruptions on society is a major challenge of volcanology. Although science is one basic component of risk reduction, the achievement of this goal requires competencies that go beyond natural sciences. Nowadays, the importance of non-scientific factors in reducing volcanic risks is often and dangerously overlooked, possibly leading to decision making that cannot be rationally justified. In this paper we explore the basic components of an ideal decision-making process, identifying the roles and responsibilities of the different partners/tasks that are involved. In particular, we advocate the use of the hazard/risk separation principle, which can help discern unambiguously the role of volcanology (and more in general of science) in the whole risk-reduction process. Although this distinction may be of low relevance in some real cases—for example, when the costs of mitigation actions are low and the likelihood of eruption is high—it becomes of paramount importance when dealing with high uncertainty on the eruption onset or size and expensive mitigation actions, such as the evacuation of a large city. Volcanologists can play different roles in the decision-making process, but they have to be aware that this demands competencies that go beyond being a good volcano scientist. The final intent of this paper is to encourage constructive cooperation between volcanologists and public-policy makers keeping separated their own tasks as defined by their roles and their competences, with the intent of establishing fully transparent decision-making protocols well before volcanic crises. These protocols can be very helpful to audit the decision-making process at any time, and they may be an excellent communication tool for the interested stakeholders, including society.

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.

In this paper, we present a simple model to forecast global focal mechanisms. This model is based on a simple discrete counting distribution of the global centroid moment tensor catalog and it also...

Estimating the quantity of CO2 diffusively emitted from the Earth’s surface has important implications for volcanic surveillance and global atmospheric CO2 budgets. However, the identification and quantification of non-hydrothermal contributions to CO2 release can be ambiguous. Here, we describe a multi-parametric approach employed at the Nisyros caldera, Aegean Arc, Greece, to assess the relative influence of deep and shallow gases released from the soil. In April 2019, we measured diffuse soil surface CO2 fluxes, together with their carbon isotope compositions, and at a depth of 80 cm, the CO2 concentration, soil temperature, and the activities of radon and thoron. The contributions of deep CO2 and biogenic CO2 fluxes were distinguished on the basis of their carbon isotope compositions. A Principal Component Analysis (PCA), performed on the measured parameters, effectively discriminates between a deep- and a shallow degassing component. The total CO2 output estimated from a relatively small testing area was two times higher with respect to that observed in a previous survey (October 2018). The difference is ascribed to variation in the soil biogenic CO2 production, that was high in April 2019 (a wet period) and low or absent in October 2018 (a dry period). Accounting for seasonal biogenic activity is therefore critical in monitoring and quantifying CO2 emissions in volcanic areas, because they can partially- or completely overwhelm the volcanic-hydrothermal signal.

One of the aims of EUROVOLC is to raise awareness and share data by exploiting existing tools for hazard and risk. Here we present the ongoing effort within EUROVOLC WP12 to create an online tool to collect information from people witnessing volcanic events at European or other volcanoes.In the recent past, building on the experience from earthquakes, and from the trans-national effects of Eyjafallajökull eruption, European research groups have built tools (e.g. questionnaires or apps) for facilitating the collection of data by citizens. These efforts are presently fragmented and sparse across Europe (and across the world).As the first step we have conducted a reconnaissance survey of existing citizen science tools in volcanology (from operational and research projects), available for download through EUROVOLC websiteOne of the aims of EUROVOLC is to raise awareness and share data by exploiting existing tools for hazard and risk. Here we present the ongoing effort within EUROVOLC WP12 to create an online tool to collect information from people witnessing volcanic events at European or other volcanoes.In the recent past, building on the experience from earthquakes, and from the trans-national effects of Eyjafallajökull eruption, European research groups have built tools (e.g. questionnaires or apps) for facilitating the collection of data by citizens. These efforts are presently fragmented and sparse across Europe (and across the world).As the first step we have conducted a reconnaissance survey of existing citizen science tools in volcanology (from operational and research projects), available for download through EUROVOLC website.The new EUROVOLC tool will:- access and collate data collected by several pre-existing tools. These tools currently include ‘myVolcano’ by British Geological Survey; sulphur dioxide and ash recording tools by Iceland Met Office; Osservatorio Vesuviano web questionnaire & Tefranet by INGV-Catania. These tools were selected based on whether their data can be ‘pulled’ in real-time;- allow additional tools to be incorporated as they become available;- allow recording of new data by the users;- allow visualizing on a map the data in which the users are interested in, that can be selected by region/country, by recording time, or by observed phenomenon;- allow downloading the data in which the users are interested inIn this way, the users of EUROVOLC tool will have access to observations collected by the multiple tools available across EUROPE through a single access point.The EUROVOLC tool will become available in July 2020.

In the last decade, probabilistic volcanic hazard assessment (PVHA) has become one of the most rapidly developing topics in volcanology. PVHA relies on a number of simulation tools, which have been catalogued within H2020 EUROVOLC project.Here we apply two of these tools that will concur to a probabilistic multi-hazard assessment for volcanic phenomena at La Soufrière de Guadeloupe, as reported in reference scenarios elaborated by OVSG-IPGP and communicated to the authorities. In the last 9 kyr the activity at La Soufrière is characterized by recurrent effusive to explosive activity, sector collapses and intense fumarolic emissions. Based on literature data, we focus particularly on the most likely explosive (phreatic or hydrothermal) scenario and explore the hazard posed by gas dispersal and ballistics impact, which have never been the focus of PVHA. We also set up a preliminary spatial map for phreatic vent opening, a baseline for the PVHA here presented.

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.

Making communities safer requires better tools to identify, quantify, and manage risks. Among the most important tools are stress tests, originally designed to test the risk posed by nuclea...

Massive emissions of deep CO2 coupled to the 2009–2018 Central Italy earthquakes., Deep CO2 emissions characterize many nonvolcanic, seismically active regions worldwide, and the involvement of deep CO2 in the earthquake cycle is now generally recognized. However, no long-time records of such emissions have been published, and the temporal relations between earthquake occurrence and tectonic CO2 release remain enigmatic. Here, we report a 10-year record (2009–2018) of tectonic CO2 flux in the Apennines (Italy) during intense seismicity. The gas emission correlates with the evolution of the seismic sequences: Peaks in the deep CO2 flux are observed in periods of high seismicity and decays as the energy and number of earthquakes decrease. We propose that the evolution of seismicity is modulated by the ascent of CO2 accumulated in crustal reservoirs and originating from the melting of subducted carbonates. This large-scale, continuous process of CO2 production favors the formation of overpressurized CO2-rich reservoirs potentially able to trigger earthquakes at crustal depth.

We present the results of a statistical study on tephra dispersal in the case of a reactivation of the Campi Flegrei volcano. To represent the spectrum of possible eruptive sizes, four classes of eruptions were considered. Excluding the lava emission, three classes are explosive (Small, Medium, and Large) and can produce a significant quantity of volcanic ash. Hazard assessments were made through simulations of atmospheric dispersion of ash and lapilli, considering the full variability of winds and eruptive vents. The results are presented in form of conditional hazard curves given the occurrence of specific eruptive sizes, representative members of each size class, and then combined to quantify the conditional hazard given an eruption of any size. The main focus of this analysis was to constrain the epistemic uncertainty (i.e. associated with the level of scientific knowledge of phenomena), in order to provide unbiased hazard estimations. The epistemic uncertainty on the estimation of hazard curves was quantified, making use of scientifically acceptable alternatives to be aggregated in the final results. The choice of such alternative models was made after a comprehensive sensitivity analysis which considered different weather databases, alternative modelling of submarine eruptive vents and tephra total grain-size distributions (TGSD) with a different relative mass fraction of fine ash, and the effect of ash aggregation. The results showed that the dominant uncertainty is related to the combined effect of the uncertainty with regard to the fraction of fine particles with respect to the total mass and on how ash aggregation is modelled. The latter is particularly relevant in the case of magma-water interactions during explosive eruptive phases, when a large fraction of fine ash can form accretionary lapilli that might contribute significantly in increasing the tephra load in the proximal areas. The variability induced by the use of different meteorological databases and the selected approach to modelling offshore eruptions were relatively insignificant. The uncertainty arising from the alternative implementations, which would have been neglected in standard (Bayesian) quantifications, were finally quantified by ensemble modelling, and represented by hazard and probability maps produced at different confidence levels.

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.

Volcanic islands pose several major types of natural hazards, often interconnected and concentrated in relatively small areas. The quantification of these hazards must be framed from a multi-hazard perspective whilst building on existing single-hazard analyses. Ischia is a densely inhabited volcanic island with a long eruptive history lasting more than 150 ka (last in 1302 AD) characterized by the significant asymmetric resurgence of a caldera block. Here, we review the state-of-art of the natural hazards of Ischia, aiming at building a solid base for future holistic multi-hazard quantifications. We frame our analysis in three steps: i) review of geological, historical and current activity; ii) review of available hazard models and analyses; iii) development of an interpretative framework for the interdependent hazards. The results highlight that volcanic activity has been quite intense and many volcano-related hazardous phenomena have affected the island including in very recent times, both for eruptive (phreatic or magmatic eruptions) and non-eruptive (earthquakes, landslides, and tsunamis) phenomena. The effects of some of them (e.g. tsunamis, tephra) are also relevant beyond the island territory. Quantitative hazard assessments are almost absent and should be developed in the future considering the evident interconnections between hazards. To this end, we propose a conceptual interpretative multi-hazard framework that highlights the fundamental role played by the resurgent block in controlling and connecting the different hazards, in terms of both spatial distribution of the sources and temporal clustering.

Transportation infrastructures are complex systems of various connected components like bridges, roads, tunnels, embankments, retaining walls in case of a highway system or wharfs, cranes, buildings, utility systems in case of port facilities. Due to their spatial extent, they are exposed to variable natural hazards such as earthquakes, tsunami or landslides. Experience from past disastrous events shows that transportation infrastructures are quite vulnerable due to the lack of redundancy, the lengthy repair time, the rerouting difficulties or the cascading failures and interdependencies. Their damage could be greatly disruptive in terms of safety of life, business disruption, access to emergency services and key lifelines utilities, rescue operations and socio-economic impacts. Therefore, in terms of resilience it is important to recognize and quantify the risks and global losses associated to damages of transportation systems and to establish efficient risk mitigation strategies. These include, among others, enhancement of emergency preparedness, strengthening of existing structures and improvement of the recovery planning. Herein an integrated framework for the probabilistic systemic vulnerability and risk assessment of transportation and utility networks is presented, based on the achievements of the recently completed EC project SYNER-G (www.syner-g.eu) and the ongoing EC project STREST (www.strest-eu.org). The infrastructure is modeled according to a detailed taxonomy. The framework encompasses in an integrated fashion all aspects in the chain, from regional hazard to fragility assessment of components to the socio-economic impacts of a natural disaster, accounting for relevant uncertainties within an efficient quantitative simulation scheme, and modeling interactions between multiple component systems in the taxonomy. Selected Performance Indicators (PIs) are calculated for each network based on the estimated damages and functionality losses of the different components under the given hazards. The methodology and tools are demonstrated through case studies in the road network and the harbor of Thessaloniki city, Greece, under seismic hazard and associated geotechnical hazards (i.e. soil liquefaction). The applications include assessments of systems' performance considering the spatial seismic hazard with correlation of ground motion intensities, the vulnerability of the network components, and the effect of interactions within the system, as well as, between components of different systems. In particular, road disruptions can be caused due to direct damage of road segments and bridges, as well as building and overpass collapses. Harbor operations can be disturbed due to failures of waterfront structures and cargo handling equipment, as well as disruptions to the electric power supply and building collapses. The systemic risk for the road network and harbor is calculated, specifically focusing on the short-term impact of seismic events (just after the earthquake) and the risk curves (i.e. mean annual rates of exceedance for loss in performance of the infrastructures) are provided. The significant elements for the functionality of each system are defined through correlation factors to the system PIs. Such results can contribute to the decision-making regarding the enhancement of existing and the robust development of new infrastructures in the frame of safety and resiliency. Language: en

Site-specific Seismic Probabilistic Tsunami Hazard Analysis (SPTHA) is computationally demanding, 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 that, based on the similarity of offshore tsunamis and hazard curves in front of a target site, uses a filtering procedure to reduce the number of numerical simulations needed, while still allowing full treatment of aleatory and epistemic uncertainty. Moreover, near-field sources are identified, on the basis of the tsunami coseismic initial conditions, and treated separately to avoid biases in the tsunami hazard assessment. In fact, coastal coseismic deformation necessarily affects the tsunami intensity, depending on the scenario size, mechanism, and position. Therefore, we developed two parallel filtering schemes in the far- and the near-field, respectively. For near-field sources, offshore tsunami amplitude can not represent a proxy for the coastal inundation, and filtering is based on coseismic field. By comparison of the results obtained with and without the correction for the near-field sources, for a use-case at the Milazzo oil refinery (Sicily, Italy), we demonstrated that special treatment of local sources plays a fundamental role and is applicable in local scale SPTHA.

Any trustworthy probabilistic seismic-hazard analysis (PSHA) has to account for the intrinsic variability of the system (aleatory variability) and the limited knowledge of the system itself (epistemic uncertainty). The most popular framework for this purpose is the logic tree. Notwithstanding its vast popularity, the logic-tree outcomes are still interpreted in two different and irreconcilable ways. In one case, practitioners claim that the mean hazard of the logic tree is the hazard and the distribution of all outcomes does not have any probabilistic meaning. On the other hand, other practitioners describe the seismic hazard using the distribution of all logic-tree outcomes. In this article, we explore in detail the reasons for this controversy regarding the interpretation of logic tree, showing that the distribution of all outcomes is more appropriate to provide a joined, full description of aleatory variability and epistemic uncertainty. Then, we provide a more general framework, that we call ensemble modeling, in which the logic-tree outcomes can be embedded. In this framework, the logic tree is not a classical probability tree, but it is just a technical tool that samples epistemic uncertainty. Ensemble modeling consists of inferring the parent distribution of the epistemic uncertainty from which this sample is drawn. Ensemble modeling offers some remarkable additional features. First, it allows a rigorous and meaningful validation of any PSHA; this is essential if we want to keep PSHA within the scientific domain. Second, it provides a proper and clear description of the aleatory variability and epistemic uncertainty that can help stakeholders appreciate the whole range of uncertainties in PSHA. Third, it may help to reduce the computational timewhen the logic tree becomes computationally intractable because of too many branches. © 2015, Seismological Society of America. All rights reserved.

This article presents an integrated approach for the probabilistic systemic risk analysis of a road network considering spatial seismic hazard with correlation of ground motion intensities, vulnerability of the network components, and the effect of interactions within the network, as well as, between roadway components and built environment to the network functionality. The system performance is evaluated at the system level through a global connectivity performance indicator, which depends on both physical damages to its components and induced functionality losses due to interactions with other systems. An object-oriented modeling paradigm is used, where the complex problem of several interacting systems is decomposed in a number of interacting objects, accounting for intra- and interdependencies between and within systems. Each system is specified with its components, solving algorithms, performance indicators and interactions with other systems. The proposed approach is implemented for the analysis of the road network in the city of Thessaloniki (Greece) to demonstrate its applicability. In particular, the risk for the road network in the area is calculated, specifically focusing on the short-term impact of seismic events (just after the earthquake). The potential of road blockages due to collapses of adjacent buildings and overpass bridges is analyzed, trying to individuate possible criticalities related to specific components/subsystems. The application can be extended based on the proposed approach, to account for other interactions such as failure of pipelines beneath the road segments, collapse of adjacent electric poles, or malfunction of lighting and signaling systems due to damage in the electric power network.

The inter-arrival times of the post 2000 seismicity at Campi Flegrei caldera are statistically distributed into different populations. The low inter-arrival times population represents swarm events, while the high inter-arrival times population marks background seismicity. Here, we show that the background seismicity is increasing at the same rate of (1) the ground uplift and (2) the concentration of the fumarolic gas specie more sensitive to temperature. The seismic temporal increase is strongly correlated with the results of recent simulations, modelling injection of magmatic fluids in the Campi Flegrei hydrothermal system. These concurrent variations point to a unique process of temperature-pressure increase of the hydrothermal system controlling geophysical and geochemical signals at the caldera. Our results thus show that the occurrence of background seismicity is an excellent parameter to monitor the current unrest of the caldera.

During volcanic crises, volcanologists estimate the impact of possible imminent eruptions usually through deterministic modeling of the effects of one or a few preestablished scenarios. Despite such an approach may bring an important information to the decision makers, the sole use of deterministic scenarios does not allow scientists to properly take into consideration all uncertainties, and it cannot be used to assess quantitatively the risk because the latter unavoidably requires a probabilistic approach. We present a model based on the concept of Bayesian event tree (hereinafter named BET-VH-ST, standing for Bayesian event tree for short-term volcanic hazard), for short-term near-real-time probabilistic volcanic hazard analysis formulated for any potential hazardous phenomenon accompanying an eruption. The specific goal of BET-VH-ST is to produce a quantitative assessment of the probability of exceedance of any potential level of intensity for a given volcanic hazard due to eruptions within restricted time windows (hours to days) in any area surrounding the volcano, accounting for all natural and epistemic uncertainties. BET-VH-ST properly assesses the conditional probability at each level of the event tree accounting for any relevant information derived from the monitoring system, theoretical models, and the past history of the volcano, propagating any relevant epistemic uncertainty underlying these assessments. As an application example of the model, we apply BET-VH-ST to assess short-term volcanic hazard related to tephra loading during Major Emergency Simulation Exercise, a major exercise at Mount Vesuvius that took place from 19 to 23 October 2006, consisting in a blind simulation of Vesuvius reactivation, from the early warning phase up to the final eruption, including the evacuation of a sample of about 2000 people from the area at risk. The results show that BET-VH-ST is able to produce short-term forecasts of the impact of tephra fall during a rapidly evolving crisis, accurately accounting for and propagating all uncertainties and enabling rational decision making under uncertainty. ©2014. American Geophysical Union. All Rights Reserved.

The societal importance and implications of seismic‐hazard assessment forces the scientific community to pay increasing attention to the evaluation of uncertainty in order to provide accurate assessments. Probabilistic seismic hazard assessment (PSHA) formally accounts for the natural variability of the involved phenomena, from seismic sources to wave propagation. Recently, increased attention has been paid to the consequences of alternative modeling procedures on hazard results. This uncertainty, essentially of epistemic nature, has been shown to have major impacts on PSHA results, leading to extensive applications of techniques like the logic tree. Here, we develop a formal Bayesian inference scheme for PSHA that allows us, on the one hand, to explicitly account for all uncertainties and, on the other hand, to consider a larger set of sources of information, from heterogeneous models to past data. This process decreases the chance of undesirable biases and leads to a controlled increase of the precision of the probabilistic assessment. In addition, the proposed Bayesian scheme allows (1) the assignment of a subjective reliability to single models, without requirement of completeness or homogeneity, and (2) a transparent and uniform evaluation of the strength of each piece of information used on the final results. The applicability of the method is demonstrated through the assessment of seismic hazard in the Emilia–Romagna region of northern Italy. In this application the results of a traditional Cornell–McGuire hazard model based on a logic tree are updated with the historical macroseismic records to provide a unified assessment that accounts for both sources of information.

We present the realization of a fault-source data set designed to become the starting point in regional-scale tsunami hazard studies. Our approach focuses on the parametric fault characterization in terms of geometry, kinematics, and assessment of activity rates, and includes a systematic classification in six justification levels of epistemic uncertainty related with the existence and behaviour of fault sources. We set up a case study in the central Mediterranean Sea, an area at the intersection of the European, African, and Aegean plates, characterized by a complex and debated tectonic structure and where several tsunamis occurred in the past. Using tsunami scenarios of maximum wave height due to crustal earthquakes (Mw=7) and subduction earthquakes (Mw=7 and Mw=8), we illustrate first-order consequences of critical choices in addressing the seismogenic and tsunamigenic potentials of fault sources. Although tsunamis generated by Mw=8 earthquakes predictably affect the entire basin, the impact of tsunamis generated by Mw=7 earthquakes on either crustal or subduction fault sources can still be strong at many locales. Such scenarios show how the relative location/orientation of faults with respect to target coastlines coupled with bathymetric features suggest avoiding the preselection of fault sources without addressing their possible impact onto hazard analysis results.

Multi-risk approaches have been recently proposed to assess and compare different risks in the same target area. The key points of multi-risk assessment are the development of homogeneous risk definitions and the treatment of risk interaction. The lack of treatment of interaction may lead to significant biases and thus to erroneous risk hierarchization, which is one of primary output of risk assessments for decision makers. In this paper, a formal statistical model is developed to treat interaction between two different hazardous phenomena in long-term multi-risk assessments, accounting for possible effects of interaction at hazard, vulnerability and exposure levels. The applicability of the methodology is demonstrated through two illustrative examples, dealing with the influence of (1) volcanic ash in seismic risk and (2) local earthquakes in tsunami risk. In these applications, the bias in single-risk estimation induced by the assumption of independence among risks is explicitly assessed. An extensive application of this methodology at regional and sub-regional scale would allow to identify when and where a given interaction has significant effects in long-term risk assessments, and thus, it should be considered in multi-risk analyses and risks hierarchization. © 2013 Springer Science+Business Media Dordrecht.

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.

The Campi Flegrei caldera is a restless structure affected by general subsidence and ongoing resurgence of its central part. The persistent activity of the system and the explosive character of the volcanism lead to a very high volcanic hazard that, combined with intense urbanization, corresponds to a very high volcanic risk. One of the largest sources of uncertainty in volcanic hazard/risk assessment for Campi Flegrei is the spatial location of the future volcanic activity. This paper presents and discusses a long-term probability hazard map for vent opening in case of renewal of volcanism at the Campi Flegrei caldera, which shows the spatial conditional probability for the next vent opening, given that an eruption occurs. The map has been constructed by building a Bayesian inference scheme merging prior information and past data. The method allows both aleatory and epistemic uncertainties to be evaluated. The probability map of vent opening shows that two areas of relatively high probability are present within the active portion of the caldera, with a probability approximately doubled with respect to the rest of the caldera. The map has an immediate use in evaluating the areas of the caldera prone to the highest volcanic hazard. Furthermore, it represents an important ingredient in addressing the more general problem of quantitative volcanic hazards assessment at the Campi Flegrei caldera. © 2011 Springer-Verlag.