Selva, Jacopo

Integrating diverse expert opinions in hazard and risk projects is essential to managing subjective decisions and quantifying uncertainty to produce stable and trustworthy results. A structured procedure is necessary to organize the gathering of experts' opinions while ensuring transparency, accountability, and independence in judgements. We propose a novel Multiple-Expert management Protocol (MEP) to address this challenge, providing procedural guidelines for conducting single to multi-hazard risk analyses. MEP establishes a workflow to manage subjectivity rooted in (i) moderated and staged group interactions, (ii) trackable blind advice through written elicitations with mathematical aggregation, (iii) participatory independent review, (iv) close cooperation between scientific and managerial coordination, and (v) proper and comprehensive documentation. Originally developed for stress testing critical infrastructure, MEP is designed as a single, flexible, technology-neutral procedural workflow applicable to various sectors. Moreover, its scalability allows it to adapt from high to low-budget projects and from complex probabilistic multi-hazard risk assessments to standard single-hazard analyses, with different experts' degree and type of involvement depending on available funding and emerging controversies. We present two compelling case studies to showcase MEP's practical applicability: a multi-hazard risk analysis for a port infrastructure and a single-hazard regional tsunami hazard assessment.

Nisyros Island (Greece) is affected by widespread gas emissions from fumarolic fields located at the bottom of hydrothermal craters in the southern part of its caldera. This morphology and the current low gas fluxes make Nisyros an ideal site for testing the limits of physics-based gas dispersal models in confined and low-emission conditions. Here, we focused our attention on the local scale volcanic gas dispersion from the Stephanos hydrothermal crater. In April 2023, a 1-week survey was carried out to measure weather data, CO 2 and H 2 S gas fluxes, air concentrations from portable gas stations, and chemical composition of fumarolic gases and to acquire thermal images of the crater floor. These data were used as inputs and boundary conditions for numerical simulations using a DISGAS-2.6.0 model in order to quantify the present-day volcanic degassing and its associated uncertainties, accounting for the meteorological variability. Model results are provided in terms of H 2 S probabilistic exceedance and persistence maps, showing gas concentrations within the crater that fall below the thresholds indicated for the occurrence of serious respiratory problems. Since DISGAS-2.6.0 does not account for chemical reactions, this study represents a good opportunity to discuss the methodological limits of simulating the dispersion of H 2 S which is challenging due to its rapid degradation and dilution in the atmosphere. In this regard, we also provided an empirical law of the H 2 S depletion in low-emission conditions that takes into account the uncertainties related to the field measurements.

Owing to the current lack of plausible and exhaustive physical pre-eruptive models, often volcanologists rely on the observation of monitoring anomalies to track the evolution of volcanic unrest episodes. Taking advantage from the work made in the development of Bayesian Event Trees (BET), here we formalize an entropy-based model to translate the observation of anomalies into probability of a specific volcanic event of interest. The model is quite general and it could be used as a stand-alone eruption forecasting tool or to set up conditional probabilities for methodologies like the BET and of the Bayesian Belief Network (BBN). The proposed model has some important features worth noting: (i) it is rooted in a coherent logic, which gives a physical sense to the heuristic information of volcanologists in terms of entropy; (ii) it is fully transparent and can be established in advance of a crisis, making the results reproducible and revisable, providing a transparent audit trail that reduces the overall degree of subjectivity in communication with civil authorities; (iii) it can be embedded in a unified probabilistic framework, which provides an univocal taxonomy of different kinds of uncertainty affecting the forecast and handles these uncertainties in a formal way. Finally, for the sake of example, we apply the procedure to track the evolution of the 1982–1984 phase of unrest at Campi Flegrei.

The management of multiple hazards simultaneously impacting on a territory is a challenge for effective risk mitigation. This is particularly true on active volcanoes like Mt. Etna, characterized by effusive and explosive eruptions, often coupled with an intense seismic activity. This work aims at presenting the approach of the PANACEA project on the treatment of multi-hazards in terms of risk, which requires a common definition of the exposed elements and their vulnerability. Another aspect emerging from the recent and historical volcanic crises at Etna, is the occurrence of cascading effects and the problem of assessing their short-term interactions. Here we present a risk model taking into account a set of sequences of hazardous events which may result from a volcano unrest to possible impacts to some infrastructural elements. The outcomes of the project are intended to be a significant step towards a more comprehensive resilience to volcanic disasters, leading to a more safe society.

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

Probabilistic Seismic Hazard Assessment (PSHA) evaluates the probability of exceedance of a given earthquake intensity threshold like the Peak Ground Acceleration, at a target site for a given exposure time. The stochasticity of the occurrence of seismic events is modelled by stochastic processes and the propagation of the earthquake wave in the soil is typically evaluated by empirical relationships called Ground Motion Prediction Equations. The large uncertainty affecting PSHA is quantified by defining alternative model settings and/or model parametri zations. In this work, we propose a novel Bootstrapped Modularised Global Sensitivity Analysis (BMGSA) method for identifying the model parameters most important for the uncertainty in PSHA, that consists in generating alternative artificial datasets by bootstrapping an available input-output dataset and aggregating the individual rankings obtained with the modularized method from each of those. The proposed method is tested on a realistic PSHA case study in Italy. The results are compared with a standard variance-based Global Sensitivity Analysis (GSA) method of literature. The novelty and strength of the proposed BMGSA method are both in the fact that its application only requires input-output data and not the use of a PSHA code for repeated calculations.

Nowadays, modeling of tephra fallout hazard is coupled with probabilistic analysis that takes into account the natural variability of the volcanic phenomena in terms of eruption probability, eruption sizes, vent position, and meteorological conditions. In this framework, we present a prototypal methodology to carry out the long-term tephra fallout hazard assessment in southern Italy from the active Neapolitan volcanoes: Somma–Vesuvius, Campi Flegrei, and Ischia. The FALL3D model (v.8.0) has been used to run thousands of numerical simulations (1500 per eruption size class), considering the ECMWF ERA5 meteorological dataset over the last 30 years. The output in terms of tephra ground load has been processed within a new workflow for large-scale, high resolution volcanic hazard assessment, relying on a Bayesian procedure, in order to provide the mean annual frequency with which the tephra load at the ground exceeds given critical thresholds at a target site within a 50-year exposure time. Our results are expressed in terms of absolute mean hazard maps considering different levels of aggregation, from the impact of each volcanic source and eruption size class to the quantification of the total hazard. This work provides for the first time, a multi-volcano probabilistic hazard assessment posed by tephra fallout, comparable with those used for seismic phenomena and other natural disasters. This methodology can be applied to any other volcanic areas or over different exposure times, allowing researchers to account for the eruptive history of the target volcanoes that, when available, could include the occurrence of less frequent large eruptions, representing critical elements for risk evaluations.

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.

Seismic Probabilistic Tsunami Hazard Assessment (SPTHA) is a framework for calculating the probability that seismically induced tsunami waves exceed a specific threshold height, over a given time span and a specific region (i.e. regional SPTHA) or site (i.e. local SPTHA). To account for the uncertainty of the possible sources, SPTHA must integrate the results of a large number of computationally demanding tsunami simulations In this work, we innovatively use Parallel density scanned Adaptive Kriging (P-ds AK) to overcome the computational efficiency challenge of local SPTHA within a framework that consists in modeling/retrieving the full spectrum of possible earthquake triggering events at the regional level, filtering sources not relevant for the target, adopting a clustering procedure to select “representative scenarios” for inundation modeling, and, finally, adopt P-ds AK to identify the clusters centroids that most influence the hazard intensity (i.e., wave height) in the areas of interest. This approach is applied in the area of the oil refinery located in Milazzo (Italy). The application shows a consistent reduction of the number of high-resolution tsunami simulations required for the evaluation of the hazard curves over a set of inland Point of Interest (PoIs), either concentrated in one specific area or distributed along the coast.

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project.