Massive emissions of deep CO ₂ coupled to the 2009–2018 Central Italy earthquakes.

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

In this paper, we illustrate a Bayesian Event Tree to estimate Volcanic Hazard (BET_VH). The procedure enables us to calculate the probability of any kind of long-term hazardous event for which we are interested, accounting for the intrinsic stochastic nature of volcanic eruptions and our limited knowledge regarding related processes. For the input, the code incorporates results from numerical models simulating the impact of hazardous volcanic phenomena on an area and data from the eruptive history. For the output, the code provides a wide and exhaustive set of spatiotemporal probabilities of different events; these probabilities are estimated by means of a Bayesian approach that allows all uncertainties to be properly accounted for. The code is able to deal with many eruptive settings simultaneously, weighting each with its own probability of occurrence. In a companion paper, we give a detailed example of application of this tool to the Campi Flegrei caldera, in order to estimate the hazard from tephra fall.