A simple two-state model interprets temporal modulations in eruptive activity and enhances multivolcano hazard quantification.

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.

Deep versus shallow sources of CO2 and Rn from a multi-parametric approach: the case of the Nisyros caldera (Aegean Arc, Greece).

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.

Beyond eruptive scenarios: assessing tephra fallout hazard from Neapolitan volcanoes.

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.