Tierz, Pablo

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.

Pyroclastic density currents (PDCs) and lahars are two of the most destructive phenomena that are generated at volcanoes worldwide. Therefore, assessing their volcanic hazard is a primary step towards the estimation (and reduction) of volcanic risk around volcanoes. In this Editorial, we present a compilation of Original Research manuscripts that study PDCs and lahars, and contribute to different components of their volcanic hazard assessment. Taking these global perspectives into account, we also provide our own expert judgment on what future directions might be taken to keep improving these PDC and lahar hazard assessments moving forward.

A methodology for a comprehensive probabilistic tsunami hazard analysis (PTHA) 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 Source module 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 Propagation module the tsunami waves are simulated and propagated in the deep sea by a numerical model that solves the shallow water equations. In the Impact module the tsunami wave heights are estimated at the coast using the Green's amplification law. The selected tsunami intensity is the wave height. In the Bayesian module the probabilistic tsunami analysis computes the long‐term comprehensive Bayesian PTHA. 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 prior probability density functions are integrated with the likelihood derived from the historical information based on past tsunami data. The posterior probability density functions are evaluated to produce the hazard curves in selected sites of the Gulf of Naples.

Pyroclastic density currents (PDCs) are hot flowing mixtures of gas and pyroclasts that can cause widespread loss of life and structural damage around the erupting volcano. Hazard assessments that include quantification of aleatory and epistemic uncertainty are a necessary step toward calculating volcanic risk of PDCs in an accurate and complete manner. We develop a three-stage procedure to quantify such uncertainties for dense PDCs. First, the TITAN2D model is parameterized to simulate the PDC phenomenology at the target volcano. Second, TITAN2D is coupled with Polynomial Chaos Quadrature to propagate aleatory uncertainty from model parameters to hazard intensity measures (flow depth and speed). Third, the TITAN2D-PCQ analysis is merged with the Bayesian Event Tree for Volcanic Hazard to include other volcano-specific aleatory uncertainty and estimates of epistemic uncertainty. A comprehensive collection of probabilistic hazard curves and maps for flow depth and speed around the volcano is obtained through this methodology and its application is illustrated at Somma-Vesuvius (Italy). Our results indicate that, given an eruption from the current central crater, exceedance probabilities are around 30% (aleatory uncertainty only) and between 10% and 60% (aleatory and epistemic uncertainty), for flow depth = 1 m and flow speed = 2 m/s, over the first 2–3 km around the vent. Dense PDCs faster than 30 m/s may cover areas about 50 km 2 around the vent, on average, 1 every 10 eruptions. This type of probabilistic hazard assessment represents a crucial step toward quantitative volcanic risk of dense PDCs at Somma-Vesuvius and worldwide.

The metropolitan area of Napoli ( ∼ 3 M inhabitants) in southern Italy is located in between two explosive active volcanoes: Somma-Vesuvius and Campi Flegrei. Pyroclastic density currents (PDCs) from these volcanoes may reach the city center, as witnessed by the Late Quaternary stratigraphic record. Here we compute a novel multivolcano Probabilistic Volcanic Hazard Assessment of PDCs, in the next 50 years, by combining the probability of PDC invasion from each volcano (assuming that they erupt independently) over the city of Napoli and its surroundings. We model PDC invasion with the energy cone model accounting for flows of very different (but realistic) mobility and use the Bayesian Event Tree for Volcanic Hazard to incorporate other volcano-specific information such as the probability of eruption or the spatial variability in vent opening probability. Worthy of note, the method provides a complete description of Probabilistic Volcanic Hazard Assessment, that is, it yields percentile maps displaying the epistemic uncertainty associated with our best (aleatory) hazard estimation. Since the probability density functions of the model parameters of the energy cone are unknown, we propose an ensemble of different hazard assessments based on different assumptions on such probability density functions. The ensemble merges two plausible distributions for the collapse height, reflecting a source of epistemic (specifically, parametric) uncertainty. We also apply a novel quantification for a spatially varying epistemic uncertainty associated to PDC simulations.

Pyroclastic density currents (PDCs) are extremely dangerous phenomena so their modeling is essential for haz­ ard and risk purposes. However, PDCs are governed by very complex processes, making their deterministic prediction impossible. Probabilistic approaches are in a pioneering phase and feature large (and still unknown) uncertainties, from the natural variability of PDCs (aleatory uncertainty) to the main sources of epistemic uncertainty (input, parametric, theoretical and structural). In this chapter, we quantify these uncertainties by using the Energy Cone Model (ECM) in a Monte Carlo scheme applied to Mount Vesuvius. According to our results, theoretical uncertainty has the largest impact, 5 to 100 times bigger than input uncertainty, which seems to play a minor role. We find that conditional probabilities of PDC arrival (given an eruption of a specific size) show spatial distributions related to the surrounding topography. In particular, for medium and large eruptions, the conditional probability of PDCs traveling beyond Mount Somma is 1%–15% and 50%–60%, while they reach the Napoli airport in about 0%–1% and 0%–15% of the simulations, respectively. Small‐eruption PDCs remain restricted to the south flank and summit area. These results may guide future research devoted to reduce epistemic uncertainties and improve volcanic hazard analyses associated with PDCs.

Pyroclastic density currents (PDCs) are gravitydriven hot mixtures of gas and volcanic particles which can propagate at high speed and cover distances up to several tens of kilometers around a given volcano. Therefore, they pose a severe hazard to the surroundings of explosive volcanoes able to produce such phenomena. Despite this threat, probabilistic volcanic hazard assessment (PVHA) of PDCs is still in an early stage of development. PVHA is rooted in the quantification of the large uncertainties (aleatory and epistemic) which characterize volcanic hazard analyses. This quantification typically requires a big dataset of hazard footprints obtained from numerical simulations of the physical process. For PDCs, numerical models range from very sophisticated (not useful for PVHA because of their very long runtimes) to very simple models (criticized because of their highly simplified physics). We present here a systematic and robust validation testing of a simple PDC model, the energy cone (EC), to unravel whether it can be applied to PVHA of PDCs. Using past PDC deposits at Somma-Vesuvius and Campi Flegrei (Italy), we assess the ability of EC to capture the values and variability in some relevant variables for hazard assessment, i.e., area of PDC invasion and maximum runout. In terms of area of invasion, the highest Jaccard coefficients range from 0.33 to 0.86 which indicates an equal or better performance compared to other volcanic mass-flow models. The p values for the observed maximum runouts vary from 0.003 to 0.44. Finally, the frequencies of PDC arrival computed from the EC are similar to those determined from the spatial distribution of past PDC deposits, with high PDC-arrival frequencies over an ∼8-km radius from the crater area at Somma-Vesuvius and around the Astroni crater at Campi Flegrei. The insights derived from our validation tests seem to indicate that the EC is a suitable candidate to compute PVHA of PDCs.