Roberto Tonini

Recognizing and tracking volcanic hazards related to non-magmatic unrest: a review

Eruption forecasting is a major goal in volcanology. Logically, but unfortunately, forecasting hazards related to non-magmatic unrest is too often overshadowed by eruption forecasting, although many volcanoes often pass through states of non-eruptive and non-magmatic unrest for various and prolonged periods of time. Volcanic hazards related to non-magmatic unrest can be highly violent and/or destructive (e.g., phreatic eruptions, secondary lahars), can lead into magmatic and eventually eruptive unrest, and can be more difficult to forecast than magmatic unrest, for various reasons. The duration of a state of non-magmatic unrest and the cause, type and locus of hazardous events can be highly variable. Moreover, non-magmatic hazards can be related to factors external to the volcano (e.g., climate, earthquake). So far, monitoring networks are often limited to the usual seismic-ground deformation-gas network, whereas recognizing indicators for non-magmatic unrest requires additional approaches. In this study we summarize non-magmatic unrest processes and potential indicators for related hazards. We propose an event-tree to classify non-magmatic unrest, which aims to cover all major hazardous outcomes. This structure could become useful for future probabilistic non-magmatic hazard assessments, and might reveal clues for future monitoring strategies.

PyBetVH: A Python tool for probabilistic volcanic hazard assessment and for generation of Bayesian hazard curves and maps

Abstract PyBetVH is a completely new, free, open-source and cross-platform software implementation of the Bayesian Event Tree for Volcanic Hazard (BET_VH), a tool for estimating the probability of any magmatic hazardous phenomenon occurring in a selected time frame, accounting for all the uncertainties. New capabilities of this implementation include the ability to calculate hazard curves which describe the distribution of the exceedance probability as a function of intensity (e.g., tephra load) on a grid of points covering the target area. The computed hazard curves are (i) absolute (accounting for the probability of eruption in a given time frame, and for all the possible vent locations and eruptive sizes) and (ii) Bayesian (computed at different percentiles, in order to quantify the epistemic uncertainty). Such curves allow representation of the full information contained in the probabilistic volcanic hazard assessment (PVHA) and are well suited to become a main input to quantitative risk analyses. PyBetVH allows for interactive visualization of both the computed hazard curves, and the corresponding Bayesian hazard/probability maps. PyBetVH is designed to minimize the efforts of end users, making PVHA results accessible to people who may be less experienced in probabilistic methodologies, e.g. decision makers. The broad compatibility of Python language has also allowed PyBetVH to be installed on the VHub cyber-infrastructure, where it can be run online or downloaded at no cost. PyBetVH can be used to assess any type of magmatic hazard from any volcano. Here we illustrate how to perform a PVHA through PyBetVH using the example of analyzing tephra fallout from the Okataina Volcanic Centre (OVC), New Zealand, and highlight the range of outputs that the tool can generate.

A new Bayesian Event Tree tool to track and quantify volcanic unrest and its application to Kawah Ijen volcano

Although most of volcanic hazard studies focus on magmatic eruptions, volcanic hazardous events can also occur when no migration of magma can be recognized. Examples are tectonic and hydrothermal unrest that may lead to phreatic eruptions. Recent events (e.g., Ontake eruption on September 2014) have demonstrated that phreatic eruptions are still hard to forecast, despite being potentially very hazardous. For these reasons, it is of paramount importance to identify indicators that define the condition of nonmagmatic unrest, in particular for hydrothermal systems. Often, this type of unrest is driven by movement of fluids, requiring alternative monitoring setups, beyond the classical seismic-geodetic-geochemical architectures. Here we present a new version of the probabilistic BET (Bayesian Event Tree) model, specifically developed to include the forecasting of nonmagmatic unrest and related hazards. The structure of the new event tree differs from the previous schemes by adding a specific branch to detail nonmagmatic unrest outcomes. A further goal of this work consists in providing a user-friendly, open-access, and straightforward tool to handle the probabilistic forecast and visualize the results as possible support during a volcanic crisis. The new event tree and tool are here applied to Kawah Ijen stratovolcano, Indonesia, as exemplificative application. In particular, the tool is set on the basis of monitoring data for the learning period 2000-2010, and is then blindly applied to the test period 2010-2012, during which significant unrest phases occurred.

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.

Phreatic eruptions at crater lakes: occurrence statistics and probabilistic hazard forecast

Phreatic eruptions, although posing a serious threat to people in crater proximity, are often underestimated and have been comparatively understudied. The detailed eruption catalogue for Ruapehu Volcano (New Zealand) provides an exceptional opportunity to study the statistics of recurring phreatic explosions at a crater lake volcano. We performed a statistical analysis on this phreatic eruption database, which suggests that phreatic events at Ruapehu do not follow a Poisson process. Instead they tend to cluster, which is possibly linked to an increased heat flow during periods of a more shallow-seated magma column. Larger explosions are more likely to follow shortly after smaller events, as opposed to longer periods of quiescence. The absolute probability for a phreatic explosion to occur at Ruapehu within the next month is about 10%, when averaging over the last 70 years of recording. However, the frequency of phreatic explosions is significantly higher than the background level in years prior to magmatic episodes.Combining clast ejection simulations with a Bayesian event tree tool (PyBetVH) we perform a probabilistic assessment of the hazard due to ballistic ejecta in the summit area of Ruapehu, which is frequently visited by hikers. Resulting hazard maps show that the absolute probability for the summit to be affected by ballistics within the next month is up to 6%. The hazard is especially high on the northern lakeshore, where there is a mountain refuge.Our results contribute to the local hazard assessment as well as the general perception of hazards due to steam-driven explosions.

From regional to local SPTHA: efficient computation of probabilistic inundation maps addressing near-field sources

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 5 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 farand 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 10 demonstrated that special treatment of local sources plays a fundamental role and is applicable in local scale SPTHA. Copyright statement.

A Bayesian Seismic Hazard Analysis for the city of Naples

In this paper we explore the feasibility of formulating the hazard assessment procedure to include the information of past earthquakes into the probabilistic seismic hazard analysis, together with the use of an ensemble modeling technique. This strategy allows on one hand to enlarge the information used in the evaluation of the hazard, from alternative models for the earthquake generation process to past shaking, and on the other hand to explicitly account for the uncertainties. The Bayesian scheme we propose is applied to evaluate the seismic hazard of Naples. The framework in which we have embedded the tools is flexible to include all types of uncertainties. Here we focus on a sensitive study of the earthquake occurrence by implementing models that span from random to cluster–type temporal behavior and models that include quasi-periodic occurrence of earthquakes on faults. We implement 5 different spatio–temporal models to parameterize the occurrence of earthquakes potentially dangerous for Naples. Subsequently we combine these hazard curves with ShakeMaps of past earthquakes that have been felt in Naples since 1200 A.D.. The results are posterior ensemble hazard curves for three exposure times, e.g., 5, 10, and 50 years, in a dense grid that cover the municipality of Naples, considering rocky soil and including the site amplification. Our results show the importance to include the data from past shaking since the difference between the prior and the posterior is about 8 − 15% for the different exposure times.