Cappello, Annalisa

The assessment of the spatial probability of future vent opening is one of the key factors in quantifying volcanic hazard, especially for active volcanoes where eruptions can occur at different locations and altitudes over distributed areas. Mount Etna (Italy), one of the most active volcanoes in the world, exhibits such variability, and its flank eruptions can harm people, properties and services over the volcano’s slopes. In this paper, we quantify the spatial probability of future vent opening for Etna’s flank eruptions, adopting a kernel analysis and testing different functions (exponential, Cauchy, uniform and Gaussian). Starting from the assumption that the location of past fissures is indicative of where future events will occur, we consider the flank eruptions of the last 4000 years, thus accounting for a much longer and complete record than in previous studies. The large dataset of eruptive fissures enables splitting the data into training and testing subsets. This allows selecting the best kernel model, testing the completeness of the fissure dataset and investigating a possible migration through time in fissure location. The results show that neither under-recording nor possible migration over time significantly affects the informative value of previous flank fissures in forecasting the location of future ones. The resulting map highlights that the most likely opening area follows a northeast-to-south trend, corresponding to the location of the most active rifts. It also shows that the southern flank of the volcano, which is the most urbanized one, sits downhill of the largest cumulated probability area for flank eruption. We also run sensitivity analyses to test the effect of (i) restricting the data to the most recent 400 years and (ii) including the information on the stress induced on the mapped fissures by sources of deformation proposed in the literature for recent eruptions of Etna. The sensitivity analyses confirm the main features of the proposed map and add information on the epistemic uncertainty attached to it.

Bridge management is a complex and challenging task at global scale. The main purpose of bridge management is to facilitate the identification of bridge deficiencies in order to carefully plan and execute maintenance operations, and ensure the continued safety of traffic. In 2020, the Italian Ministry of Infrastructure issued mandatory guidelines for risk classification and management, safety assessment and monitoring of bridges. These guidelines rely upon bridge inventory, which includes a number of heterogeneous inspection data organized in different files. This paper presents a hardware and software architecture for the automatic entry of all inspection data (e.g. spatial information, sheets and photos) in a relational spatial database and their visualization through a Desktop and Web GIS (Geographic Information System) application. The procedure is summarized in a new Bridge Management System (BMS), whose main novelty is the full control of the georeferenced infrastructures, with the opportunity of improving the status check and management of bridges and their components. The BMS has been developed using data collected during inspection surveys requested by the Consortium of Sicilian Highways on the conditions of the bridges and viaducts belonging to motorways in Sicily (Italy). The developed system overcomes most of the common limitations in existing BMSs, such as the limited capacity of visualizing geospatial data and the reduced supported data formats, providing a valid Decision Support System in evaluating and prioritizing repair and maintenance actions.

How (in)formal should the classic expression describing risk as the product of hazard, exposure, and vulnerability be considered? What would be the most complete way to describe the process of risk mitigation? These are the questions we try to answer here, using a formal, mathematically sound yet abstract description of hazard, exposure, vulnerability, and risk. We highlight the elements that can be affected for the purpose of mitigation and show how this can improve the quantitative assessment of the procedural aspects of risk mitigation, both long- and short-term, down to the timescale of emergency response.

The development of probabilistic maps associated with lava flow inundation is essential to assess hazard in open vent volcanoes, especially those that have highly urbanized flanks. In this study we present the new lava flow hazard map linked to the summit eruptions of Mt. Etna, which has been developed using a probabilistic approach that integrates statistical analyses of the volcanological historical data with numerical simulations of lava flows. The statistical analysis of volcanological data (including vent location, duration and lava volumes) about all summit eruptions occurred since 1998 has allowed us both to estimate the spatiotemporal probability of future vent opening and to extract the effusion rate curves for lava flow modelling. Numerical simulations were run using the GPUFLOW model on a 2022 Digital Surface Model derived from optical satellite images. The probabilistic approach has been validated through a back-analysis by calculating the fit between the expected probabilities of inundation and the lava flows actually emplaced during the 2020-2022 period. The obtained map shows a very high probability of inundation of lava flows emitted at vents linked to the South East Crater, according to the observation of the eruptive dynamics in the last decades.

Hydrothermal systems can generate phreatic and/or phreatomagmatic explosions with little warning. Understanding the temporal and spatial evolution of geophysical and geochemical signals at hydrothermal systems is crucial for detecting precursory signs of unrest and informing on hazards. Thermal signatures of such systems are poorly defined because data records are often too short or discrete compared to activity timescales, which can be decadal. La Fossa system of Vulcano has been monitored since the 1980s and entered a period of unrest in 2021. We assessed the thermal signature of La Fossa using ground- and satellite-based data with various temporal and spatial scales. While continuously recording stations provided continuous but point-based measurements, fumarole field vent surveys and infrared images obtained from satellite-flown sensors (ASTER and VIIRS) allowed lower temporal resolution but synoptic records to be built. By integrating this multi-resolution data set, precursory signs of unrest could retrospectively be detected from February to June 2021. The intensity of all unrest metrics increased during the summer of 2021, with an onset over a few days in September 2021. By September, seismic, CO2, SO2 and other geochemical metrics also indicated unrest, leading Civil Protection to raise the alert level to yellow on October 1. Heat flux, having been 4 MW in May 2019, increasing to 90 MW by September, and peaking at 120 MW in March 2022. We convolved our thermal data sets with all other monitoring data to validate a Vulcano Fossa Unrest Index (VFUI), the framework of which can be potentially applied to any hydrothermal system. The VFUI highlighted four stages of unrest, none of which were clear in any single data set: background, precursory, onset, and unrest. Onset was characterized by a sudden release of fluids, likely caused by the failure of sealed zones that had become pressurized during the precursory phase that began possibly as early as February 2021. Unrest has been ongoing for more than 18 months and may continue for several more years. Our understanding of this system behavior has been due to hindsight, but demonstrates how multiparametric surveys can track and forecast unrest.

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.

Between December 2020 and February 2022, the South East Crater of Etna has been the source of numerous eruptions, mostly characterized by the emission of lava fountains, pyroclastic material and short-lasting lava flows. Here we estimate the volume and distribution of the lava deposits by elaborating multi-source satellite imagery. SEVIRI data have been elaborated using CL-HOTSAT to estimate the lava volume emitted during each event and calculate the cumulative volume; Pléiades and WorldView-1 data have been used to derive Digital Surface Models, whose differences provide thickness distributions and hence volumes of the volcanic deposits. We find a good agreement, with the total average lava volume obtained by SEVIRI reaching 73.2 × 106 m3 and the one from optical data amounting to 67.7 × 106 m3. This proves the robustness of both techniques and the accuracy of the volume estimates, which provide important information on the lava flooding history and evolution of the volcano.

Classification of roof covering in urban areas using aerial imagery is a challenging task. In this work we present a preliminary mapping of roofs using the high-resolution Skysat multispectral images. The classification is performed using a two-stage machine learning approach: the first stage includes a supervised classification for land use, while the second stage includes the classification of terraces and roofs with one or more pitches in those areas previously recognized as edifices. The methodology has been tested to classify the roofs in the north-east part of the Stromboli Island (Sicily, Italy). Our preliminary results are promising and encourage us to pursue further developments as ways to improve accuracy and reliability of the classification.

This work presents the first results of the PANACEA project regarding the assessment of different volcano-related hazards at Mt. Etna (lava and pyroclastic flows, tephra fallout and earthquakes) by exploiting data deriving from the volcano’s history with accurate physical–mathematical models. Volcano-related hazards are distributed differently on Etna—from the deserted summit area down to the densely populated flanks—but must be considered together for long-term territorial planning.

Timely estimations of magma volumes emitted during an eruption or a sequence of explosive events are vital for investigating the eruptive activity and evaluating the associated hazard. A reliable method for estimating erupted volumes is based on the analysis of digital surface models that nowadays can be obtained subsequently using stereo or tri-stereo optical satellite imagery. However, the real-time estimation of the erupted volumes is still an open challenge. Here, we explore the capacity of extracting volume estimates from continuous measurements of volumetric strain changes recorded by borehole dilatometers. We compare the volumes derived from numerous high spatial resolution satellite images with high precision strain records at Etna during 2020–2022, when more than 60 lava fountains occurred. The good correlation between the two data sets shows that strain changes can be used as a proxy to estimate the emitted volumes both over time and in real-time.