One of the most hazardous phenomena which characterizes the summit activity at Mt. Etna (Italy) is represented by pyroclastic avalanches, gravity-driven flows of pyroclastic material at high particle concentration, characterized by modest volumes (usually lower than a few millions of cubic metres) and small thickness-to-length ratio. The frequency of pyroclastic avalanches at Mt. Etna has increased during the recent 2020-2022 volcanic activity, where a series of intense paroxysmal eruptions took place at the South East Crater (SEC). The accumulation of proximal deposits generated by the explosive activity led to the growth of SEC, which posed favorable conditions in triggering partial collapses of unstable flanks of the crater. Pyroclastic avalanches propagated mainly eastward and southward of the SEC, up to distances of about 2 km from the source.

Numerical modeling of pyroclastic avalanche propagation and emplacement constitutes a powerful tool for hazard assessment, despite several difficulties in simulating the rheology of the polydisperse granular mixture. In this work, pyroclastic avalanches are simulated using the open-source code IMEX-SfloW2D. Depth-averaged equations are implemented in the code to model the granular flow as an incompressible, single-phase granular fluid, described by the Voellmy–Salm rheology. Comparison of numerical results with the well-documented pyroclastic avalanches occurred on 11 February 2014 and during the 10 February 2022 paroxysmal eruption (one of the most intense of the 2020-2022 series) allowed us to investigate the variability of the avalanche dynamics with its volume, the influence of the three-dimensional volcano morphology, and to statistically calibrate the unknown rheological parameters (i.e., the dry-friction coefficient µ and viscous-turbulent friction coefficient ξ). Finally, we provide a preliminary quantification of new potential collapse scenarios, to assess pyroclastic avalanche probabilistic hazard on the summit area, one of the most preferential tourists destination at Mt. Etna.

How to cite: de’ Michieli Vitturi, M., Zuccarello, F., and Esposti Ongaro, T.: Probabilistic hazard assessment of pyroclastic avalanches at Mt. Etna volcano through numerical modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7974, https://doi.org/10.5194/egusphere-egu23-7974, 2023.

Mt. Etna volcanic activity has been characterized, in the last two decades, by more than 150 paroxysmal events (from moderate to intense and impulsive explosive activity, coupled sometime to voluminous lava flows) as well as by some large eruptive events (e.g., 2001, 2002-03, 2004-05, 2006, 2008) involving the upper sector of the northern and southern flanks of the volcano, along with the summit craters. Taking advantage of an extensive dataset of continuous GNSS stations covering the entire volcano edifice, we propose an unprecedented and detailed picture of different deformative stages. Raw GNSS observations, are processed by using the GAMIT/GLOBK software and achieved results, e.g. station daily time series and network-scale surface deformation fields, are referred to a local reference frame. By inspecting the daily baseline changes for EDAM and EMGL stations we detected a total of 59 different ground deformation phases consisting in 29 inflation phases, 21 deflation phases, 5 magmatic intrusions and 4 periods with no significant deformation. The surface deformation for each detected phase is used to constrain isotropic half-space elastic inversion models, therefore providing significant constraints on subsurface Mt. Etna’s magmatic storages. We integrate our results with recent tomographic models, correlating the inferred sources with V_P and V_P/V_S anomalies, in order to provide exhaustive interpretative model into the general volcano-tectonic context of Mt. Etna and in turn, new insight on hazard assessment.

How to cite: Palano, M., Pezzo, G., and Chiarabba, C.: Mt. Etna volcano: what have we learned from 20 years of continuous GNSS observations?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8913, https://doi.org/10.5194/egusphere-egu23-8913, 2023.

The San Bartolo eruption is the last flank eruption occurred at Stromboli volcano about 2 ka ago on the NE flank of the island. Despite its importance in being the most recent example of flank activity outside the barren Sciara del Fuoco slope, where the recent activity concentrated, some important volcanological data, such as the duration and lava volume have not yet been provided. Here, we present a new simulation of the San Bartolo eruption carried out using a combination of field analyses and numerical modelling. In particular, we used the CL-HOTSAT satellite monitoring system to estimate the effusion rate and erupted volume of the 2002-03 eruption, which formed a similar lava flow field extending from about 600 m in elevation to the coast. These were used as input of the physics-based model GPUFLOW to reproduce the emplacement dynamics of the San Bartolo lava flow. The aim is to reconstruct the sequence of events and infer a possible duration and impact of the eruption. Our results can provide a useful scenario should a flank eruption occur in the future, a possibility that was close to happening in 1998, when the ground deformation stations revealed a lateral intrusion in the shallow supply system of the volcano.

How to cite: Ganci, G., Bilotta, G., Cappello, A., and Calvari, S.: The San Bartolo lava flow field, Stromboli volcano, Italy: Simulation of the most recent historic flank eruption (2 kyr) affecting the inhabited area aimed at hazard assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14042, https://doi.org/10.5194/egusphere-egu23-14042, 2023.

Combining field measurements, satellite estimates and numerical modeling provide great advantages for the continuous monitoring of effusive eruptions. Here we demonstrate the potential of a new integrated monitoring system called HOTFLOW developed for Etna volcano, which is based on the CL-HOTSAT thermal monitoring system for the processing of satellite imagery and GPUFLOW model for the simulation of lava flows. The potential of HOTFLOW is demonstrated here using the ongoing eruption of Mount Etna started on November 27, 2022. We provide insights into lava flow field evolution by supplying detailed views of flow field construction (e.g., the opening of ephemeral vents) that are useful for more accurate and reliable forecasts of the eruptive activity. Moreover, we give a detailed chronology of the lava flow activity based on field observations and satellite images (i.e. SEVIRI, MODIS, Landsat 8/9, Sentinel-2, Planetscope, Skysat), assess the potential extent of impacted areas, map the evolution of lava flow field, and provide lava flow hazard projections. 

How to cite: Cappello, A., Bilotta, G., Ganci, G., and Zuccarello, F.: Volcano hazard monitoring at Mount Etna: the 2022 case study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13903, https://doi.org/10.5194/egusphere-egu23-13903, 2023.

Gravity measurements are increasingly used for high-precision and high-resolution Earth investigation. Recent times highlight the intention to combine both terrestrial and satellite data in order to reach higher accuracy for several purposes such as geological structures determination and geoid models construction.

Here we present results of a comparison between a twenty-year (2002-2022) relative and absolute gravity data collected through the Microg LaCoste FG5#238 absolute gravimeter (AG), in the framework of repeated measurements in one station at about 1750 m above sea level and the satellite gravity data provided by CNES/GRGS RL05 Earth gravity field models, from GRACE and SLR data.

The comparison allows to estimate the long-term correlation between the two dataset and a remarkably good fit was found in the long-term trend, revealing gravity changes most likely due to hydrological and volcanological effects.

Our study shows how the combination of terrestrial and satellite data can be used to obtain a fuller and more accurate picture of the temporal characteristics of the studied processes. The combined use of these dataset results crucial especially in a harsh, unsteady and changing environment as well as the Etna volcano.

How to cite: Samperi, L., Pereira, A., Greco, F., Carbone, D., Contrafatto, D., Messina, A. A., Mirabella, L., Pacino, M. C., and Pereira, A.: Comparison between a 20-year terrestrial and satellite gravity data at Mt. Etna volcano (Italy)., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15614, https://doi.org/10.5194/egusphere-egu23-15614, 2023.

The large amount of lava outflows during effusive eruptions can cause profound morphological changes, affecting both the natural and inhabited environment, destroying buildings, agricultural fields and important infrastructures such as roads, power lines, aqueducts and even modified the coastline. The ongoing demographic congestion around volcanic structures increases the potential risks and costs that lava flows represent and leads to a growing demand for implementing effective risk mitigation measures. Therefore, it is important to assess the elements at risk in volcanic areas to establish the mitigation actions to reduce the lava flow risk. Risk management for volcanoes is not just an emergency response to save lives but is also important in terms of economic loss. However, the collection of data regarding exposed elements surrounding the volcanoes is a lengthy and time-consuming process but utilizing the satellite images together with several machine learning techniques helps address this goal. We propose a cloud based platform in Google Colab using Land Use and Land Cover (LULC) classifiers to automatically assess the elements at risk by exploiting freely available high spatial resolution satellite images. This procedure allows to get an updated map of elements at risks in volcanic areas worldwide and will allow to routinely update the exposure map and thus risk map. In fact, up-to-date risk maps are fundamental to reaching the optimal decision in case of any hazard and crisis and can help us add or delete critical zones around the volcano. Using the freely available Sentinel 2-Multispectral Instrument (MSI) images and deep learning models, we aim to test the LULC applicability to a variety of volcanic areas whilst comparing the performances of two Convolutional Neural Network (CNN) architectures, namely VGG16 and ResNET50.

How to cite: Corradino, C., Cariello, S., Torrisi, F., Amato, E., Zago, V., and Del Negro, C.: Deep Learning for volcanic risk assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15785, https://doi.org/10.5194/egusphere-egu23-15785, 2023.

Timely forecasting of the evolution of lava flows is one of the key elements for assessing volcanic hazards. Lava flows are among the main hazardous phenomena during an effusive eruption, due to the possibility to reach urban areas and cause damage to infrastructure. Physical-mathematical models can be used to estimate the dynamics of a fluid or the fluid-solid interactions, in particular for the case of lava flows. However, high fidelity models require long execution times and large computational resources. Recently, artificial intelligence (AI) has been adopted to emulate physics-based models and deliver similar results, speeding up the simulations. We will discuss the possibility to use AI-based approaches to emulate highly complex numerical models used to simulate the spatio-temporal evolution of lava flows. Analyzing and treating the formal mathematical aspects of the models under analysis, we will verify and validate the models using test cases associated with the main features of lavas, discussing the accuracy and the performance offered by the two approaches.

How to cite: Zago, V., Amato, E., Cariello, S., Corradino, C., Torrisi, F., and Del Negro, C.: On Artificial Intelligence-based emulators of physical models to forecast the evolution of lava flows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16305, https://doi.org/10.5194/egusphere-egu23-16305, 2023.

Buoyant jets are ubiquitous in both naturally- and industrially-derived environmental flows (e.g. volcanic eruptions, marine wastewater discharges, industrial atmospheric emissions), leading to significant and wide-ranging societal, economic, and environmental impacts. For example, during the 2010 eruption of Eyjafjallajökull in Iceland, European and North American airspace was closed for over a month, causing societal disruption and costing the aviation industry millions of dollars per day whilst flight restrictions were in place. Understanding the fundamental behaviour of buoyant jets is therefore crucial to minimising their potential impacts. A buoyant jet can be divided into two regions: a momentum-driven jet region close to the source, and a buoyancy-driven plume region further away from the source. Well-established integral model theories have been developed that are based on detailed knowledge of how the time-averaged behaviour in the plume region is affected by steady source conditions in the jet region. These steady-state theories underpin many of the numerical models used to predict the evolutionary behaviour of buoyant jets, particularly when quantitative data is difficult to obtain directly from the source conditions, due to physical and practical limitations. As such, the assumption of time-averaged conditions at the source eliminates any variability in the downstream plume behaviour associated with source unsteadiness. Observations of evolving buoyant jets at field scales, such as during pulsatory volcanic eruptions, indicates a potential disconnect between these well-established steady-state theories and reality.

The current study aims to address this disconnect by evaluating the impact of source unsteadiness on the evolving downstream plume behaviour by conducting a series of scaled parametric experiments of buoyant jets discharged vertically into both homogeneous and stratified ambient water bodies. The fresh water source fluid of density ρ₀ = 1000 kg.m^-3, with a known concentration of fluorescent dye or seeding particles added, was pumped into a stagnant, homogeneous or stratified saline water ambient volume with density ranging from ρ₁ = 1010 – 1030  kg.m^-3. Unsteady buoyant jet source conditions were achieved using an electronically operated solenoid valve to control the rate of valve opening and closing, thus creating pulsatory discharge conditions with a known frequency. These unsteady source conditions could then be compared directly with equivalent steady discharges, permitting a comprehensive evaluation of the evolving plume behaviour (e.g. geometry, velocity structure, dye concentration, and entrainment characteristics) in response to source variability. A range of measurement techniques, including particle image velocimetry, ultrasonic velocity profiling and laser-induced fluorescence, was adopted in the study. The implications of the experimental results comparing steady versus unsteady plume dynamics will be discussed in the context of the evolution of volcanic plumes.

How to cite: Hetherington, M., Cuthbertson, A., Dawson, S., and Dioguardi, F.: Quantifying the impact of source variability on unsteady buoyant jet behaviour, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-310, https://doi.org/10.5194/egusphere-egu23-310, 2023.

To learn more about the physical processes related to volcanic activity, more and more data from extensive networks of seismic stations is being collected and analyzed. Conventionally, this data is identified and classified manually – a labor-intensive and time-consuming process. Here, we propose a classification method based on the clustering of wavelet scattering transforms of the volcanic events, which are embedded into a lower dimensional space, using t-distributed stochastic neighbor embedding (t-SNE). Wavelet scattering is chosen because of its advantageous properties, such as the invariance of the representation, the high information content, and its stability. For clustering, the spectral clustering method is used. By embedding the data to a pre-trained t-SNE scaffolding a supervised classification method is also possible. For classification, a simple k-nearest neighbor-classifier is used. The method is tested on events from the Llaima volcano in Chile, under supervised and also unsupervised conditions. These lead to promising results with a classification accuracy of 97% in the unsupervised and 99% in the supervised case, respectively.

How to cite: Laumann, P., Srivastava, N., Li, W., and Ruempker, G.: Volcano-seismic event classification using wavelet scattering transforms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17117, https://doi.org/10.5194/egusphere-egu23-17117, 2023.

Volcanic clouds are a major hazard to air traffic, public health, infrastructure, and economic sectors. Therefore, monitoring and tracking volcanic clouds and determining eruptive source parameters (e.g., erupted volume, plume height, mass eruption rate) is crucial to characterizing eruption dynamics and assessing associated natural hazards.
A literature review is proposed in this study to understand better how Earth Observation (EO) satellite sensors are used to monitor, track, and model ash and SO₂ during volcanic eruptions, ranging from optical (multispectral, hyperspectral, and LiDAR) to radar and thermal data. This review seeks to characterize the different sensors algorithms and models, their accuracy, advantages, and limitations. A systematic literature review was carried out to accomplish this goal utilizing the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) standard. To the best of the author's
knowledge, it is the first systematic literature review fully dedicated to satellite Remote Sensing-based approaches (RS) to monitor, track and model volcanic cloud monitoring, prediction, and forecasting methods.
The review was performed on academic papers on the Web of Science to find relevant scientific publications on volcanic cloud monitoring, published from January 1 st , 2010, to September 30 th , 2022. The search parameters used were keywords chosen based on the review topic. They were combined as follows: "Volcanic cloud" OR "Volcanic plume" OR "Volcanic Column" AND "Ash plume" OR "Ash cloud" OR "plume" AND "Remote Sensing" OR "Satellite" AND "Monitoring" AND "Eruptive Source Parameters" OR "SO₂ mass Flux" OR "SO₂ Flux". From this search, 84 papers were chosen, the selection was based on the use of satellites to detect and monitor volcanic clouds, model and forecast, and combining both approaches in order to estimate the eruptive source parameters. This work assesses the state of the art in satellite remote sensing across the globe to identify and comprehend the major gaps, constraints, and prospective advancements in the sensors, algorithms, and models.

How to cite: Mota, R., Gil, A., and Pacheco, J.: Detecting, monitoring and modeling volcanic clouds with EarthObservation (EO) satellites data: a systematic review, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17439, https://doi.org/10.5194/egusphere-egu23-17439, 2023.

Lahars are a type of volcanic hazard that can have devastating impacts on surrounding environments and communities. They are difficult to predict and study, making them particularly dangerous. In order to better understand the extent and potential impacts of lahars, this study utilizes digital simulations and GIS technology to model lahar activity at Mount Merapi in Indonesia and Mount Rainier in the United States.

The study employs the use of two computer codes, Titan2D and LAHARZ, to generate visual outputs for GIS systems. Titan2D is used to model near volcano hazards, such as pyroclastic density currents, using a Digital Elevation Model (DEM) of the volcano. These outputs are then remobilized as lahars and extended along valleys using LAHARZ, resulting in outputs that mimic real-life scenarios.

Both Mount Merapi and Mount Rainier are located near densely populated settlements and have the potential to generate lahars, indicating a possibility for significant hazards. Using a 30-metre DEM and varying parameters, this study simulates lahars of varying volumes ranging from 125,000 m³ to 16,000,000 m³ in order to identify the extent of the hazard in multiple scenarios.

Both Titan2D and LAHARZ have been tested individually by researchers in the past and have been found to accurately recreate past events. This study tests their combined use as a tool for producing hazard maps viewable in GIS, which can aid in hazard prediction and analysis. The resulting hazard maps for both Mount Merapi and Mount Rainier are found to be comparable to existing hazard maps for these volcanoes, suggesting that the combination of Titan2D and LAHARZ is an effective tool for hazard analysis.

How to cite: Chopra, T. and Cortés, J.: Modelling lahars at Merapi (Indonesia) and Rainier (USA) volcanoes using Titan2D and LAHARZ computer models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15787, https://doi.org/10.5194/egusphere-egu23-15787, 2023.

The classic definition of risk as the product of hazard, exposure and vulnerability was initially intended as a qualitative rather than quantitative formula. Its informal nature becomes more apparent when evaluating strategies for risk mitigation, for which different informal equations have been presented in the literature.

We offer here a mathematical perspective on the equations that define risk and a novel approach for a quantitative analysis of risk mitigation that help highlight decision variables among the many input variables that participate in risk analysis and modeling.

We show how the classic Risk = Hazard * Exposure * Vulnerability formula represents a zero-order approximation of the more formal integral representation that we derive, and that risk mitigation can be quantified based on parallels with mathematical homotopies with an associated cost function, with an interesting outlook on the design of both long-term and short-term risk mitigation strategies.

How to cite: Bilotta, G., Cappello, A., and Ganci, G.: A mathematical perspective on the formalization of risk mitigation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13528, https://doi.org/10.5194/egusphere-egu23-13528, 2023.

Satellite remote sensing data are suitable to monitor global scale volcanic hazards in an efficient and timely manner. The development of monitoring systems which automatically collect and process satellite data is crucial during a volcanic crisis. The huge amount of multispectral satellite data available requires new approaches capable of processing them automatically and artificial intelligence (AI) addresses these needs. Machine learning, a type of AI in which computers learn from data, is gaining importance in volcanology. The combination of ML algorithms and satellite remote sensing in volcano monitoring has the potential of analyzing global data in near real-time for mapping and monitoring purposes. Here, an AI-based platform was developed to monitor in near real-time the volcanic activity from space. AI algorithms are used to retrieve information about the ongoing volcanic activity. Under this perspective, a key role is played by ML since it overcomes the issues related to hard coded/explicit rules by implicitly learning them from historical satellite data. Volcanic eruptions are then fully characterized in terms of their energy release, e.g. volcanic radiative power (VRP), effusive rate, quantification of the erupted products, i.e. volume, spatial extension, volcanic cloud composition. This task is achieved by combining a variety of freely available satellite datasets, i.e. infrared (IR) data with different spatial, temporal and spectral features.  In particular, both a geostationary satellite sensor, i.e. SEVIRI (Spinning Enhanced Visible and InfraRed Imager, on board Meteosat satellites), and several mid-high spatial resolution polar satellite sensors, e.g. MODIS (Moderate Resolution Imaging Spectroradiometer, on board Terra and Aqua satellites), VIIRS (Visible Infrared Imaging Radiometer Suite, on board the Suomi-NPP and NOAA-20 satellites), SLSTR (Sea and Land Surface Temperature Radiometer, on board Sentinel-3A and Sentinel-3B satellites), MSI (MultiSpectral Instrument, on board Sentinel-2), are adopted. We demonstrate the potential of this web-based satellite-data-driven platform during the recent eruptive events on Stromboli and Etna. 

How to cite: Del Negro, C., Amato, E., Cariello, S., Corradino, C., Torrisi, F., and Zago, V.: An Artificial Intelligence-based platform for volcanic hazard monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15945, https://doi.org/10.5194/egusphere-egu23-15945, 2023.