Eruption Source Parameters Research Articles

Reconstructing fallout features and dispersal of Cretaio Tephra (Ischia Island, Italy) through field data analysis and numerical modelling: Implications for hazard assessment

The magnitude and intensity of the 60 CE Cretaio Tephra, the largest historical explosive eruption at Ischia caldera (Italy), was studied integrating field data and numerical modelling in order to reconstruct the dispersal of the fallout related to the climax phase of the eruption and characterize its physical parameters.A field survey of the main fall unit (EUC) of Cretaio Tephra and its sampling was performed on the island, which is the proximal area of dispersal, and for the first time outcrops of this tephra were found southward on the Capri Island. The dataset is completed by a distal tephra sampled in the offshore and described in the literature, allowing the analysis of samples of intermediate and distal deposits. Grain size analysis of the samples collected on the island show bimodal distributions due to the presence of a ballistic component, especially in the most proximal sampling sites around the inferred and buried vent. Tails of fine ash in the distributions can be associated with settling enhanced by ash aggregation processes in the moisture-rich plume. Tephra dispersal was reconstructed using the HAZMAP tephra dispersal model by minimizing the difference between the simulations obtained exploring plausible ranges of eruption source parameters and the available thickness and grain-size measurements. Results show that the dispersal axis of EUC is to the south-southeast, the best guess for the total erupted volume of tephra is 0.075 km3, the plume height ranges between 5 and 13 km, for an average mass eruption rate (MER) of 105 kg/s, hence a duration of the eruption of a few days. Considering an eruptive scenario given by the eruption parameters and diffusion coefficient of the Cretaio Tephra, together with a statistical set of wind profiles, HAZMAP modelling allowed us to generate tephra loading probability maps, needed to assess the impact of such an eruption at Ischia Island and the Neapolitan metropolitan area, pivotal for civil protection purposes.Another important outcome of the study is that, for volcanic islands, the reconstruction of eruption parameters may benefit much more from the search of even few medial and distal offshore outcrops than from implementing the on-island proximal dataset only.

Eruption type probability and eruption source parameters at Cotopaxi and Guagua Pichincha volcanoes (Ecuador) with uncertainty quantification

Future occurrence of explosive eruptive activity at Cotopaxi and Guagua Pichincha volcanoes, Ecuador, is assessed probabilistically, utilizing expert elicitation. Eight eruption types were considered for each volcano. Type event probabilities were evaluated for the next eruption at each volcano and for at least one of each type within the next 100 years. For each type, we elicited relevant eruption source parameters (duration, average plume height, and total tephra mass). We investigated the robustness of these elicited evaluations by deriving probability uncertainties using three expert scoring methods. For Cotopaxi, we considered both rhyolitic and andesitic magmas. Elicitation findings indicate that the most probable next eruption type is an andesitic hydrovolcanic/ash-emission (~ 26–44% median probability), which has also the highest median probability of recurring over the next 100 years. However, for the next eruption at Cotopaxi, the average joint probabilities for sub-Plinian or Plinian type eruption is of order 30–40%—a significant chance of a violent explosive event. It is inferred that any Cotopaxi rhyolitic eruption could involve a longer duration and greater erupted mass than an andesitic event, likely producing a prolonged emergency. For Guagua Pichincha, future eruption types are expected to be andesitic/dacitic, and a vulcanian event is judged most probable for the next eruption (median probability ~40–55%); this type is expected to be most frequent over the next 100 years, too. However, there is a substantial probability (possibly >40% in average) that the next eruption could be sub-Plinian or Plinian, with all that implies for hazard levels.

Tephra deposit inversion by coupling Tephra2 with the Metropolis-Hastings algorithm: algorithm introduction and demonstration with synthetic datasets

In this work we couple the Metropolis-Hastings algorithm with the volcanic ash transport model Tephra2, and present the coupled algorithm as a new method to estimate the Eruption Source Parameters of volcanic eruptions based on mass per unit area or thickness measurements of tephra fall deposits. Outputs of the algorithm are presented as sample posterior distributions for variables of interest. Basic elements in the algorithm and how to implement it are introduced. Experiments are done with synthetic datasets. These experiments are designed to demonstrate that the algorithm works from different perspectives, and to show how inputs affect its performance. Advantages of the algorithm are that it has the ability to i) incorporate prior knowledge; ii) quantify the uncertainty; iii) capture correlations between variables of interest in the estimated Eruption Source Parameters; and iv) no simplification is assumed in sampling from the posterior probability distribution. A limitation is that some of the inputs need to be specified subjectively, which is designed intentionally such that the full capacity of the Bayes’ rule can be explored by users. How and why inputs of the algorithm affect its performance and how to specify them properly are explained and listed. Correlation between variables of interest in the posterior distributions exists in many of our experiments. They can be well-explained by the physics of tephra transport. We point out that in tephra deposit inversion, caution is needed in attempting to estimate Eruption Source Parameters and wind direction and speed at each elevation level, because this could be unnecessary or would increase the number of variables to be estimated, and these variables could be highly correlated. The algorithm is applied to a mass per unit area dataset of the tephra deposit from the 2011 Kirishima-Shinmoedake eruption. Simulation results from Tephra2 using posterior means from the algorithm are consistent with field observations, suggesting that this approach reliably reconstructs Eruption Source Parameters and wind conditions from deposits.

Near-real-time volcanic cloud monitoring: insights into global explosive volcanic eruptive activity through analysis of Volcanic Ash Advisories

Understanding the location, intensity, and likely duration of volcanic hazards is key to reducing risk from volcanic eruptions. Here, we use a novel near-real-time dataset comprising Volcanic Ash Advisories (VAAs) issued over 10 years to investigate global rates and durations of explosive volcanic activity. The VAAs were collected from the nine Volcanic Ash Advisory Centres (VAACs) worldwide. Information extracted allowed analysis of the frequency and type of explosive behaviour, including analysis of key eruption source parameters (ESPs) such as volcanic cloud height and duration. The results reflect changes in the VAA reporting process, data sources, and volcanic activity through time. The data show an increase in the number of VAAs issued since 2015 that cannot be directly correlated to an increase in volcanic activity. Instead, many represent increased observations, including improved capability to detect low- to mid-level volcanic clouds (FL101–FL200, 3–6 km asl), by higher temporal, spatial, and spectral resolution satellite sensors. Comparison of ESP data extracted from the VAAs with the Mastin et al. (J Volcanol Geotherm Res 186:10–21, 2009a) database shows that traditional assumptions used in the classification of volcanoes could be much simplified for operational use. The analysis highlights the VAA data as an exceptional resource documenting global volcanic activity on timescales that complement more widely used eruption datasets.

The radius of the umbrella cloud helps characterize large explosive volcanic eruptions

Eruption source parameters (in particular erupted volume and column height) are used by volcanologists to inform volcanic hazard assessments and to classify explosive volcanic eruptions. Estimations of source parameters are associated with large uncertainties due to various factors, including complex tephra sedimentation patterns from gravitationally spreading umbrella clouds. We modify an advection-diffusion model to investigate this effect. Using this model, source parameters for the climactic phase of the 2450 BP eruption of Pululagua, Ecuador, are different with respect to previous estimates (erupted mass: 1.5–5 × 1011 kg, umbrella cloud radius: 10–14 km, plume height: 20–30 km). We suggest large explosive eruptions are better classified by volume and umbrella cloud radius instead of volume or column height alone. Volume and umbrella cloud radius can be successfully estimated from deposit data using one numerical model when direct observations (e.g., satellite images) are not available.

Modelling pyroclastic density currents from a subplinian eruption at La Soufri\xe8re de Guadeloupe (West Indies, France)

We have used a three-dimensional, non-equilibrium multiphase flow numerical model to simulate subplinian eruption scenarios at La Soufrière de Guadeloupe (Lesser Antilles, France). Initial and boundary conditions for computer simulations were set on the basis of independent estimates of eruption source parameters (i.e. mass eruption rate, volatile content, temperature, grain size distribution) from a field reconstruction of the 1530 CE subplinian eruption. This event is here taken as a reference scenario for hazard assessment at La Soufrière de Guadeloupe. A parametric study on eruption source parameters allowed us to quantify their influence on the simulated dynamics and, in particular, the increase of the percentage of column collapse and pyroclastic density current (PDC) intensity, at constant mass eruption rate, with variable vent diameter. Numerical results enabled us to quantify the effects of the proximal morphology on distributing the collapsing mass around the volcano and into deep and long valleys and to estimate the areas invaded by PDCs, their associated temperature and dynamic pressure. Significant impact (temperature > 300 °C and dynamic pressure > 1 kPa) in the inhabited region around the volcano is expected for fully collapsing conditions and mass eruption rates > 2 × 107 kg/s. We thus combine this spatial distribution of temperature and dynamic pressure with an objective consideration of model-related uncertainty to produce preliminary PDC hazard maps for the reference scenario. In such a representation, we identify three areas of varying degree of susceptibility to invasion by PDCs—very likely to be invaded (and highly impacted), susceptible to invasion (and moderately impacted), and unlikely to be invaded (or marginally impacted). The study also raises some key questions about the use of deterministic scenario simulations for hazard assessment, where probability distributions and uncertainties are difficult to estimate. Use of high-performance computing techniques will in part allow us to overcome such difficulties, but the problem remains open in a scientific context where validation of numerical models is still, necessarily, an incomplete and ongoing process. Nevertheless, our findings provide an important contribution to the quantitative assessment of volcanic hazard and risk at La Soufrière de Guadeloupe particularly in the context of the current unrest of the volcano and the need to prepare for a possible future reawakening of the volcano that could culminate in a magmatic explosive eruption.

Large Variations in Volcanic Aerosol Forcing Efficiency Due to Eruption Source Parameters and Rapid Adjustments

AbstractThe relationship between volcanic stratospheric aerosol optical depth (SAOD) and volcanic radiative forcing is key for quantifying volcanic climate impacts. In their Fifth Assessment Report, the Intergovernmental Panel on Climate Change used one scaling factor between volcanic SAOD and volcanic forcing based on climate model simulations of the 1991 Mt. Pinatubo eruption, which may not be appropriate for all eruptions. Using a large ensemble of aerosol‐chemistry‐climate simulations of eruptions with different sulfur dioxide emissions, latitudes, emission altitudes, and seasons, we find that the effective radiative forcing (ERF) is on average 20% less than the instantaneous radiative forcing, predominantly due to a positive shortwave cloud adjustment. In our model, the volcanic SAOD‐ERF relationship is non‐unique and varies widely depending on time since an eruption, eruption latitude, and season due to differences in aerosol dispersion and incoming solar radiation. Our revised SAOD‐ERF relationships suggest that volcanic forcing has been previously overestimated.

A real-time tephra fallout rate model by a small-compact X-band Multi-Parameter radar

Real-time monitoring of volcanic tephra fallout rate is an important factor to predict ash plume dispersion and to mitigate risk to air traffic. Ground-based weather radar has been one of the fundamental instruments to detect the plume and derive eruptive source parameters, such as the tephra fallout rate. The current work presents the use of two small and compact X-band Multi-Parameter (X-MP) radars for a new tephra fallout rate model development and the technical aspects of the system in Sinabung and Merapi Volcanoes. The new model estimates the tephra fallout rate using two radar parameters: the specific differential phase shift parameter and the reflectivity intensity factor. Total cumulated mass estimated from the radar-based tephra fallout rate model from the radar is compared with the plume height model and an empirical radar-based model. A volcanic eruptive index (VEI)-2 of Sinabung generated a plume exceeding 15 km, resulting in a maximum tephra fallout rate of 0.58 kg m−2 h−1 and a total tephra mass of 51 × 106 kg. The VEI 1 of Sinabung caused a plume height of 2.5 km, resulting in a maximum tephra fallout rate of 0.3 kg m−2 h−1 and a total cumulated tephra of 9 × 106 kg. In the last case, a VEI 1 eruption of Mt. Merapi produces a 6 km plume, resulting in a maximum tephra fallout rate of 0.28 kg m−2 h−1 and a total cumulated tephra of 35 × 106 kg. The sector range height indicator scan-mode strategy in the VEI 2 eruption of Mt. Sinabung ran at six degrees azimuth angles capturing only a partial volume of the plume. Thus, the total mass was only 22% of the result from the empirical plume height model, even though the plume height was assumed to be equally the same with the maximum height scanned of radar at 7 km. In contrast, the volumetric scan by a plan position indicator strategy gave a total cumulated tephra mass, that matches better to the result of the empirical plume height model at 65–92%. Based on these results and the ability of the X-MP radar to capture the volcanic plume at the same reported onset time, we can confirm the importance of an X-MP radar for real-time tephra fallout monitoring during an eruption.

The Impact of Eruption Source Parameter Uncertainties on Ash Dispersion Forecasts During Explosive Volcanic Eruptions

AbstractVolcanic ash in the atmosphere is a hazard to aviation. To predict which areas of airspace are most likely to be affected by the presence of ash, Volcanic Ash Advisory Centers (VAACs) use observations and atmospheric dispersion models. These models are initialized with, among other parameters, a mass eruption rate (MER), which quantifies the emission rate into the atmosphere at the source. This influences the predicted spatial–temporal evolution and concentration of the ash cloud. Different models are available to estimate MER from the volcanic plume height and some models also include the weather conditions (e.g., wind speed). The REFIR software tool uses time‐series of plume height estimated from observations and weather data to provide estimates of MER through time. Here we present an updated version of REFIR that can now be used also to calculate MER for past eruptions and produce output parameters in a format suitable for use with the NAME dispersion model (UK Met Office—London VAAC). We also investigate how uncertainty in input parameters is propagated through to dispersion model output. Our results show that a +/−1 km uncertainty on a 6 km high plume can result in the affected area ranging by a factor of three between the minimum and maximum estimates. Additionally, we show that using wind‐affected plume models results in affected areas that are five times larger than using no‐wind‐affected models. This demonstrates the sensitivity of MER to the type of plume model chosen (no‐wind‐ vs. wind‐affected).

Isomass and Probability Maps of Ash Fallout Due to Vulcanian Eruptions at Tungurahua Volcano (Ecuador) Deduced from Historical Forecasting

Since April of 2015, the ash dispersion and ash fallout due to Vulcanian eruptions at Tungurahua, one of the most active volcanoes in Ecuador, have been forecasted daily. For this purpose, our forecasting system uses the meteorological Weather Research and Forecasting (WRF) and the FALL3D models. Previously, and based on field data, laboratory, and numerical studies, corresponding eruption source parameters (ESP) have been defined. We analyzed the historically forecasted results of the ash fallout quantities over four years (April 2015 to March 2019), in order to obtain the average isomass and probability maps for three-month periods: February–March–April (FMA), May–June–July (MJJ), August–September–October (ASO), and November–December–January (NDJ). Our results indicate similar ash fallout shapes during MJJ and ASO, with a clear and defined tendency toward the west of the volcano; this tendency is less defined during NDJ and FMA. The proximal region west of the volcano (about 100 km to the west) has the highest probability (>70%) of being affected by ash fallout. The distant region to the west (more than 100 km west) presented low to medium probabilities (10%–70%) of ash fallout. The cities of Guaranda (W, 60% to 90%), Riobamba (SW, 70%), and Ambato (NW, 50% to 60%) have the highest probabilities of being affected by ash fallout. Among the large Ecuadorian cities, Guayaquil (SW, 10% to 30%) has low probability, and Quito (N, ≤5%) and Cuenca (SSE, <5%) have very low probabilities of being affected by ash fallout. High ash clouds can move in different directions, compared to wind transport near the surface. Therefore, it is possible to detect ash clouds by remote sensing which, in Ecuador, is limited to the layers over the meteorological clouds, which move in a different direction than low wind; the latter produces ash fallout over regions in different directions compared to the detected ash clouds. In addition to the isomass/probability maps and detected ash clouds, forecasting is permanently required in Ecuador.

Operational Response to Volcanic Ash Risks Using HOTVOLC Satellite-Based System and MOCAGE-Accident Model at the Toulouse VAAC

In 2010, the Eyjafjallajökull volcano erupted, generating an ash cloud causing unprecedented disruption of European airspace. Despite an exceptional situation, both the London and Toulouse Volcanic Ash Advisory Centres (VAAC) provided critical information on the location of the cloud and on the concentration of ash, thus contributing to the crisis management. Since then, substantial efforts have been carried out by the scientific community in order to improve remote sensing techniques and numerical modeling. Satellite instruments have proven to be particularly relevant for the characterization of ash cloud properties and a great help in the operational management of volcanic risk. In this study, we present the satellite-based system HOTVOLC developed at the Observatoire de Physique du Globe de Clermont-Ferrand (OPGC) using Meteosat geostationary satellite and designed for real-time monitoring of active volcanoes. After a brief presentation of the system we provide details on newly developed satellite products dedicated to the ash cloud characterization. This includes, in particular, ash cloud altitude and vertical column densities (VCD). Then, from the Stromboli 2018 paroxysm, we show how HOTVOLC can be used in a timely manner to assist the Toulouse VAAC in the operational management of the eruptive crisis. In the second part of the study, we provide parametric tests of the MOCAGE-Accident model run by the Toulouse VAAC from the April 17 Eyjafjallajökull eruption. For this purpose, we tested a range of eruption source parameters including the Total Grain Size Distribution (TGSD), the eruptive column profile, the top plume height and mass eruption rate (MER), as well as the fine ash partitioning. Finally, we make a comparison on this case study between HOTVOLC and MOCAGE-Accident VCD.

Model sensitivities in the case of high-resolution Eulerian simulations of local tephra transport and deposition

Tephra transport modelling has predominantly been carried out over synoptic-scale resolutions, resolving only fundamental atmospheric circulations. Consequently, model sensitivity studies have focused on these resolutions. In recent years, improved eruption source parameter (ESP) estimation methods and computational efficiency have progressively led to the use of higher resolutions to increase the fidelity of tephra transport simulations. This is a new computational regime whose model sensitivities remain largely unexplored. Here, high-resolution simulations using a 300 m horizontal grid spacing were carried out using WRF and FALL3D to assess sensitivity to: meteorological input data (using both reanalysis and forecast data), boundary layer parametrisation, plume model, settling velocity, total grain-size distribution (TGSD), and aggregation, leading to a total of 1280 possible configurations. An eruption that occurred on 28 July 2019 at Sakurajima volcano, Japan, is used as a case study, as the northwards dispersal of a 3.8 km-high plume led to the temporary closure of Kagoshima airport. Sedimentation was observed at three optical disdrometers on the volcano, providing insight on the deposition timing and particle size distribution. Results reveal that meteorological input data can have a significant impact over the fidelity of the simulations, with errors changing over one order of magnitude depending on the initialisation dataset. This was found to be the result of local bias in the simulated wind fields over the region of dispersal, which was not represented in the domain-wide assessment of the WRF simulations. Ensemble simulations using different meteorological input data were found to mitigate this, significantly increasing the fidelity of the final ensemble product. Use of a plume model and the correct estimation of the TGSD were seen to significantly improve results. Finally, even with detailed data to inform the choice, use of an empirical aggregation scheme was seen to significantly degrade the accuracy of the simulations.

Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud

It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused unprecedented disruption to air traffic across Europe. During this event, the London Volcanic Ash Advisory Centre (VAAC) provided advice and guidance on the expected location of volcanic ash in the atmosphere using observations and the atmospheric dispersion model NAME (Numerical Atmospheric-Dispersion Modelling Environment). Rapid changes in regulatory response and procedures during the eruption introduced the requirement to also provide forecasts of ash concentrations, representing a step-change in the level of interrogation of the dispersion model output. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. We present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned. Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep moist atmospheric convection and parametrizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Finally, upper air wind field data used by the dispersion model is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event.

Automated detection and measurement of volcanic cloud growth: towards a robust estimate of mass flux, mass loading and eruption duration

Identifying the spatial extent of volcanic ash clouds in the atmosphere and forecasting their direction and speed of movement has important implications for the safety of the aviation industry, community preparedness and disaster response at ground level. Nine regional Volcanic Ash Advisory Centres were established worldwide to detect, track and forecast the movement of volcanic ash clouds and provide advice to en route aircraft and other aviation assets potentially exposed to the hazards of volcanic ash. In the absence of timely ground observations, an ability to promptly detect the presence and distribution of volcanic ash generated by an eruption and predict the spatial and temporal dispersion of the resulting volcanic cloud is critical. This process relies greatly on the heavily manual task of monitoring remotely sensed satellite imagery and estimating the eruption source parameters (e.g. mass loading and plume height) needed to run dispersion models. An approach for automating the quick and efficient processing of next generation satellite imagery (big data) as it is generated, for the presence of volcanic clouds, without any constraint on the meteorological conditions, (i.e. obscuration by meteorological cloud) would be an asset to efforts in this space. An automated statistics and physics-based algorithm, the Automated Probabilistic Eruption Surveillance algorithm is presented here for auto-detecting volcanic clouds in satellite imagery and distinguishing them from meteorological cloud in near real time. Coupled with a gravity current model of early cloud growth, which uses the area of the volcanic cloud as the basis for mass measurements, the mass flux of particles into the volcanic cloud is estimated as a function of time, thus quantitatively characterising the evolution of the eruption, and allowing for rapid estimation of source parameters used in volcanic ash transport and dispersion models.

Volcanic ash forecast using ensemble-based data assimilation: an ensemble transform Kalman filter coupled with the FALL3D-7.2 model (ETKF–FALL3D version 1.0)

Abstract. Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification (e.g., the eruption strength or vertical distribution of the emitted particles), with consequent implications for an operational ash impact assessment. We present an ensemble-based data assimilation and forecast system for volcanic ash dispersal and deposition aimed at reducing uncertainties related to eruption source parameters. The FALL3D atmospheric dispersal model is coupled with the ensemble transform Kalman filter (ETKF) data assimilation technique by combining ash mass loading observations with ash dispersal simulations in order to obtain a better joint estimation of the 3-D ash concentration and source parameters. The ETKF–FALL3D data assimilation system is evaluated by performing observing system simulation experiments (OSSEs) in which synthetic observations of fine ash mass loadings are assimilated. The evaluation of the ETKF–FALL3D system, considering reference states of steady and time-varying eruption source parameters, shows that the assimilation process gives both better estimations of ash concentration and time-dependent optimized values of eruption source parameters. The joint estimation of concentrations and source parameters leads to a better analysis and forecast of the 3-D ash concentrations. The results show the potential of the methodology to improve volcanic ash cloud forecasts in operational contexts.

New insights into eruption source parameters of the 1600 CE Huaynaputina Plinian eruption, Peru

In the Central Andes, large Plinian eruptions (Volcanic Explosivity Index ≥ 5) occur at a relatively high frequency, i.e. average one every 2000 to 4000 years over the past 50,000 years in Peru. Such recurring explosive activity represents a significant challenge for regions typically hosting several million people (e.g. Southern Peru, Western Bolivia and Northern Chile). With VEI 6, the 1600 CE Huaynaputina eruption is considered the largest historical eruption in South America. We have re-examined the first Plinian phase of this eruption in order to better assess critical eruption source parameters (i.e. erupted volume, plume height, mass eruption rate, eruption duration).The revised bulk volume of the tephra-fall deposit associated with the Plinian phase is approximately 13–14 km3, almost twice the previous estimate (7–8 km3 within the 1 cm isopach) based on methods including power law, Weibull function and Bayesian linear regression. Tephra was dispersed by strong winds to the WNW as far as 400 km on Peruvian territory and then in the Pacific Ocean. Seven villages were buried, killing ~ 1500 people. The revised plume height estimate, 32.2 ± 2.5 km, is consistent with the early estimations. As a result, the Huaynaputina 1600 CE first eruption phase lies in the upper part of the Plinian field close to the ultra-Plinian transition, making this event one of the largest in the past millennium which coincides with results from recent studies on palaeoclimatic impacts.

Characterizing magma fragmentation and its relationship with eruptive styles of Somma-Vesuvius volcano (Naples, Italy)

Among the active volcanoes worldwide, Somma-Vesuvius is one with the highest volcanic risk as the surrounding areas are highly populated. Somma-Vesuvius is quiescent since 1944, but geological and historical records reveal a frequent violent explosive activity in the last 4000 years, representing a severe risk for the ∼700,000 inhabitants currently living in the area having a high probability for being impacted by pyroclastic density currents (“red zone”) and more than one million people who can be potentially affected by tephra fallout. This study aims to analyze the distribution and grain-size of tephra fallout deposits from several Somma-Vesuvius eruptions of different styles, ranging from Violent Strombolian to sub-Plinian and Plinian, for characterizing the associated magmatic fragmentation through the assessment of their total grain-size distribution (TGSD). Chronologically, we focus on the Avellino (4365 BP) and Pompeii (A.D. 79) Plinian eruptions, the Pollena (A.D. 472) sub-Plinian eruption, and the 1906 and 1944 Violent Strombolian eruptions. The related TGSDs were estimated by means of the Voronoi tessellation method, which requires a suitable number of local grain-size distributions and estimation of the area of minimum tephra loading (zero-line contour). However, field-derived TGSDs can be biased towards the coarse and fine grain-size populations due to the typical paucity of available field outcrops of fallout deposits. To encompass this issue, we performed a sensitivity study on the assumption behind TGSD reconstructions and described TGSDs through analytical grain-size distributions that best fit the field TGSDs. Our main objective is a more robust estimation of the TGSDs associated with the different eruptive styles, which is crucial, together with the other eruption source parameters, for robustly predicting tephra loading and airborne ash dispersal of future eruptions at Somma-Vesuvius.

Unravelling the links between seismo-acoustic signals and eruptive parameters: Etna lava fountain case study

Deriving eruption source parameters from geophysical data is critical for volcano hazard mitigation, yet remains a challenging task in most volcanoes worldwide. In this work, we explored the temporal relationship between geophysical signals and eruptive parameters measured during six explosive episodes from the New South-East Crater of Mt. Etna (Italy). The quadratic reduced seismic velocity and pressure were calculated to track the temporal variation of volcanic elastic radiation, and the lava fountain height was estimated by thermal camera image processing. The temporal relationships between these geophysical and eruptive time series were studied. In particular, the first considered lava fountain exhibited a “clockwise hysteresis” pattern: higher seismic amplitude with respect to the fountain height during the waxing phase as compared to the waning phase. We also calculated the regression parameters for both linear and power laws, linking seismo-acoustic and eruptive time series. For the linear regressions, we found fairly constant values of the scaling factors in five out of six eruptive episodes, which can be considered as a promising step to derive eruption source parameters from geophysical data in real-time. Regarding power law regressions, a clear relationship was observed between the exponents determined for the power law linking quadratic reduced velocity and lava fountain height, and the time interval duration from the previous eruption. These results suggest that the condition of the uppermost part of the plumbing system (e.g. viscosity of residing magma and conduit conditions) play a key role in the seismic energy generation during the eruptions.

Experimental High-Resolution Forecasting of Volcanic Ash Hazard at Sakurajima, Japan

A high-resolution forecast methodology for the ash hazard at Sakurajima volcano, Japan, is presented. The methodology employs a combined modeling approach and utilizes eruption source parameters estimated by geophysical observations from Sakurajima, allowing for a proactive approach in forecasting. The Weather Research and Forecasting (WRF) model is used to downscale Japan Meteorological Agency (JMA) forecast data over the area of interest. The high-resolution meteorological data are then used in FALL3D model to provide a forecast for the ash dispersal and deposition. The methodology is applied for an eruption that occurred on June 16, 2018. Disdrometer observations of ashfall are used along with ash dispersal modeling to inform the choice of the total grain size distribution (TGSD). A series of pseudo-forecast ash dispersal simulations are then carried out using the proposed methodology and estimated TGSD, initialized with meteorological forecast data released up to ∼13 hours before the eruption, with results showing surprising consistency up to ∼10 hours before the eruption. Using forecast data up to 4 hours before the eruption was seen to constrain observation to model ratios within a factor of 2–4 depending on the timing of simulation and location. A number of key future improvements for the methodology are also highlighted.

On the mobility of pyroclastic currents in light of deposit thickness and clast size trends

Pyroclastic currents (PCs) represent the most hazardous manifestations of explosive volcanic activity. The evaluation of their mobility, as related to the eruptive source parameters and the interactions with the surroundings along their path, is crucial for hazard assessment at active volcanoes in order to define the areas exposed to their impacts. Here we report on two case studies from the Latera Volcanic Complex (Vulsini Volcanic District, central Italy) that may approach the two end-members of the classical spectrum of PC dynamics in terms of particle concentration and dominant flow regime (i.e., dilute, turbulent vs. concentrated, laminar PCs). For the two examples, we document specific (and contrasting) variation trends of deposit thickness and maximum lithic and pumice clast sizes with distance from vent, which provide a further characterization in terms of forced vs. inertial flow behavior. In the first case, thickness and maximum lithic and pumice sizes show a mild, linear decrease with distance, implying a sort of balance between forced and inertial regimes. Flow pressure and mass progressively drop via deposition, along with flow competence. For given source conditions (i.e., mass eruption rate, fountaining height, erupting mixture, etc.) and slope angle of topography, the PC runout depends primarily on the waning density contrast between the flow and the surrounding atmosphere (density current), ending with the loft of the current. The second case, representative of moderate- to large-volume, dense PCs derived by continuous feeding with high mass discharge rate (i.e., from collapsing Plinian columns or caldera-forming events), shows an increase of deposit thickness with distance, which is accompanied by opposite lithic and pumice clast size-distance trends (i.e., decreasing lithic sizes and increasing pumice sizes downcurrent), compatible with high particle concentration and forced, non-turbulent, granular flow regime. By analogy with the mobility of dry debris flows, we suggest that the sliding component of transport, which prevails in proximal to intermediate settings, may account for the prevailing tendency of the PC to transport than to deposit, which leaves behind relatively thin sheets with coarse lithics, while the dominant spreading component downcurrent would result in increasing accumulation and the stacking of coarse pumice-rich flow lobes in more distal settings. The spreading component of transport would become more and more important with increasing material supply at PC source, resulting in additional runout. These findings cast new light on the first-order controlling factors of flow mobility for the two PC end-members, laying the groundwork for determining the most relevant PC hazard parameters, i.e. the maximum runout and the flow properties upon impact (dynamic pressure, burial potential, temperature).