Developing physical-mathematical models able to describe the evolution of eruptive phenomena is a key point in volcanology. In the case of high-risk phenomena, such as lava flows or ash dispersal, predicting their spatial and temporal evolution and determining the potentially affected areas is fundamental in supporting every action directed at mitigating the risk as well as for environmental planning. This session aims to address unresolved challenging questions related to complex geophysical flow modeling and simulation, gathering physical-mathematical models, numerical methods and field and satellite data analysis in order to: (i) expand knowledge of complex volcanic processes and their space-time dynamics; (ii) monitor and model volcanic phenomena; (iii) evaluate model robustness through validation against real case studies, analytical solutions and laboratory experiments; (iv) quantify the uncertainty propagation through both forward (sensitivity analyses) and inverse (optimization/calibration) modelling in all components of volcanic hazard modelling in response to eruptive crises.
vPICO presentations: Fri, 30 Apr
Volcanic ash poses a significant hazard for aviation. If an ash cloud forms as result of an eruption, it forces a series of flight planning decisions that consider important safety and economic factors. These decisions are made using a combination of satellite retrievals and volcanic ash forecasts issued by Volcanic Ash Advisory Centres. However, forecasts of ash hazard remain deterministic, and lack quantification of the uncertainty that arises from the estimation of eruption source parameters, meteorology and uncertainties within the dispersion model used to perform the simulations. Quantification of these uncertainties is fundamental and could be achieved by using ensemble simulations. Here, we explore how ensemble-based forecasts — performed using the Met Office dispersion model NAME — together with sequential satellite retrievals of ash column loading, may improve forecast accuracy and uncertainty characterization.
We have developed a new methodology to evaluate each member of the ensemble based on its agreement with the satellite retrievals available at the time. An initial ensemble is passed through a filter of verification metrics and compared with the first available set of satellite observations. Members far from the observations are rejected. The members within a limit of acceptability are used to resample the parameters used in the initial ensemble, and design a new ensemble to compare with the next available set of satellite observations. The filtering process and parameter resampling are applied whenever new satellite observations are available, to create new ensembles propagating forward in time, until all available observations are covered.
Although the method requires the run of many ensemble batches, and it is not yet suited for operational use, it shows how combining ensemble simulations and sequential satellite retrievals can be used to quantify confidence in ash forecasts. We demonstrate the method by applying it to the recent Raikoke (Kurii Islands, Russia) eruption, which occurred on the 22nd July 2019. Each ensemble consists of 1000 members and it is evaluated against 6-hourly HIMAWARI satellite ash retrievals.
How to cite: Capponi, A., Harvey, N. J., Dacre, H. F., Beven, K., and James, M. R.: Ensemble-based volcanic ash forecasts using satellite retrievals for quantitative verification, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3212, https://doi.org/10.5194/egusphere-egu21-3212, 2021.
Jan Mayen Island (Norway), located in the North Atlantic, is considered the world’s northernmost active subaerial volcano, with at least five eruptive periods recorded during the last 200 years. Explosive activity of the volcano may seriously affects the nearby important air traffic routes. However, no quantitative studies on the possible impact of a new explosive volcanic eruption on the air traffic have been conducted. In this work, we statistically characterise the spatial and temporal distribution of airborne volcanic ash cloud and its persistence at different flight levels. Since current operational forecast products do not always meet the requirements of the aviation sector and related stakeholders (using coarse time and space scales, with outputs on a 40 km horizontal resolution grid and 6 hour time averages), and they neglect epistemic/aleatory uncertainties in quantitative forecasts on real time, we propose hourly high resolution hazard maps over a 3D-grid covering a 2 km-resolution spatial domain 2000 km x 2000 km wide. We present the use of high-performance computing (HPC) to overcome the computational limitations associated with unbiased long-term probabilistic volcanic hazard assessment (PVHA) .Considering a continuum of possible combinations of Eruptive Source Parameters (ESP) to assess and quantify the uncertainty, and the natural variability associated with wind fields over 20 years of data, from 1999 to 2019, we run thousands of analytical solutions (numerical simulations) using the most recent version of the FALL3D model. As a result, the first comprehensive long-term PVHA for Jan Mayen volcanic island is presented.
How to cite: Titos, M., Martínez, B., Barsotti, S., Sandri, L., Folch, A., Mingari, L., Costa, A., and Macedonio, G.: Assessing potential impacts on the air traffic routes due to an ash-producing eruption on Jan Mayen Island (Norway), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7594, https://doi.org/10.5194/egusphere-egu21-7594, 2021.
Sangay volcano (2.00°S, 78.34°W, 5326 m asl), located at the southern end of the Northern Volcanic Zone of the Andes (Morona Santiago province, Ecuador), has frequently been referred as one of the most active volcanoes in the world. Its most recent eruptive period began on May 7, 2019 and is still ongoing. It is characterized by a semi-continuous viscous lava flow emission accompanied by frequent low magnitude explosions (Vasconez et al., this meeting). This eruptive episode is the first in more than two decades to produce significant impacts both locally and regionally, and reached its paroxysm on September 20, 2020 without clear precursory signals. The eruption started at 9:20 (UTC) and lasted about one and a half hours. The eruptive column rapidly split into a high-altitude (15 km asl) gas-rich cloud, drifting eastward at 5-8 m/s and a lower (12 km asl) ash-rich cloud, drifting westward at 10-14 m/s. The ash began to fall at 11:00 (UTC) in the communities near the volcano and reached the city of Guayaquil, the second largest city in Ecuador, at 13:00 (UTC), forcing the closure of the international airport.
In this work, we evaluate the ash dispersion simulations performed by the IG-EPN using the Ash3D model before, during and after the eruption using different eruptive source parameters (ESP), by comparison with the available satellite images (GOES-16). The simulated ash fallout for each set of ESP is compared to reports from the community and volcanic observers, as well as with a fallout map obtained from a four-days field trip initiated immediately after the eruption to ensure good quality of samples and measurements (September 20-23). Ash fallout was estimated using thickness measurements where possible and area density at 40 sites located between 30 and 180 km from the volcano. The grain size distribution of 35 samples was obtained by laser diffraction.
Our results show that the general westward direction and speed of the ash cloud in the simulations is coherent with the satellite images, except for the high-altitude, gas-rich cloud. However, large discrepancies were found when comparing the simulated and measured ash fallout. Field data shows that the first simulation using ESP based on the previous activity at Sangay, underestimated the eruption size, while the second simulation using the eruption column height estimated in near-real time overestimated it. As expected, the simulation carried out immediately after the eruption, based on the first field results shows the best correlation with field data, although there are still some second-order discrepancies. In particular, the plume axis was shifted about 12° northward in the simulation, which is attributed to the atmospheric model. We also noted that the deposition pattern was slightly different between the field data and the simulation. Grain size analysis reveals uni- to multimodal distributions, associated with complex eruptive dynamics and aggregation that probably influenced the sedimentation process. Further research is needed to better understand the eruptive dynamics at Sangay in order to improve forecasts.
How to cite: Bernard, B., Samaniego, P., and Encalada Simbaña, M.: Forecasting the dispersion and fallout of volcanic ash during a crisis: Assessment of the September 20, 2020 eruption at Sangay volcano in Ecuador, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13871, https://doi.org/10.5194/egusphere-egu21-13871, 2021.
This paper presents a numerical procedure for testing the effects of both static and dynamic loading of volcanic ash deposition on concrete roofs. The study aims to propose, a revision to the building regulations to make existing and future European buildings more resilient. The investigation uses a multi-physics simulation approach. Mathematical modelling is developed to investigate the volcanic ash effects in the context of the EN1991 code. A numerical modelling tool (EDEM software) for the Discrete Element Method (DEM) and structural analysis tool (ANSYS) for the Finite Element Method (FEM) is used to investigate 1 m x 1 m x 0.0154 m concrete slab plate subjected to pressure load considering the wind and no-wind effects. The modelled wind velocity was held constant at 0. 2 m/s. The density of the volcanic ash is low compared to natural systems but can be changed to reflect a range of relevant (measured) eruptive products. The key parameters and the results are illustrated as follows. With the initial results only, it is clear that our modelling technique has the potential to explore the loading effects of ash over a range of geological and environmental conditions during deposition.
The number of simulated volcanic particle loads is 80000, Volcanic ash particle density of 1000 (kg/m3). The simulated particle variables results for wind effects in the horizontal direction of (0.2 m/s) are as follows: The maximum pressures as 220042(Pa), the maximum deformation as 0.177 (mm) and the maximum was stress as 10.3 (MPa). The no wind effect (controlled condition) simulations particle variable results are as follows: the maximum pressures as 6411.3 (Pa), the maximum deformation as 0.061 (mm) and the maximum stress as 3.44 (MPa).
As expected, the wind effect resulted in an uneven distribution of the ash on the roof surface, which in turn produced areas of high-pressure load and stress levels. These results will have a possible impact on the designs of buildings on flat roof considerations. We aim to continue with further investigations to determine the stress impact and collapse failure due to loading over a wide range of relevant volcanic ash particle size compositions.
How to cite: Petford, N., Quainoo, P., and Kaczmarczyk, S.: Ashfall hazard: modelling volcanic ash roof loading and revisions to European Building Codes , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2309, https://doi.org/10.5194/egusphere-egu21-2309, 2021.
Large (VEI= 4-6) Quaternary explosive eruptions have repeatedly occurred in Armenia and the neighboring territories. Worth noting are the Plinian eruptions of Aragats stratovolcano (4096m), located in the vicinity of the Armenian capital city Yerevan (pop. >1 million) and producing lava flows variable in composition and size, pyroclastic density currents (PDCs) and fallout deposits (Connor et al., 2011; Gevorgyan et al., 2020). The youngest lavas from Aragats are 0.52 million years (myr) old and the youngest ignimbrites are 0.65 myr old. (Connor et al., 2011, Gevorgyan et al., 2020).
Here we present some features of a violent explosive Plinian eruption (VEI=4) from the relatively small, subsidiary Irind vent on the slopes of Aragats stratovolcano. We report results from newly mapped thick pumice fall deposits and pumice-rich welded lapilli-tuff and vitrophyres. Formation of up to ~10 m thick pumice fall deposits is related to a sustained Plinian eruption, while the formation of overlaying pumice tuffs (age= 0.490±0.028 M.yrs, Connor et al., 2011) and vitrophyre cover is interpreted as result of collapse of the eruption column due to a decrease of the magma supply.
Following the pyroclastic eruption, a voluminous (2.9-3.6 km3) effusive eruption of Irind created up to 120 m thick trachydacite lava flows that extended 18 km from the vent. Such long and thick lava flows are not typical for viscous felsic lavas. The Irind eruption products are characterized by a plagioclase-two pyroxene mineral association that is atypical for Aragats. The Irind magmas are trachydacitic (SiO2= 66 wt; MgO= 0.7 wt%) with high- K2O contents (5.2 wt%) and enrichments in U, Th, LILE and LREE compared to Aragats. Geothermobarometry and hygrometry based on detailed textural analysis and mineral chemistry (Cpx, Opx, plagioclase, glass) reveals that Irind magmas also have elevated H2O, increased alkalinity and high T (~970 °C)- all features capable to generate magmas with much lower viscosity (4.2–4.5 log η Pa·s) in respect to typical dacites.
Our results support the view that often small eruptive vents (Irind) on the slopes of large coeval stratovolcanoes (Aragats) are not necessarily tapping their voluminous magma mushes underneath and are capable to deliver independent Plinian eruptions. We speculate that these are triggered by intrusions of hot, volatile-rich, alkaline felsic magmas, presumably emplaced fast, similar to the Chaiten eruption in 2008, and did not mix well with the otherwise dominant and older magmatic system under Aragats.
Connor C., Connor L., Halama, R., Meliksetian, K., Savov, I., 2011. Volcanic Hazard Assessment of the Armenia Nuclear Power Plant Site, Final Report, 278 pp.
Gevorgyan, H., Breitkreuz, C., Meliksetian, K, et al., 2020. Quaternary ring plain- and valley-confined pyroclastic deposits of Aragats stratovolcano (Lesser Caucasus): Lithofacies, geochronology and eruption history, JVGR 401, 1-22.
How to cite: Meliksetian, K., Gevorgyan, H., Savov, I., Connor, C., Connor, L., Halama, R., Jrbashyan, R., Navasardyan, G., Grigoryan, E., and Ishizuka, O.: Plinian eruption of the Middle Pleistocene Irind volcano, Armenia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14206, https://doi.org/10.5194/egusphere-egu21-14206, 2021.
The quasi-biannual oscillation (QBO) dominates the equatorial zonal wind in the tropical stratosphere. Alternating easterly and westerly wind regimes form in the upper stratosphere and propagate downwards to the tropopause with a mean period of approximately 28 months. The westerly phase of the QBO is characterized by faster and more regular downward propagation, while the easterly phase has higher intensity (up to double the wind speed) and longer duration. Long-term lower stratospheric wind records indicate prevailing easterly winds (~60 % of the time) for the tropical regions. However, during westerly phases of the QBO, the wind is exclusively blowing towards the east. This leads to different but well predictable tephra distributions during the two phases. The QBO is effectively controlling the variations of the lower stratospheric wind regimes between 15º N and 15º S. Therefore, the effects of the QBO on spatial tephra distribution impact all tropical volcanic regions, including Central America, SE-Asia, the Andean Northern Volcanic Zone and the African Rift. We use the Tephra2 model in a case study from Tandikat volcano in West Sumatra to analyse the different QBO phases' effects on tephra distribution from Plinian eruptions. Incorporating the QBO in probabilistic hazard assessments for Plinian eruptions improves the accuracy of the hazard assessments. Understanding the effects of the QBO on the spatial tephra distribution will also help re-evaluate distal tephra records.
How to cite: Eisele, S., Qingyuan, Y., Bouvet de Maissoneuve, C., and Jenkins, S. F.: The Effects of the Quasi-Biannual Oscillation on Tephra Distribution from a Plinian Eruption, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9334, https://doi.org/10.5194/egusphere-egu21-9334, 2021.
Volcanic ashfall negatively affects crops, causing major economic losses and jeopardising the livelihood of farmers in developing countries where agriculture is at volcanic risk. Ash on plant foliage reduces the amount of incident light, thereby limiting photosynthesis and plant yield. An excessive ash load may also result in mechanical plant damages, such as defoliation and breakage of the stem and twigs. Characterising crop vulnerability to ashfall is critical to conduct a comprehensive volcanic risk analysis. This is normally done by describing the relationship between the ash deposit thickness and the corresponding reduction in crop yield, i.e. a fragility function. However, ash depth measured on the ground surface is a crude proxy of ash retention on plant foliage as this metrics neglects other factors, such as ash particle size, leaf pubescence and condition of humidity at leaf surfaces, which are likely to influence the amount of ash that stays on leaves.
Here we report the results of greenhouse experiments in which we measured the percentage of leaf surface area covered by ash particles for one hairy leaf plant (tomato, Solanum lycopersicum L.) and one hairless leaf plant (chilli pepper, Capsicum annuum L.) exposed to simulated ashfalls. We tested six particle size ranges (≤ 90, 90-125, 125-250, 250-500, 500-1000, 1000-2000 µm) and two conditions of humidity at leaf surfaces, i.e. dry and wet. Each treatment consisted of 15 replicates. The tomato and chilli pepper plants exposed to ash were at the seven- and eight-leaf stage, respectively. An ash load of ~570 g m-2 was applied to each plant using a homemade ashfall simulator. We estimated the leaf surface area covered by ash from pictures taken before and immediately after the simulated ashfall. The ImageJ software was used for image processing and analysis.
Our results show that leaf coverage by ash increases with decreasing particle size. Exposure of tomato and chilli pepper to ash ≤ 90 μm always led to ~90% coverage of the leaf surface area. For coarser particles sizes (i.e. between 125 and 500 µm) and dry condition at leaf surfaces, a significantly higher percentage (on average 29 and 16%) of the leaf surface area was covered by ash in the case of tomato compared to chilli pepper, highlighting the influence of leaf pubescence on ash retention. In addition, for particle sizes between 90 and 500 µm, wetting of the leaf surfaces prior to ashfall enhanced the ash cover by 19 ± 5% and 34 ± 11% for tomato and chilli pepper, respectively.
These findings highlight that ash deposit thickness alone cannot describe the hazard intensity accurately. A thin deposit of fine ash (≤ 90 µm) will likely cover the entire leaf surface area, thereby eliciting a disproportionate effect on plant foliage compared to a thicker but coarser deposit. Similarly, for a same ash depth, leaf pubescence and humid conditions at the leaf surfaces will enhance ash retention, thereby increasing the likelihood of damage. Our study will contribute to improve the reliability of crop fragility functions used in volcanic risk assessment.
How to cite: Ligot, N., Pereira, B., Bogaert, P., Lobet, G., and Delmelle, P.: Particle size, leaf pubescence and condition of humidity at leaf surfaces are key factors determining the retention of volcanic ash on crop foliage., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7833, https://doi.org/10.5194/egusphere-egu21-7833, 2021.
Understanding past eruption dynamics at a volcano is crucial for forecasting the range of possible future eruptions and their associated hazards and risk. In this work we reconstructed pyroclastic density currents and tephra fall from three eruptions at Gede volcano, Indonesia with the aim of gaining further insight into past eruptions and identifying suitable eruption source parameters for future hazard and risk assessment. Gede has the largest number of people living within 100 km of any volcano worldwide, and has exhibited recent unrest activity, yet little is known about its eruption history. For pyroclastic density currents, we used Titan2D to reconstruct geological deposits dated at 1200 and c. 1000 years BP. An objective and quantitative multi-criteria method was developed to evaluate the fit of over 300 pyroclastic density current (PDC) model simulations to field observations. We found that the 1200 years BP geological deposits could be reproduced with either a dome collapse or column collapse as the generation mechanism although a relatively low basal friction of 6 degrees would suggest that the PDCs were markedly mobile. Lower basal frictions may reflect the occurrence of previous PDCs that smoothed the path, reducing frictional resistance and enabling greater runout for the reconstructed unit. For the 1,000 years BP PDC, a column collapse mechanism and higher basal friction was required to fit the geological deposits. In agreement with previous studies, we found that Titan2D simulations were most sensitive to the basal friction; however, we also found that the internal friction – often fixed and considered of low influence on outputs - can have a moderate effect on the simulated average deposit thickness. We used Tephra2 to reconstruct historic observations of tephra dispersed to Jakarta and other towns during the last known magmatic eruption of Gede in 1948. In the absence of observable field deposits, or detailed information from the published literature, we stochastically sampled eruption source parameters from wide ranges informed by analogous volcanic systems. Our modelling suggests that the deposition of tephra in Jakarta during the November 1948 eruption was a very low probability event, with approximately a 0.03 % chance of occurrence. Through this work, we exemplify the reconstruction of past eruptions when faced with epistemic uncertainty, and improve our understanding of past eruption dynamics at Gede volcano, providing a crucial step towards the reduction of risk to nearby populations through volcanic hazard assessment.
How to cite: Tennant, E., Jenkins, S., Winson, A., Widiwijayanti, C., Gunawan, H., Haerani, N., Kartadinata, N., Banggur, W., and Triastuti, H.: The numerical reconstruction of three past eruptions at Gede volcano (Indonesia), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10575, https://doi.org/10.5194/egusphere-egu21-10575, 2021.
Pyroclastic density currents (PDCs) are hot, density-driven flows of gas, rock and ash generated during explosive volcanic eruptions, or from the collapse of lava domes (e.g. Fisher, 1979; Branney and Kokelaar, 2002; Cas et al. 2011). They pose a catastrophic geological hazard and have caused >90 000 deaths since 1600AD (Auker et al. 2013). Improved understanding of PDCs will enable us to better understand the explosive eruptions that generate them, improving our preparedness for future volcanic events. However, these deadly hazards are rarely observed up close and are difficult to analyse in real-time. To understand the flow dynamics of density currents we must use models and interpretations of their deposits (e.g. Smith N and Kokelaar, 2013; Rowley et al. 2014, Williams et al. 2014, Sulpizio et al. 2014; Lube et al. 2019, Smith G 2018, 2020).
The deposits of pyroclastic density currents, known as ‘ignimbrites’ can reveal important clues about how these deadly volcanic hazards behave in time and space Reverse grading in an ignimbrite can be interpreted in different ways (Branney & Kokelaar, 2002). It could record a growing eruption intensity through time - where increasingly larger clasts are introduced into the pyroclastic density current. Alternatively, it could record Kinematic sorting (the ‘muesli effect’) and transport processes within the current where larger particles became increasingly likely to be deposited as the current wanes (Palladino & Valentine,1995). The link between current dynamics and reverse grading is currently untested in aerated granular currents.
This project seeks to investigate the relationship between current dynamics and deposit architecture, specifically by considering granular sorting mechanisms in unidirectional flow. We will use an analogue flume (following methods in Rowley et. al., 2014, and Smith G et al., 2018, 2020) to explore how reverse grading and lateral grading may be related to changes in grain sizes at source versus kinematic sorting processes. A mix of grain sizes will be incorporated into the current via a hopper which allows for the starting composition of the current to be varied e.g. homogenous mix versus layered. Photographs of the deposit will be taken through the transparent sidewall of the flume and analysed using image analysis software. These experiments will be complimented by static tests of kinematic sorting, where a Perspex column will be sliced to reveal internal 3d architecture. This project will contribute to our understanding of lithofacies architecture in the field, and help to quantity how we interpret the sedimentation of ignimbrites.
Auker et al. (2013) https://doi.org/10.1186/2191-5040-2-2
Branney and Kokelaar (2002) https://doi.org/10.1144/GSL.MEM.2003.027
Cas et al. (2011) Bulletin of Volcanology 731583 https://doi.org/10.1007/s00445-011-0564-y
Fisher (1979) https://doi.org/10.1016/0377- 0273(79)90008-8
Lube et al. (2019) https://doi.org/10.1038/s41561-019-0338-2
Palladino & Valentine (1995). https://doi.org/10.1016/0377-0273(95)00036-4
Rowley et al. (2014) https://doi.org/10.1007/s00445-014-0855-1
Smith N. and Kokelaar (2013) https://doi.org/10.1007/s00445-013-0768-4
Smith G. et al. (2018) https://doi.org/10.1007/s00445-018-1241-1
Smith, G. et al. (2020). https://doi.org/10.1038/s41467-020-16657-z
How to cite: Johnson, M., Dowey, N., Williams, R., and Rowley, P.: The muesli effect in pyroclastic density currents - what does reverse grading in an ignimbrite mean? , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16402, https://doi.org/10.5194/egusphere-egu21-16402, 2021.
Piton de la Fournaise, situated on La Réunion Island (France), is one of the most active hot spot basaltic shield volcanoes worldwide, experiencing at least two eruptions per year since the establishment of the observatory in 1979. Eruptions are typically fissure-fed and form extensive lava flow fields. About 95 % of some ~250 historical events (since the first confidently dated eruption in 1708) have occurred inside an uninhabited horse-shoe shaped caldera (hereafter referred to as the Enclos) which is open to the ocean on its eastern side. Rarely (12 times since the 18th century), fissures have opened outside of the Enclos where housing units, population centers and infrastructure are at risk. In such a situation, lava flow hazard maps are a useful way of visualizing lava flow inundation probabilities over large areas. Here, we present a lava flow hazard map for Piton de la Fournaise volcano based on: i) vent distribution, ii) statistics of lava flow lengths, iii) lava flow recurrence times, and iv) simulations of lava flow paths across multi-temporal (i.e., regularly updated) topography using the DOWNFLOW stochastic numerical model. A map of the entire volcano highlights that the most probable (up to 12 %) location for future lava flow inundation is within the Enclos, where about 100,000 visitors are present each year. Hazard distribution changes throughout the analysis period due to the high frequency of eruptions that constantly modifies the vent opening distribution as well as the topography and the lava flow dimensional characteristics. Outside of the Enclos, probabilities reach 0.5 % along the well-defined rift zones and, although hazard occurrence in inhabited areas is deemed to be very low (<0.1 %), it may be underestimated here, as our study is only based on post-18th century records and neglects cycles of activity at the volcano. Specific hazard maps considering different event scenarios (i.e., events fed by different combinations of temporally evolving superficial and deep sources) are required to better assess affected areas in the future – especially by atypical, but potentially extremely hazardous, large volume eruptions. At such an active site, our method supports the need for regular updates of DEMs and associated lava flow hazard maps if we are to be effective in mitigating the associated risks.
How to cite: Chevrel, O., Favalli, M., Nicolas, V., Harris, A., Fornaciai, A., Richter, N., Derrien, A., Boissier, P., Di Muro, A., and Peltier, A.: Lava flow hazard map of Piton de la Fournaise volcano , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12266, https://doi.org/10.5194/egusphere-egu21-12266, 2021.
An active phase of Soufrière Hills Volcano (Montserrat, Lesser Antilles) has started in 1995 and had its most intense period between 1995 and 2010, when phases of lava dome growth were interrupted by dome collapses triggering ash clouds and different types of pyroclastic flows. These flows were released in various directions, so that two thirds of the island were left in an inhabitable state. The material deposited was later remobilized through lahar flows, burying the centre of the former capital town of Plymouth. In the present work, we attempt to back-calculate the sequences of dome growth – pyroclastic flows, and the subsequent lahar flows, in an integrated way, using the mass flow simulation tool r.avaflow. Thereby, we build on the reconstruction of the pre-event topography as well as on various reference data obtained from the large amount of available literature – mainly, the peak elevation and volumes of the lava domes, the impact areas of the flow processes, and ash fall characteristics. Most observations are successfully reproduced with physically plausible, though calibrated, parameter sets and temporal scaling of lava dome growth. Due to the complexity and multi-stage nature of the volcanic crisis, a number of simplifications had to be introduced, such as considering only the twelve largest sequences of dome growth and pyroclastic flows, and evaluating some of the results on the basis of aggregated impact areas for more than one event. Consequently, the results reflect a strong conceptual component, but can - at least in part - be considered useful for predictive modelling of similar events. Another scope of the simulation results, however, is its educational use. Appropriately presented, they greatly facilitate the generation of a better understanding for complex chains of volcanic processes and their consequences to learners at various levels in different educational contexts, from school and university all the way to targeted awareness-building campaigns.
How to cite: Mergili, M. and Pudasaini, S. P.: Landscape and hazard evolution during the Montserrat volcanic crisis 1995–2010: an integrated simulation with r.avaflow, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13407, https://doi.org/10.5194/egusphere-egu21-13407, 2021.
On 24 December 2018 a flank eruption started on Etna from an eruptive fissure opened on the eastern side of the New Southeast Crater (NCSE) at about 3,100 m asl, which in few minutes, propagated to the south-east, overcoming the edge of the western wall of the Valle del Bove (VdB), reaching an altitude of 2,400 m asl and a total length of about 2 km. The eruption, which lasted only three days, produced lava flows from different vents along the eruptive fissure that reached a distance of about 4.2 km and covered an area of about 1 km2. The satellite monitoring of the 2018 Etna eruption was performed using the HOTSAT system using mid and thermal infrared data acquired by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which provided minimum and maximum estimates for the lava thermal flux, the effusion rate and the lava volume. The SEVIRI-derived effusion rate estimates were used as input of the MAGFLOW model to simulate the actual lava flow field, obtaining a very good fit. We also simulated different eruptive scenarios assuming the lava emission wouldn’t run out in only three days to forecast if, when and how the lava flow could reach the inhabited areas, causing possible significant damage.
How to cite: Bilotta, G., Calvari, S., Cappello, A., Corradino, C., Del Negro, C., Ganci, G., and Hérault, A.: Lava flow hazard of the 2018 Etna eruption: What happened and what could happen, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4448, https://doi.org/10.5194/egusphere-egu21-4448, 2021.
Lava flow simulations are valuable tools for forecasting and assessing the areas that may be potentially affected by new eruptions, but also for interpreting past volcanic events and understanding the controls on lava flow behaviour. The plugin Q-LavHA v3.0 (Mossoux et al., 2016), integrated into QGIS, allows simulating the inundation probability of an a’a lava flow from one or more eruptive vents spatially distributed in a Digital Elevation Model (DEM). Q-LavHA allows running probabilistic and deterministic methods to calculate the spatial propagation and the maximum length of lava flows, considering a number of morphometric and/or thermo-rheological parameters.
El Hierro is the smallest and westernmost island of the Canary Archipelago where basaltic lava flows infer the major volcanic hazard. However, no lava flow emplacement modelling has been carried out yet on the island. Here we present Montaña Aguarijo's lava flow simulation, a monogenetic volcano located on the NW rift of El Hierro. Detailed geological fieldwork and current topographic-bathymetric data were used to reconstruct the pre-eruption (before the eruption modifies the relief) and post-eruption (at the end of the eruption, prior to erosive processes) DEMs. The obtained morphometric parameters of the lava flow (2,268m long; 5m medium thickness; 422,560m3) were used to run probabilistic (Maximum Length) and deterministic (FLOWGO) models. The latter also considers a set of thermo-rheological properties of the lava flow such as initial viscosity, phenocryst content, or vesicle proportion.
Results obtained show a high degree of overlap between the real and simulated lava flows. Therefore, the thermo-rheological parameters considered in the deterministic approach are close to the real ones that constrained Montaña Aguarijo lava flow propagation. Moreover, this work evidence the effectiveness of Q-LavHA plugin when simulating complex lava flows such as Montaña Aguarijo’s lava which runs through a coastal platform, a typical morphology of oceanic volcanic islands.
Financial support was provided by Project LAJIAL (ref. PGC2018-101027-B-I00, MCIU/AEI/FEDER, EU). This study was carried out in the framework of the Research Consolidated Groups GEOVOL (Canary Islands Government, ULPGC) and GEOPAM (Generalitat de Catalunya, 2017 SGR 1494).
Mossoux, S., Saey, M., Bartolini, S., Poppe, S., Canters F., Kervyn, M. (2016). Q-LAVHA: A flexible GIS plugin to simulate lava flows. Computers & Geosciences, 97, 98-109.
How to cite: Rodriguez-Gonzalez, A., Prieto-Torrell, C., Aulinas, M., Perez-Torrado, F. J., Fernandez-Turiel, J.-L., Criado Hernández, C., and Cabrera, M. C.: Lava flow modelling at El Hierro (Canary Islands): the case of Montaña Aguarijo volcano, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13608, https://doi.org/10.5194/egusphere-egu21-13608, 2021.
Numerical simulation is a fundamental aspect of modern volcanology, providing tools for the forecasting of lava flows behavior, so as to assist in the design of mitigation actions for volcanic risk. In addition to the prediction of the emplacement topology, numerical simulation can be useful to study the possible outcomes of the interaction between a lava flow and a building. This kind of information can help to estimate the vulnerability of buildings so as to produce more accurate risk evaluations. Smoothed Particle Hydrodynamics (SPH) is a particle-based numerical method, particularly suited for the simulation of fluids with a high level of complexity, that can intrinsically deal with all of the physical properties of lava. GPUSPH is a simulation engine based on the SPH method that has been developed in order to take into account the challenging aspects of lava simulations and has been successfully applied to the simulation of lava-related benchmark tests. Here we use the SPH method, coupled within the framework of GPUSPH with a rigid body mechanics solver provided by the Project Chrono engine, for the realistic study of lava-buildings interaction. The resulting coupled model is able to simulate masonry with a brick-level accurate description, providing insights on any damages happening to the structure. We will show the simulation of a lava flow interacting with an elementary masonry piece, where a total collapse of the structure is induced by the action of the lava.
How to cite: Zago, V., Bilotta, G., Cappello, A., Dalrymple, R., Fortuna, L., Ganci, G., Herault, A., and Del Negro, C.: SPH model for the simulation of lava-buildings interactions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14717, https://doi.org/10.5194/egusphere-egu21-14717, 2021.
Abstract: Temperature is a key parameter controlling the rheology of lava flows. Unfortunately, the hazardous behavior of eruptions prevents direct measurements of hot magmatic bodies. Hence, the temperature of magma is mostly retrieved by using remote sensing methods (ground-based or satellite-based detectors) build on measuring the infrared (IR) radiance of the body . These well-established techniques are however subjected to important errors related to, among others, the poor knowledge of the spectral emissivity (ε), which is one of the most critical parameters in IR radiance measurement [2, 3]. In this study, we performed in situ optical measurements at relevant magmatic temperatures of basaltic magma from the 2014–2015 Holuhraun eruption (Bardarbunga volcano, Iceland). Spectral emissivity has been systematically determined over a wide spectral range (400–15000 cm−1) covering TIR, MIR and SWIR regions, from room temperature up to 1473 K using a non-contact in situ IR emissivity apparatus . SEM, EMPA, Raman spectroscopy, DSC, XRD and TEM techniques helped characterize and understand the complex radiative behavior of this natural magmatic composition. The results show not only that spectral emissivity varies accordingly with temperature and wavenumber but also that small changes in bulk rock composition or texture produce drastic changes in emissivity at given temperature, with iron content and its oxidation state being the main agents controlling this parameter. Appropriate emissivity values can then be used to refine current radiative data from IR remote sensing and to implement the thermo-rheological models of lava flows  as to support hazard assessment and risk mitigation.
References:  Kolzenburg et al. 2017. Bull. Volc. 79:45.  Harris, A. 2013: Cambridge University press. 728.  Rogic et al. 2019 Remote Sens., 11, 662  De Sousa Meneses et al. 2015. Infrared Physics & Technology 69.  Ramsey et al. 2019. Annals of Geophysics, 62, 2.
Keywords: Spectral emissivity, temperature, IR spectroscopy, remote sensing, basalt
How to cite: Biren, J., Cosson, L., del Campo, L., Genevois, C., Veron, E., Ory, S., Li, H., Slodczyk, A., and Andújar, J.: Exploring the in situ high temperature emissivity of 2014–2015 Bardarbunga magmas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-714, https://doi.org/10.5194/egusphere-egu21-714, 2021.
At Stromboli, minor volcanic eruptions occur at time intervals of approximately five minutes on average, making it one of the most active volcanoes worldwide. In addition to these mostly harmless events, there are also stronger eruptions and paroxysms which pose a serious threat to residents and tourists. In light of recent developments in Machine Learning, this study attempts to apply these new tools for the analysis of the time-varying volcanic eruptions at Stromboli. As input for the Machine-Learning approach, we use continuous recordings of seismic signals from two seismometers on the island. The data is available from IRIS and includes records starting in 2012 up to the present.
One primary challenge is to label and classify the data, i.e., to discriminate events of interest from noise. The variety of signal-appearance in the recorded data is wide, in some periods the events are clearly distinguishable from noise whereas, in other cases relevant events are obscured by the high noise level. To enable the event-detection in all cases, we developed the following algorithm: in the first step, the seismic data is pre-processed with an STA/LTA-Filter, which allows detection of events based on a prominence threshold. However, due to the diversity of signal patterns, a fixed set of hyperparameters (STA- and LTA-window length, prominence threshold, correlation coefficient) fails to reliably extract the relevant events in a consistent manner. Therefore, the (time-varying) noise level of the recordings is used as an additional key indicator. After this, the hyperparameters are optimized. The automatic adaptation is then used for labeling the continuous seismic data.
After extracting the events based on this approach, a machine learning model is trained to analyze the recordings for possible patterns in the interval times and the event amplitudes. This study is expected to provide constraints on the possibility to detect complex time-dependent patterns of the eruption history at Stromboli.
How to cite: Fenner, D., Rümpker, G., Stöcker, H., Chakraborty, M., Li, W., Faber, J., Zhou, K., Steinheimer, J., and Srivastava, N.: Machine Learning analysis of seismic signals recorded at Stromboli Volcano, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13166, https://doi.org/10.5194/egusphere-egu21-13166, 2021.
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