Modeling of the 2001 lava flow at Etna volcano by a Cellular Automata approach

Vicari, Annamaria; Hérault, Alexis; Negro, Ciro Del; Coltelli, M.; Marsella, Maria; Proietti, Cristina

doi:10.1016/j.envsoft.2006.10.005

Cited by 105 publications

(71 citation statements)

References 19 publications

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“…However, more sophisticated lava flow modelling efforts, including stochastic slope-controlled models (Harris and Rowland 2001;Favalli et al 2005), cellular automata models (Crisci et al 2004;Del Negro et al 2005;Vicari et al 2007), and other numerical simulations (Dietterich et al 2015), also rely on high quality DEMs input layers to produce successful results. UAS provide a means of effectively generating these needed DEMs, regardless of the modeling method.…”

Section: Applications For Other Lava Flow Modelsmentioning

confidence: 99%

Lava flow hazard prediction and monitoring with UAS: a case study from the 2014–2015 Pāhoa lava flow crisis, Hawai‘i

2017

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Accurately predicting lava flow path behavior is critical for active crisis management operations. The advance and emplacement of pāhoehoe flows modifies and inverts pre-existing topography, prompting the need for rapid and accurate updates to the topographic models used to forecast flow paths. The evolution and velocity of pāhoehoe flows are dependent on macro and micro topography, the slope of the descent path, effusion rate, and rheology. During the 2014-2015 Pāhoa crisis on the island of Hawai'i, we used a low-altitude unmanned aerial system (UAS) to quickly and repeatedly image the active front of a slowly advancing pāhoehoe lava flow. This imagery was used to generate a series of 1 m resolution bare-earth digital elevation models (DEMs) and associated paths of steepest descent over the study area. The spatial resolution and timeliness of these UAS-derived models are an improvement over the existing topographic data used by managers during the crisis. Results from a stepwise resampling experiment suggest that the optimum DEM resolution for generating accurate pāhoehoe flow paths through lowland tropical forest environments is between 1 and 3 m. Our updated models show that future flows in this area will likely be deflected by these newly emplaced flows, possibly threatening communities not directly impacted by the original 2014-2015 lava flow. We demonstrate the value of deploying UAS during a dynamic volcanic crisis and suggest that this technology can fill critical monitoring gaps for Kīlauea and other active volcanoes worldwide.

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Section: Applications For Other Lava Flow Modelsmentioning

confidence: 99%

Lava flow hazard prediction and monitoring with UAS: a case study from the 2014–2015 Pāhoa lava flow crisis, Hawai‘i

2017

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“…However, despite the sophisticated numerical models that have been developed for basaltic lava flows (Crisci et al, 1986;Hidaka et al, 2005;Vicari et al, 2007;Hérault et al, 2011), a full understanding of the factors controlling the rate and extent of lava flow advance, which is essential for adequate hazard forecasting over a broad range of lava geochemistries, remains elusive due to the complexity of lava flow rheology, internal architecture, and interactions with topography. The frequency of basaltic eruptions has provided many examples for modeling low-silica lavas (Crisci et al, 1986;Hidaka et al, 2005;Vicari et al, 2007;Hérault et al, 2011), but equivalent studies of high-silica flows are relatively rare and poorly constrained, and thus reflect weaknesses in our universal understanding of lava emplacement processes. Here, we use observations of the 2011-2012 Cordón Caulle rhyolite lava flow as an unparalleled opportunity to study a high-silica flow, and we present the first modeling study to our knowledge that examines the advance of a high viscosity and crystal-poor lava.…”

Section: Introductionmentioning

confidence: 99%

Emplacing a Cooling-Limited Rhyolite Lava Flow: Similarities with Basaltic Lava Flows

et al. 2017

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Accurate forecasts of lava flow length rely on estimates of eruption and magma properties and, potentially more challengingly, on an understanding of the relative influence of characteristics such as the apparent viscosity, the yield strength of the flow core, or the strength of the lava's surface crust. For basaltic lavas, the relatively high frequency of eruptions has resulted in numerous opportunities to test emplacement models on such low silica lava flows. However, the flow of high silica lava is much less well understood due to the paucity of contemporary events and, if observations of flow length change are used to constrain straightforward models of lava advance, remaining uncertainties can limit the insight gained. Here, for the first time, we incorporate morphological observations from during and after flow field evolution to improve model constraints and reduce uncertainties. After demonstrating the approach on a basaltic lava flow (Mt. Etna 2001), we apply it to the 2011-2012 Cordón Caulle rhyolite lava flow, where unprecedented observations and syn-emplacement satellite imagery of an advancing silica-rich lava flow have indicated an important influence from the lava flow's crust on flow emplacement. Our results show that an initial phase of viscosity-controlled advance at Cordón Caulle was followed by later crustal control, accompanied by formation of flow surface folds and large-scale crustal fractures. Where the lava was unconstrained by topography, the cooled crust ultimately halted advance of the main flow and led to the formation of breakouts from the flow front and margins, influencing the footprint of the lava, its advance rate, and the duration of flow advance. Highly similar behavior occurred in the 2001 Etna basaltic lava flow. In our comparison of these two cases, we find close similarities between the processes controlling the advance of a crystal-poor rhyolite and a basaltic lava flow, suggesting common controlling mechanisms that transcend the profound rheological and compositional differences of the lavas.

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“…whether grid cell locations are 4-or 8-connected or whether lava is spread proportional to slope), which affect the simulated flow behavior. In this paper, the MOLASSES algorithm incorporates a Moore Neighborhood, where grid cells interact with 8 adjacent neighbors to avoid mesh-based anisotropy (e.g., Vicari et al, 2007). Lava spreading among neighbors is proportional to the relative cell-to-cell slope.…”

Section: Molasses (Modular Lava Simulation Software In Earthmentioning

confidence: 99%

Benchmarking computational fluid dynamics models of lava flow simulation for hazard assessment, forecasting, and risk management

et al. 2017

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Numerical simulations of lava flow emplacement are valuable for assessing lava flow hazards, forecasting active flows, designing flow mitigation measures, interpreting past eruptions, and understanding the controls on lava flow behavior. Existing lava flow models vary in simplifying assumptions, physics, dimensionality, and the degree to which they have been validated against analytical solutions, experiments, and natural observations. In order to assess existing models and guide the development of new codes, we conduct a benchmarking study of computational fluid dynamics (CFD) models for lava flow emplacement, including VolcFlow, OpenFOAM, FLOW-3D, COMSOL, and MOLASSES. We model viscous, cooling, and solidifying flows over horizontal planes, sloping surfaces, and into topographic obstacles. We compare model results to physical observations made during well-controlled analogue and molten basalt experiments, and to analytical theory when available. Overall, the models accurately simulate viscous flow with some variability in flow thickness where flows intersect obstacles. OpenFOAM, COMSOL, and FLOW-3D can each reproduce experimental measurements of cooling viscous flows, and OpenFOAM and FLOW-3D simulations with temperature-dependent rheology match results from molten basalt experiments. We assess the goodness-of-fit of the simulation results and the computational cost. Our results guide the selection of numerical simulation codes for different applications, including inferring emplacement conditions of past lava flows, modeling the temporal evolution of ongoing flows during eruption, and probabilistic assessment of lava flow hazard prior to eruption. Finally, we outline potential experiments and desired key observational data from future flows that would extend existing benchmarking data sets.

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Modeling of the 2001 lava flow at Etna volcano by a Cellular Automata approach

Cited by 105 publications

References 19 publications

Lava flow hazard prediction and monitoring with UAS: a case study from the 2014–2015 Pāhoa lava flow crisis, Hawai‘i

Lava flow hazard prediction and monitoring with UAS: a case study from the 2014–2015 Pāhoa lava flow crisis, Hawai‘i

Emplacing a Cooling-Limited Rhyolite Lava Flow: Similarities with Basaltic Lava Flows

Benchmarking computational fluid dynamics models of lava flow simulation for hazard assessment, forecasting, and risk management

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