Forecasting lava flow hazards during the 2006 Etna eruption: Using the MAGFLOW cellular automata model

Hérault, Alexis; Vicari, Annamaria; Ciraudo, A.; Negro, Ciro Del

doi:10.1016/j.cageo.2007.10.008

Cited by 71 publications

(34 citation statements)

References 15 publications

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“…Codes such as MAGFLOW [68,69] and DOWNFLOW [70,71] have been developed to provide near-real-time prediction of lava flow paths. These and other models have generally been shown to provide a good match with the true path of supply-limited lava flows governed by underlying topography.…”

Section: Discussionmentioning

confidence: 99%

Satellite and Ground Remote Sensing Techniques to Trace the Hidden Growth of a Lava Flow Field: The 2014–2015 Effusive Eruption at Fogo Volcano (Cape Verde)

et al. 2018

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Fogo volcano erupted in 2014-2015 producing an extensive lava flow field in the summit caldera that destroyed two villages, Portela and Bangaeira. The eruption started with powerful explosive activity, lava fountains, and a substantial ash column accompanying the opening of an eruptive fissure. Lava flows spreading from the base of the eruptive fissure produced three arterial lava flows. By a week after the start of the eruption, a master lava tube had already developed within the eruptive fissure and along the arterial flow. In this paper, we analyze the emplacement processes based on observations carried out directly on the lava flow field, remote sensing measurements carried out with a thermal camera, SO 2 fluxes, and satellite images, to unravel the key factors leading to the development of lava tubes. These were responsible for the rapid expansion of lava for thẽ 7.9 km length of the flow field, as well as the destruction of the Portela and Bangaeira villages. The key factors leading to the development of tubes were the low topography and the steady magma supply rate along the arterial lava flow. Comparing time-averaged discharge rates (TADR) obtained from satellite and Supply Rate (SR) derived from SO 2 flux data, we estimate the amount and timing of the lava flow field endogenous growth, with the aim of developing a tool that could be used for hazard assessment and risk mitigation at this and other volcanoes.

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Section: Discussionmentioning

confidence: 99%

Satellite and Ground Remote Sensing Techniques to Trace the Hidden Growth of a Lava Flow Field: The 2014–2015 Effusive Eruption at Fogo Volcano (Cape Verde)

et al. 2018

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“…One of the main parameters controlling the evolution of individual flows is effusion rate (Pinkerton and Wilson 1994;Walker 1973), and long period effusion rate changes, such as flow-waning prior to the cessation of an eruption, can now be routinely incorporated in flow models (e.g. Crisci et al 2008;Herault et al 2009;Hidaka et al 2005). However, effusion rates commonly vary over a wide range of timescales (from years to minutes) and, for short period changes, neither the causes nor the effects are fully understood, despite their potential importance in the evolution of flow fields (e.g.…”

Section: Introductionmentioning

confidence: 99%

Imaging short period variations in lava flux

2010

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Short period (e.g. <1 h) variations in lava effusion rate have been detected previously on Mount Etna, Sicily, but the causes and effects of such changes are poorly understood because of difficulties in obtaining suitably high frequency measurements over long periods. Here, we report short period flux variations in active lava flows, recorded in dense time series imagery over a 7-night period using modified remote trail cameras. The sequences of night-time images show significant pulses of enhanced incandescence, interpreted as short period increases in lava flux, travelling down-channel at velocities of ∼10-20 mmin −1 . Pulse generation decreased from an average of one pulse per hour on the first night to approximately one per night within a few nights. Effusion rate changes on these timescales are considered to reflect instabilities in magma ascent and, consequently, could provide insight into subsurface flow processes.

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“…In particular, we developed the MAGFLOW cellular automata model to forecast possible lava flow paths and to predict in near real time the evolution of the phenomena during ongoing eruptions Herault et al 2007). An algorithm based on the Monte Carlo approach to solve truly the anisotropic problem was also included in MAGFLOW.…”

Section: Introductionmentioning

confidence: 99%

Simulations of the 2004 lava flow at Etna volcano using the magflow cellular automata model

et al. 2007

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Since the mechanical properties of lava change over time, lava flows represent a challenge for physically based modeling. This change is ruled by a temperature field which needs to be modeled. MAGFLOW Cellular Automata (CA) model was developed for physically based simulations of lava flows in near real-time. We introduced an algorithm based on the Monte Carlo approach to solve the anisotropic problem. As transition rule of CA, a steadystate solution of Navier-Stokes equations was adopted in the case of isothermal laminar pressure-driven Bingham fluid. For the cooling mechanism, we consider only the radiative heat loss from the surface of the flow and the change of the temperature due to mixture of lavas between cells with different temperatures. The model was applied to reproduce a real lava flow that occurred during the 2004-2005 Etna eruption. The simulations were computed using three different empirical relationships between viscosity and temperature.

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Forecasting lava flow hazards during the 2006 Etna eruption: Using the MAGFLOW cellular automata model

Cited by 71 publications

References 15 publications

Satellite and Ground Remote Sensing Techniques to Trace the Hidden Growth of a Lava Flow Field: The 2014–2015 Effusive Eruption at Fogo Volcano (Cape Verde)

Satellite and Ground Remote Sensing Techniques to Trace the Hidden Growth of a Lava Flow Field: The 2014–2015 Effusive Eruption at Fogo Volcano (Cape Verde)

Imaging short period variations in lava flux

Simulations of the 2004 lava flow at Etna volcano using the magflow cellular automata model

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