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

Negro, Ciro Del; Fortuna, Luigi; Hérault, Alexis; Vicari, Annamaria

doi:10.1007/s00445-007-0168-8

Cited by 96 publications

(58 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Digital Elevation Models (DEMs) are the primary data layer used in models to estimate future lava flow paths and provide flow hazard assessments. The accuracy of the modeled results, either from the paths of steepest descent method (Kauahikaua 2007) or other physics-based lava flow models (e.g., FLOWGO, SCIARA, DOWNFLOW, MAGFLOW), depends strongly on how well the DEM represents the physical environment, which can be difficult to determine in heavily vegetated areas (Harris and Rowland 2001;Crisci et al 2004;Favalli et al 2005;Negro et al 2008). As lava flows change the landscape, subsequent flows will travel along new paths of steepest descent, requiring updated DEMs to reflect the dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

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

2017

View full text Add to dashboard Cite

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.

show abstract

“…Robust quantitative probabilistic volcanic risk models are increasingly desirable for volcanic risk management, particularly for loss forecasting, infrastructure management and land-use planning. This has driven the development of sophisticated probabilistic hazard models (e.g., Schilling 1998;Bonadonna 2006;Costa et al 2006;Del Negro et al 2008;Wadge 2009). However, vulnerability models have lagged considerably and there is now an increasingly urgent need for quantitative vulnerability assessment of volcanic hazard impacts.…”

Section: Introductionmentioning

confidence: 99%

Framework for developing volcanic fragility and vulnerability functions for critical infrastructure

Wilson

Deligne

et al. 2017

J Appl. Volcanol.

View full text Add to dashboard Cite

Volcanic risk assessment using probabilistic models is increasingly desired for risk management, particularly for loss forecasting, critical infrastructure management, land-use planning and evacuation planning. Over the past decades this has motivated the development of comprehensive probabilistic hazard models. However, volcanic vulnerability models of equivalent sophistication have lagged behind hazard modelling because of the lack of evidence, data and, until recently, minimal demand. There is an increasingly urgent need for development of quantitative volcanic vulnerability models, including vulnerability and fragility functions, which provide robust quantitative relationships between volcanic impact (damage and disruption) and hazard intensity. The functions available to date predominantly quantify tephra fall impacts to buildings, driven by life safety concerns. We present a framework for establishing quantitative relationships between volcanic impact and hazard intensity, specifically through the derivation of vulnerability and fragility functions. We use tephra thickness and impacts to key infrastructure sectors as examples to demonstrate our framework. Our framework incorporates impact data sources, different impact intensity scales, preparation and fitting of data, uncertainty analysis and documentation. The primary data sources are post-eruption impact assessments, supplemented by laboratory experiments and expert judgment, with the latter drawing upon a wealth of semi-quantitative and qualitative studies. Different data processing and function fitting techniques can be used to derive functions; however, due to the small datasets currently available, simplified approaches are discussed. We stress that documentation of data processing, assumptions and limitations is the most important aspect of function derivation; documentation provides transparency and allows others to update functions more easily. Following our standardised approach, a volcanic risk scientist can derive a fragility or vulnerability function, which then can be easily compared to existing functions and updated as new data become available. To demonstrate how to apply our framework, we derive fragility and vulnerability functions for discrete tephra fall impacts to electricity supply, water supply, wastewater and transport networks. These functions present the probability of an infrastructure site or network component equalling or exceeding one of four impact states as a function of tephra thickness.

show abstract

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

Cited by 96 publications

References 22 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)

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

Framework for developing volcanic fragility and vulnerability functions for critical infrastructure

Contact Info

Product

Resources

About