Forecasting lava flow paths by a stochastic approach

Favalli, Massimiliano

doi:10.1029/2004gl021718

Cited by 118 publications

(157 citation statements)

References 15 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%

“…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%

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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%

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

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“…DOWNFLOW is a stochastic model developed at INGV-Pisa that searches for the most likely array of down-hill paths that a lava flow will follow on a DEM of a given spatial resolution, vertical resolution and error (Favalli et al 2005). During each eruption, DOWN-FLOW was initialized upon reception of the new vent location using the 25-m resolution DEM of Piton de la Fournaise based on the 1997 topography.…”

Section: Initialization and Execution Of Downflow And Flowgomentioning

confidence: 99%

Effusive crises at Piton de la Fournaise 2014–2015: a review of a multi-national response model

et al. 2017

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Many active European volcanoes and volcano observatories are island-based and located far from their administrative "mainland". Consequently, Governments have developed multisite approaches, in which monitoring is performed by a network of individuals distributed across several national research centers. At a transnational level, multinational networks are also progressively emerging. Piton de la Fournaise (La Réunion Island, France) is one such example. Piton de la Fournaise is one of the most active volcanoes of the World, and is located at the greatest distance from its "mainland" than any other vulnerable "overseas" site, the observatory being 9365 km from its governing body in Paris. Effusive risk is high, so that a well-coordinated and rapid response involving near-real time delivery of trusted, validated and operational product for hazard assessment is critical. Here we review how near-real time assessments of lava flow propagation were developed using rapid provision, and update, of key source terms through a dynamic and open integration of near-real time remote sensing, modeling and measurement capabilities on both the national and international level. The multi-national system evolved during the five effusive crises of 2014-2015, and is now mature for Piton de la Fournaise. This review allows us to identify strong and weak points in an extended observatory system, and demonstrates that enhanced multi-national integration can have fundamental implications in scientific hazard assessment and response during an on-going effusive crisis.

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“…For the prior distribution of nodes 1 to 5, we refer to the estimation of the nonmonitoring part in Marzocchi et al (2008). For nodes 6, 7, and 8, we can use results from numerical models that are available for most of the hazardous phenomena related to volcanic eruptions (e.g., Favalli et al 2005 for lava flows; Neri et al 2007 for pyroclastic density currents; Pfeiffer et al 2005 andCosta et al 2006 for ash dispersion). For a more detailed discussion about this point, see (Selva et al 2010).…”

Section: Numerical Models To Define Prior Distributionmentioning

confidence: 99%

BET_VH: a probabilistic tool for long-term volcanic hazard assessment

2010

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In this paper, we illustrate a Bayesian Event Tree to estimate Volcanic Hazard (BET_VH). The procedure enables us to calculate the probability of any kind of long-term hazardous event for which we are interested, accounting for the intrinsic stochastic nature of volcanic eruptions and our limited knowledge regarding related processes. For the input, the code incorporates results from numerical models simulating the impact of hazardous volcanic phenomena on an area and data from the eruptive history. For the output, the code provides a wide and exhaustive set of spatiotemporal probabilities of different events; these probabilities are estimated by means of a Bayesian approach that allows all uncertainties to be properly accounted for. The code is able to deal with many eruptive settings simultaneously, weighting each with its own probability of occurrence. In a companion paper, we give a detailed example of application of this tool to the Campi Flegrei caldera, in order to estimate the hazard from tephra fall.

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Forecasting lava flow paths by a stochastic approach

Cited by 118 publications

References 15 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

Effusive crises at Piton de la Fournaise 2014–2015: a review of a multi-national response model

BET_VH: a probabilistic tool for long-term volcanic hazard assessment

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