Wildland Fire Behaviour Case Studies and Fuel Models for Landscape‐Scale Fire Modeling

Santoni, Paul‐Antoine; Filippi, Jean Baptiste; Balbi, Jacques-Henri; Bosseur, Frédéric

doi:10.1155/2011/613424

Cited by 34 publications

(32 citation statements)

References 23 publications

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“…Fuel parameters are derived from Anderson et al (1981) for the mixed/grass and pine forest types, while the "Proterina" parameterisation (Santoni et al, 2011) is used for the shrubs and maquis class. Among the 10 possible fuels, these four classes (shrubs, pine, mixed, maquis) were the only burning fuels across all cases, with a large majority of maquis.…”

Section: Data Preprocessingmentioning

confidence: 99%

Evaluation of forest fire models on a large observation database

Filippi

Mallet

Nader

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper presents the evaluation of several fire propagation models using a large set of observed fires. The observation base is composed of 80 Mediterranean fire cases of different sizes, which come with the limited information available in an operational context (burned surface and approximative ignition point). Simulations for all cases are carried out with four different front velocity models. The results are compared with several error scoring methods applied to each of the 320 simulations. All tasks are performed in a fully automated manner, with simulations run as first guesses with no tuning for any of the models or cases. This approach leads to a wide range of simulation performance, including some of the bad simulation results to be expected in an operational context. Disregarding the quality of the input data, it is found that the models can be ranked based on their performance and that the most complex models outperform the more empirical ones. Data and source codes used for this paper are freely available to the community.

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Section: Data Preprocessingmentioning

confidence: 99%

Evaluation of forest fire models on a large observation database

Filippi

Mallet

Nader

2014

Nat. Hazards Earth Syst. Sci.

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“…In the absence of a long-term, concerted effort to systematically monitor and document wildfire behaviour (Alexander and Thomas 2003a,b), data to test theoretical or empirical model formulae against actual wildfire behaviour accumulate slowly from opportunistic high-quality observations (e.g., Butler and Reynolds 1997, Alexander and Taylor 2010, Santoni et al 2011. As a result, model testing or evaluation is usually based on laboratory experimental fires (e.g., Beaufait 1965, Menage et al 2012), as opposed to operational prescribed fires (e.g., Hough 1968, Doren et al 1987, Custer and Thorsen 1996, Alexander 2010 or outdoor experimental fires (e.g., Stocks et al 2004a,b, Stephens et al 2008, Cruz et al 2010, 2013, McCaw et al 2012.…”

Section: Internal Accuracy Of Model Relationshipsmentioning

confidence: 99%

“…Fire behaviour is determined by complex chemical and physical processes occurring over a wide range of spatial and temporal scales (Santoni et al 2011). The observed spread rate in a highintensity, free-burning wildfire, for example, can span over four orders of magnitude around its perimeter .…”

Section: Introductionmentioning

confidence: 99%

Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

AlexanderMartin

Cruz

2013

The Forestry Chronicle

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The degree of accuracy in model predictions of wildland fire behaviour characteristics are dependent on the model's applicability to a given situation, the validity of the model's relationships, and the reliability of the model input data. While much progress has been made by fire behaviour research in the past 35 years or so in addressing these three sources of model error, the accuracy in model predictions are still very much at the mercy of our present understanding of the natural phenomena exhibited by free-burning wildland fires and the inherent temporal and spatial variability in the fire environment. This paper will serve as a state-of-the-art primer on the subject of error sources in model predictions of wildland fire behaviour and includes a short historical overview of wildland fire behaviour research as it relates to model development.

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“…The Digital Elevation Model (DEM) is extracted from IGN BD ALTI R 25-m resolution. IFN classes have been grouped according to the methodology developed in [43] and use the proposed characterization of the vegetation. Figure 4 presents the fuel distribution over the final burnt area, with four main classes, as well as small non-burnable area providing fuel breaks.…”

Section: Data Sourcesmentioning

confidence: 99%

Simulation of a Large Wildfire in a Coupled Fire-Atmosphere Model

et al. 2018

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Abstract:The Aullene fire devastated more than 3000 ha of Mediterranean maquis and pine forest in July 2009. The simulation of combustion processes, as well as atmospheric dynamics represents a challenge for such scenarios because of the various involved scales, from the scale of the individual flames to the larger regional scale. A coupled approach between the Meso-NH (Meso-scale Non-Hydrostatic) atmospheric model running in LES (Large Eddy Simulation) mode and the ForeFire fire spread model is proposed for predicting fine-to large-scale effects of this extreme wildfire, showing that such simulation is possible in a reasonable time using current supercomputers. The coupling involves the surface wind to drive the fire, while heat from combustion and water vapor fluxes are injected into the atmosphere at each atmospheric time step. To be representative of the phenomenon, a sub-meter resolution was used for the simulation of the fire front, while atmospheric simulations were performed with nested grids from 2400-m to 50-m resolution. Simulations were run with or without feedback from the fire to the atmospheric model, or without coupling from the atmosphere to the fire. In the two-way mode, the burnt area was reproduced with a good degree of realism at the local scale, where an acceleration in the valley wind and over sloping terrain pushed the fire line to locations in accordance with fire passing point observations. At the regional scale, the simulated fire plume compares well with the satellite image. The study explores the strong fire-atmosphere interactions leading to intense convective updrafts extending above the boundary layer, significant downdrafts behind the fire line in the upper plume, and horizontal wind speeds feeding strong inflow into the base of the convective updrafts. The fire-induced dynamics is induced by strong near-surface sensible heat fluxes reaching maximum values of 240 kW m −2 . The dynamical production of turbulent kinetic energy in the plume fire is larger in magnitude than the buoyancy contribution, partly due to the sheared initial environment, which promotes larger shear generation and to the shear induced by the updraft itself. The turbulence associated with the fire front is characterized by a quasi-isotropic behavior. The most active part of the Aullene fire lasted 10 h, while 9 h of computation time were required for the 24 million grid points on 900 computer cores.

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Wildland Fire Behaviour Case Studies and Fuel Models for Landscape‐Scale Fire Modeling

Cited by 34 publications

References 23 publications

Evaluation of forest fire models on a large observation database

Evaluation of forest fire models on a large observation database

Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

Simulation of a Large Wildfire in a Coupled Fire-Atmosphere Model

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