Towards predictive simulation of wildfire spread at regional scale using ensemble-based data assimilation to correct the fire front position

Rochoux, Mélanie C.; Emery, C. M.; Ricci, Sophie; Cuenot, Bénédicte; Trouvé, Arnaud

doi:10.3801/iafss.fss.11-1443

Cited by 17 publications

(7 citation statements)

References 24 publications

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“…The instantaneous front velocity can then also be represented by the sum of a deterministic part and random contributions. This formulation has a formal analogy with the so-called ensemble Kalman filter (EnKF) (Mandel et al, 2008;Rochoux et al, 2012Rochoux et al, , 2013Rochoux et al, , 2014a. The EnKF is a statistical operational technique for handling uncertainties in the estimation of the ROS, but uncertainties in measurements are not straightforwardly related to physical random fluctuations, and data error is generally Gaussian distributed according to pure statistical arguments.…”

Section: Model Picture and Mathematical Formulation Of A Methods For Tmentioning

confidence: 99%

“…It should be stressed that, in the proposed approach, the randomisation of the fireline motion is accounted for as being due to physical processes, namely the turbulent hot-air transport and the fire spotting phenomenon. If uncertainties in the input data necessary for computing the ROS are to be taken into account, resulting in an ROS treated as a random variable, the model proposed here could be improved by coupling it with a data assimilation algorithm based, for example, on the so-called ensemble Kalman filter (Mandel et al, 2008;Rochoux et al, 2012Rochoux et al, , 2013Rochoux et al, , 2014a).…”

Section: Introductionmentioning

confidence: 99%

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Modelling wildland fire propagation by tracking random fronts

Pagnini¹,

Mentrelli²

2013

Preprint

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Abstract. Wildland fire propagation is studied in literature by two alternative approaches, namely the reaction-diffusion equation and the level-set method. These two approaches are considered alternative each other because the solution of the reaction-diffusion equation is generally a continuous smooth function that has an exponential decay and an infinite support, while the level-set method, which is a front tracking technique, generates a sharp function with a finite support. However, these two approaches can indeed be considered complementary and reconciled. Turbulent hot-air transport and fire spotting are phenomena with a random character that are extremely important in wildland fire propagation. As a consequence the fire front gets a random character, too. Hence a tracking method for random fronts is needed. In particular, the level-set contourn is here randomized accordingly to the probability density function of the interface particle displacement. Actually, when the level-set method is developed for tracking a front interface with a random motion, the resulting averaged process emerges to be governed by an evolution equation of the reaction-diffusion type. In this reconciled approach, the rate of spread of the fire keeps the same key and characterizing role proper to the level-set approach. The resulting model emerges to be suitable to simulate effects due to turbulent convection as fire flank and backing fire, the faster fire spread because of the actions by hot air pre-heating and by ember landing, and also the fire overcoming a firebreak zone that is a case not resolved by models based on the level-set method. Moreover, from the proposed formulation it follows a correction for the rate of spread formula due to the mean jump-length of firebrands in the downwind direction for the leeward sector of the fireline contour.

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Section: Model Picture and Mathematical Formulation Of A Methods For Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling wildland fire propagation by tracking random fronts

Pagnini¹,

Mentrelli²

2013

Preprint

View full text Add to dashboard Cite

show abstract

“…The instantaneous front velocity can then also be represented by the sum of a deterministic part and random contributions. This formulation has a formal analogy with the so-called ensemble Kalman filter (EnKF) Rochoux et al, 2012Rochoux et al, , 2013Rochoux et al, , 2014a. The EnKF is a statistical operational technique for handling uncertainties in the estimation of the ROS, but uncertainties in measurements are not straightforwardly related to physical random fluctuations, and data error is generally Gaussian distributed according to pure statistical arguments.…”

Section: Model Discussionmentioning

confidence: 99%

“…If uncertainties in the input data necessary for computing the ROS are to be taken into account, resulting in an ROS treated as a random variable, the model proposed here could be improved by coupling it with a data assimilation algorithm based, for example, on the so-called ensemble Kalman filter Rochoux et al, 2012Rochoux et al, , 2013Rochoux et al, , 2014a.…”

Section: Introductionmentioning

confidence: 99%

Modelling wildland fire propagation by tracking random fronts

Pagnini

Mentrelli

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. Wildland fire propagation is studied in the literature by two alternative approaches, namely the reactiondiffusion equation and the level-set method. These two approaches are considered alternatives to each other because the solution of the reaction-diffusion equation is generally a continuous smooth function that has an exponential decay, and it is not zero in an infinite domain, while the levelset method, which is a front tracking technique, generates a sharp function that is not zero inside a compact domain. However, these two approaches can indeed be considered complementary and reconciled. Turbulent hot-air transport and fire spotting are phenomena with a random nature and they are extremely important in wildland fire propagation. Consequently, the fire front gets a random character, too; hence, a tracking method for random fronts is needed. In particular, the level-set contour is randomised here according to the probability density function of the interface particle displacement. Actually, when the level-set method is developed for tracking a front interface with a random motion, the resulting averaged process emerges to be governed by an evolution equation of the reaction-diffusion type. In this reconciled approach, the rate of spread of the fire keeps the same key and characterising role that is typical of the level-set approach. The resulting model emerges to be suitable for simulating effects due to turbulent convection, such as fire flank and backing fire, the faster fire spread being because of the actions by hot-air pre-heating and by ember landing, and also due to the fire overcoming a fire-break zone, which is a case not resolved by models based on the level-set method. Moreover, from the proposed formulation, a correction follows for the formula of the rate of spread which is due to the mean jump length of firebrands in the downwind direction for the leeward sector of the fireline contour. The presented study constitutes a proof of concept, and it needs to be subjected to a future validation.

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“…Their work showed promising results while raising some concerns about spurious fire corrections and the computing time required. Following their idea, Rochoux et al (2014aRochoux et al ( , 2014bRochoux et al ( , 2015 explored a data-driven wildfire simulator based on parameter and state estimation that assimilates fire front positions and corrects the wildfire forecast by means of a level set model based on Rothermel's. They explored a parameter and state estimation strategy with stochastically based estimation of the error covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

Short-term fire front spread prediction using inverse modelling and airborne infrared images

Rios

Pastor

Valero

et al. 2016

Int. J. Wildland Fire

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Abstract.A wildfire forecasting tool capable of estimating the fire perimeter position sufficiently in advance of the actual fire arrival will assist firefighting operations and optimise available resources. However, owing to limited knowledge of fire event characteristics (e.g. fuel distribution and characteristics, weather variability) and the short time available to deliver a forecast, most of the current models only provide a rough approximation of the forthcoming fire positions and dynamics. The problem can be tackled by coupling data assimilation and inverse modelling techniques. We present an inverse modelling-based algorithm that uses infrared airborne images to forecast short-term wildfire dynamics with a positive lead time. The algorithm is applied to two real-scale mallee-heath shrubland fire experiments, of 9 and 25 ha, successfully forecasting the fire perimeter shape and position in the short term. Forecast dependency on the assimilation windows is explored to prepare the system to meet real scenario constraints. It is envisaged the system will be applied at larger time and space scales.

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Towards predictive simulation of wildfire spread at regional scale using ensemble-based data assimilation to correct the fire front position

Cited by 17 publications

References 24 publications

Modelling wildland fire propagation by tracking random fronts

Modelling wildland fire propagation by tracking random fronts

Modelling wildland fire propagation by tracking random fronts

Short-term fire front spread prediction using inverse modelling and airborne infrared images

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