Towards predictive data-driven simulations of wildfire spread – Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

Abstract

Abstract. This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: a level-set-based fire propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the non-linearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially-uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based data assimilation algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically-generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of data assimilation strongly relate to the spatial and temporal variability of the errors in the ROS model parameters.

Modelling wildland fire propagation by tracking random fronts

“…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.…”

“…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).…”

Modelling wildland fire propagation by tracking random fronts

“…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.…”

“…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.…”

Unraveling the Complexity of Wildland Urban Interface Fires

“…These models encompass different types ranging from empirical to completely analytical. Some prominent examples include - Rothermel’s wildland fuel model 15 , National bushfire model 16 , BehavePlus 17 , FlamMap 18 , 19 , FARSITE 20 , FSPro 21 , WIFIRE 22 and others 23 , 24 . These models are widely accepted and entail several aspects of wildfires; however, the models either entirely focus on wildlands or pertain to a localized aspect of fire propagation in communities.…”