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Volume 14, issue 11 | Copyright

Special issue: Numerical wildland combustion, from the flame to the...

Nat. Hazards Earth Syst. Sci., 14, 2951-2973, 2014
https://doi.org/10.5194/nhess-14-2951-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 Nov 2014

Research article | 10 Nov 2014

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

M. C. Rochoux1,2,3,4, S. Ricci1,2, D. Lucor5, B. Cuenot1, and A. Trouvé6 M. C. Rochoux et al.
  • 1CERFACS, 42 avenue Gaspard Coriolis, 31057 Toulouse Cedex 01, France
  • 2SUC/CNRS-URA1875, 42 avenue Gaspard Coriolis, 31057 Toulouse CEDEX 01, France
  • 3Ecole Centrale Paris, Grande voie des vignes, 92295 Châtenay-Malabry, France
  • 4EM2C/CNRS-UPR288, Grande voie des vignes, 92295 Châtenay-Malabry, France
  • 5Institut d'Alembert, Université Pierre et Marie Curie, CNRS-UMR7190, 4 place Jussieu, 75006 Paris, France
  • 6Dept. of Fire Protection Engineering, University of Maryland, College Park, MD 20742, USA

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: an Eulerian front 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 (DA) algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the nonlinearities 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 DA 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 DA strongly relate to the spatial and temporal variability of the errors in the ROS model parameters.

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This paper presents a data-driven wildfire simulator for forecasting wildfire spread scenarios at a reduced computational cost that is consistent with operational systems. A wildfire spread simulator combined with an ensemble-based data assimilation algorithm is indeed a promising approach to reduce uncertainties in the forecast location of the fire front and to introduce a paradigm shift in the wildfire emergency response.
This paper presents a data-driven wildfire simulator for forecasting wildfire spread scenarios...
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