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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 6 | Copyright

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

Nat. Hazards Earth Syst. Sci., 14, 1491-1503, 2014
https://doi.org/10.5194/nhess-14-1491-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 13 Jun 2014

Research article | 13 Jun 2014

Forecasting wind-driven wildfires using an inverse modelling approach

O. Rios1, W. Jahn2, and G. Rein3 O. Rios et al.
  • 1Centre for Studies on Technological Risk (CERTEC), Universitat Politècnica de Catalunya, Av. Diagonal, 647, 08028 Barcelona, Spain
  • 2Departamento de Ingeniería Mecánica y Metalúrgica, Pontificia Universidad Católica de Chile, Santiago, Chile
  • 3Department of Mechanical Engineering, Imperial College London, SW72AZ, London, UK

Abstract. A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event) in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.

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