Statistical modelling of wildfire size and intensity: a step toward meteorological forecasting of summer extreme fire risk
- 1LMD/IPSL, CNRS and École Polytechnique, Université Paris-Saclay, Palaiseau, Paris, France
- 2Laboratoire de Mathématiques d'Orsay, Université Paris-Sud, Université Paris-Saclay, 91405 Orsay, France
- 3Inria Saclay-Île-de-France, 91405 Orsay, France
- 4LMD/IPSL, UPMC Univ. Paris 06, Sorbonne Universités, PSL Research University, CNRS, ENS, École Polytechnique, Université Paris-Saclay, Palaiseau, Paris, France
Abstract. In this article we investigate the use of statistical methods for wildfire risk assessment in the Mediterranean Basin using three meteorological covariates, the 2 m temperature anomaly, the 10 m wind speed and the January–June rainfall occurrence anomaly. We focus on two remotely sensed characteristic fire variables, the burnt area (BA) and the fire radiative power (FRP), which are good proxies for fire size and intensity respectively. Using the fire data we determine an adequate parametric distribution function which fits best the logarithm of BA and FRP. We reconstruct the conditional density function of both variables with respect to the chosen meteorological covariates. These conditional density functions for the size and intensity of a single event give information on fire risk and can be used for the estimation of conditional probabilities of exceeding certain thresholds. By analysing these probabilities we find two fire risk regimes different from each other at the 90 % confidence level: a "background" summer fire risk regime and an "extreme" additional fire risk regime, which corresponds to higher probability of occurrence of larger fire size or intensity associated with specific weather conditions. Such a statistical approach may be the ground for a future fire risk alert system.