Advancing surface fuel representation for operational wildfire spread modeling at Météo-France

Accurately predicting wildfire behavior at geographical-to-regional scales using coupled atmosphere-fire models has the potential to enhance the operational activities of Météo-France, which provides fire danger assessment in support of the French civil protection services. Current fire danger indices primarily rely on meteorological variables and do not include an explicit representation of biomass fuels, despite the fact that extreme wildfire events often result from the combined effect of atmospheric conditions and fuel state.

In this study, we investigate how to integrate a detailed representation of surface fuels into the coupled Meso-NH/BLAZE modeling system (Lac et al., 2018; Costes et al., 2021), by taking advantage of high-resolution vegetation modeling from the SURFEX land surface system (Masson et al., 2013) and by defining fuel models for the vegetation types of the ECOCLIMAP database (Faroux et al. 2013). This study focuses on the French Mediterranean area for two main reasons: i) this is a wildfire-prone area that has experienced intense fire activity in recent years and that is projected to face increased fire danger due to climate change in the next decades (Fargeon et al. 2020); and ii) it has been monitored for several decades by the ONF (French forest services) through a dense observational network, providing extensive measurements of Live Fuel Moisture Content (LFMC).

We implement the Rothermel heterogeneous rate-of-spread (ROS) formulation (Andrews, 2018) in the coupled atmosphere-fire model associated with dynamic fuel models (Scott and Burgan, 2005), in order to represent both dead and live components of the biomass fuels, and to dynamically transfer the herbaceous fuel load from live to dead components as a function of the LFMC to reproduce seasonal curing. We thus analyze the added value of including a live component of biomass fuels and the role of the LFMC in the ROS predictions. Preliminary results indicate that accounting for the live fuel component part of fuels generally reduces the simulated ROS, as higher live fuel content tends to inhibit combustion. Moreover, simulations using dynamic fuel models propagate less extensively than non-dynamic fuel models.

Beyond the explicit modeling of fire-fuel interactions, we also examine the Fire Weather Indices (FWI) based on the Canadian approach (Van Wagner et al., 1985) and adopted by Météo-France to assess meteorological fire danger. By analyzing their relationship with simulated ROS, we aim to establish a first quantitative link between fire danger indicators and physically-based fire behavior predictions.