Experimental and Numerical Study of Vegetation Moisture Content on Wildfire Intensity: The Seasonal Effect

This study presents the Moisture Dynamic Model (MDM), a new semi-physical formulation designed to estimate Fuel Moisture Content (FMC) using only air temperature and relative humidity. The core innovation of this work lies in the introduction of an Arrhenius-type kinetic term into a fuel moisture prediction framework, allowing temperature-driven desorption processes to be explicitly represented within a lightweight operational model. Its predictive capability was assessed through experimental campaigns on Cistus monspeliensis shrublands in Corsica and validated using FireStar3D simulations. A second major contribution is the coupling of the MDM with the physical wildfire simulator FireStar3D to quantify how FMC prediction errors propagate into fire spread predictions. The MDM accurately reproduced the seasonal variability of FMC, achieving strong correlation with experimental data during dry summer periods. When coupled with FireStar3D, discrepancies in the predicted rate of spread remained below 4% under high-risk meteorological conditions. While the model performed robustly during summer, its accuracy decreased during spring, when rainfall events and microclimatic variability introduced greater uncertainty. This work represents a proof of concept demonstrating the potential of a simple physically interpretable FMC model for operational fire behaviour prediction.