Spatiotemporal variation characteristics and multi-combination modeling assessment of fire weather index in Shanxi Province

This is the authors' abstract. We don't add key points for this paper.

Introduction Accurate estimation of fire-weather indices in complex mountainous terrain is limited by biases in reanalysis meteorology and incomplete representation of surface-moisture heterogeneity. Methods We developed a station-supervised framework that combines ground meteorological observations, ERA5-Land variables, terrain information, and microwave soil-moisture products from SMAP, AMSR2, and FY-3 sensors. XGBoost was used to estimate FFMC, DMC, and DC, after which ISI, BUI, and FWI were derived using the standard FWI equations. Three schemes were compared on a chronological validation set. Results The ERA5 + SMC + XGBoost scheme achieved R² values of 0.69, 0.63, 0.77, and 0.43 for FFMC, DMC, DC, and FWI, respectively, and improved overall agreement relative to the ERA5-driven baseline. Study-period mean patterns indicated higher fire-weather potential in central-southern and southwestern mountainous areas, with the highest seasonal potential from spring to early summer. Discussion Satellite-derived soil moisture improved regional agreement with station-derived targets for several FWI components, but spatial extrapolation to unsampled mountainous locations remains uncertain because no spatially independent or high-resolution dynamical validation was performed.