A semi-mechanistic model for predicting daily variations in species-level live fuel moisture content

Live Fuel Moisture Content (LFMC) is one of the main factors affecting forest ignitability as it determines availability existing live fuel to burn. Currently, LFMC monitored through spectral vegetation indices or inferred from meteorological drought indices. While useful, neither approach provides mechanistic insights into species-specific variation and they are limited in ability forecast under altered future climates. Here, we developed a semi-mechanistic model predict daily across woody species different functional types by adjusting soil water balance which estimates predawn leaf potential (?pd). Our overarching goal was trade-off between biological realism, enhances applicability, parameterization complexity, may limit its value within operational settings. After calibration, predictions were validated against dataset comprising 1659 observations peninsular Spain, belonging contrasting The overall goodness fit for our (R2 = 0.5) better than that obtained an models based on 0.3) 0.1). We observed best predictive performance seeding shrubs 0.6) followed trees resprouting 0.4). Through relatively simple parameterization, here pave way new generation process-based can be used purposes fire risk mitigation scenarios.