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

活燃料水分含量 (LFMC) 是影响森林可燃性的主要因素之一，因为它决定了现有活燃料燃烧的可用性。目前，LFMC 通过光谱植被指数监测或从气象干旱指数推断。虽然有用，但这两种方法都没有提供对特定物种 LFMC 变异的机制见解，并且它们在未来气候变化下预测 LFMC 的能力有限。在这里，我们开发了一个半机械模型，通过调整估算黎明前叶片水势（Ψ _pd）。我们的首要目标是在提高模型适用性的生物真实性和可能限制其在操作环境中的价值的参数化复杂性之间进行权衡。校准后，模型预测得到了验证，该数据集包含西班牙半岛的 1659 个 LFMC 观测值，属于不同的功能类型和对比气候。我们的模型（ R ² = 0.5）的总体拟合优度 优于基于干旱指数（R ²  = 0.3）或光谱植被指数（R ²  = 0.1）的现有模型获得的拟合优度。我们观察到播种灌木（ R ² = 0.6）的最佳预测性能， 其次是树木（R ²  = 0.5) 和重新萌芽的灌木 ( R ²  = 0.4)。通过其相对简单的参数化，这里开发的方法可以为新一代基于过程的模型铺平道路，该模型可用于减轻火灾风险情景中的操作目的。