Contrasting seasonal and ecohydrological controls on LFMC dynamics and implications for dynamic wildfire risk prediction in Aleppo pine

Understanding the drivers of live fuel moisture content (LFMC) is essential for predicting fire behaviour in water-limited Mediterranean forests, where recurrent droughts and rising atmospheric demand are intensifying wildfire risk. We investigated the spatio-temporal dynamics of LFMC and their relationships with ecohydrological processes (EHP) in a mature Pinus halepensis stand in eastern Spain, a region increasingly affected by megafires. Over a 17-month period, we monitored LFMC at predawn and midday across multiple tissue types (twigs, current-year and older needles, and woody tissue), alongside detailed measurements of soil moisture and potential, stem water content and sap-flow velocity at two heights, plant water potential, meteorological variables and fire-weather indices. LFMC exhibited low seasonal and diurnal variability, reflecting strong physiological regulation, while tissue age introduced marked differences, particularly between current-year and older needles. LFMC at predawn was primarily related to antecedent atmospheric demand (VPD, temperature, humidity) and fine dead-fuel moisture. Midday LFMC also responded to these drivers but showed inverse associations with antecedent sap-flow velocity measured at crown height, highlighting the role of transpiration dynamics controlling midday values. Multiple linear models using field variables provided acceptable LFMC estimates, whereas spectral indices alone showed limited predictive capacity. Our results demonstrate the value of a detailed pyro-ecohydrological monitoring in Aleppo pine forests from a technical perspective: significant LFMC measurement and estimation efforts seemed to be not supported by the low observed seasonal variation and the reduced control exerted by the most important ecohydrological processes on LFMC. We highly recommend wildfire prevention bodies to consider this aspect when dynamic wildfire risk predictions are developed in forests dominated by Aleppo pine.