Self-Potential Responses to Tree Transpiration: Insights from a One-Year Dataset in a Mediterranean Climate

Plant transpiration is a critical component of the water cycle, and its quantification is essential for understanding terrestrial ecosystem dynamics. The self-potential (SP) method, a passive geophysical approach, presents a promising alternative for assessing transpiration rates, although the electrophysiological processes driving SP signals in trees remain underexplored. This study presents a year-long monitoring of SP and sap velocity in three tree species—Aleppo pine (Pinus halepensis Mill.), Holm oak (Quercus ilex L.), and Pubescent oak (Quercus pubescens)—across three Mediterranean study sites: Font-Blanche, LSBB, and Larzac. Using wavelet coherence analysis and variational mode decomposition, our findings reveal strong coherence between SP and sap velocity at diurnal time scales, with coherence diminishing and phase shifts increasing under higher water supply conditions. At the Font-Blanche site, correlation coefficients between diurnal SP and sap velocity variations in summer 2023 reached 0.91 for Aleppo pine and 0.77 for Holm oak. The estimated excess charge density of Aleppo pine and Holm oak sapwood, derived from linear regression between SP and sap velocity variations, ranges from 6.8 to 68.0 C·m^-3 throughout 2023, aligning with values typical of porous geological media. During dry seasons, the electrokinetic effect dominates SP signals, suggesting its potential as a tool for evaluating transpiration rates. This research demonstrates the potential value of integrating SP measurements into ecohydrological studies to better understand plant-water interactions.