Ecosystem transpiration (T) is the core process in terrestrial water and carbon cycles. Accurately estimating T is critical to improving evapotranspiration (ET) models and understanding global ecosystem responses to climate change. In this study, we evaluated four ET partitioning methods (TEA, Z16, L19, and Y21) using 368 global eddy covariance (EC) sites and 15 sap flow sites. Intercomparison results showed that TEA, Z16, and Y21 maintained good consistency, whereas L19 exhibited lower agreement, primarily due to its high sensitivity to energy closure errors and poor non-linear fitting accuracy under extreme conditions. Validation against sap flow data indicated that Z16 performed best (R2 = 0.45, KGE = 0.52), followed by Y21, while TEA had the lowest accuracy due to systematic overestimation driven by unremoved persistent background soil evaporation in its training dataset. Global analysis revealed that mean annual T ranged from 213 mm yr−1 (Z16) to 294 mm yr−1 (TEA), with annual T/ET varying between 0.45 (Z16) and 0.63 (TEA). Trend analysis further showed consistent increasing trends across all four methods for both annual T (0.33–0.83 mm·yr−2) and annual T/ET (0.0015–0.0019 yr−1). Additionally, a notably stronger relationship was found between gross primary productivity (GPP) and T than between GPP and ET. Despite substantial differences in model structures, these methods effectively capture the temporal dynamics of T and the coupled relationships between ecosystem carbon and water fluxes. Our findings provide critical benchmarks for terrestrial water cycle modeling and sustainable water resource management under a changing climate.
Wang et al. (Thu,) studied this question.