Multi-spatial scale assessment and multi-dataset fusion of global terrestrial evapotranspiration datasets

Abstract

Evapotranspiration (ET) is an important component of the terrestrial water cycle, carbon cycle, and energy balance. Currently, there are four main types of ET datasets: remote sensing–based, machine learning–based, reanalysis–based, and land–surface–model–based. However, most existing ET fusion datasets rely on a single type of ET dataset, limiting their ability to effectively capture regional ET variations. This limitation hinders accurate quantification of the terrestrial water balance and understanding of climate change impacts. In this study, the accuracy and uncertainty of thirty ET datasets (across all four types) are evaluated at multiple spatial scales, and a fusion dataset BMA (Bayesian model averaging)-ET, is obtained using BMA method and dynamic weighting scheme. ET from FLUXNET2015 as reference, the study recommends remote sensing- and machine learning-based ET datasets, especially Model Tree Ensemble Evapotranspiration (MTE), Penman-Monteith-Leuning (PML) and Process-based Land Surface Evapotranspiration/Heat Fluxes (PLSH), but the optimal selection depends on season and vegetation type. At the basin scale, most of ET datasets demonstrate superior performance. Relative uncertainty based on remote sensing and machine learning is low at the grid point scale. The fusion dataset BMA-ET accurately captures trends in ET, showing a global terrestrial increasing trend of 0.65 (0.51–0.78) mm yr−1 during the period 1980–2020. BMA-ET has higher correlation coefficients and lower root-mean-square errors than most individual ET datasets. Validation using ET from FLUXNET2015 as reference shows that correlation coefficients of more than 70 % of the flux sites exceed 0.6. Validation results based on independent data sources show that the correlation coefficients of BMA-ET with AmeriFlux, ChinaFlux, and ICOS reach 0.61, 0.72, and 0.74, respectively. Overall, BMA-ET provides a comprehensive, long-term resource for understanding global ET patterns and trends, addressing the limitation of prior ET fusion efforts. Free access to the dataset can be found at https://doi.org/10.5281/zenodo.15470621 (Wu and Miao, 2025).