Estimation of Evapotranspiration of Amazon Rainforest Using the Maximum Entropy Production Method

Energy budget of Amazonian forests has a large influence on regional and global climate, but relevant data are scarce. A novel energy partition method based on the maximum entropy production (MEP) theory is applied to simulate evapotranspiration in Amazonia. Using site-level eddy flux data, the MEP method shows high skill at the hourly, daily, and monthly scales. Consistent performance under different levels of land surface dryness is revealed, hinting that drought signal is appropriately resolved. The site-level MEP-based estimates outperform the estimates of the Moderate Resolution Imaging Spectroradiometer evapotranspiration product, which is commonly used for large-scale assessments. At the Amazon basin scale, the two series yield similar averages but exhibit spatial differences. The parameter parsimony and demonstrated skill of the MEP method make it an attractive approach for environments with diverse strategies of water flux control. Plain Language Summary Classical methods for estimating vapor flow from vegetated surfaces, known as the process of evapotranspiration, require input data on water vapor gradient, wind speed, and surface roughness as well as model parameters that are often difficult to obtain from in situ and remote sensing observations. The uncertainty of these variables can be especially large for the Amazon rainforest with high biodiversity, making it challenging to evaluate evapotranspiration in space and time. In this study, we assess the performance of a novel model that needs fewer input data and model parameters than classical methods. The model performance is validated through comparison with observations from nine sites across the Amazon basin and shows a better skill as compared to a commonly used evapotranspiration product. We also show that the model has a satisfactory performance in estimating evapotranspiration when satellite data are used, providing reasonable estimates at the scale of the entire Amazon basin.