Error characterization of global land evapotranspiration products: Collocation-based approach

Abstract

• Five collocation analysis methods are employed for the evaluation of global evapotranspiration products. • The validation based on in-situ observations reveals the promising potential of collocation methods for the evaluation of ET products. • IVD, EIVD and QC are the three most preferred collocation algorithms. • Among all products, ERA5 and GLEAM outperform PMLV2, FLUXCOM and GLDAS with relatively low uncertainty. Evapotranspiration (ET) is one of the key elements linking Earth’s water-carbon system. Accurate estimation of global land evapotranspiration is essential for understanding land–atmosphere interactions under a changing climate. However, due to a lack of observations at the global scale, inherent uncertainties limit the direct use of these data. In this study, we employed collocation analysis methods, including single and double instrumental variable algorithms (IVS/IVD), triple collocation (TC), quadruple collocation (QC) and extended double instrumental variable algorithms (EIVD) to evaluate five widely used ET products at 0.1° and 0.25° resolutions over daily and 8-day frequencies. To validate the reliability of collocation methods, the collocation analysis results were compared with evaluations based on in-situ observations. The results exhibited reasonably high accuracy with an average correlation of determination ( R 2 ) of 0.71 for all methods. In addition, IVD, EIVD and QC demonstrated better performances than other methods. In general, the ERA5 and GLEAM products showed lower uncertainty than the other products over 0.1° and 0.25°, respectively. Although the error resulting from nonzero error cross-correlation (ECC) should be considered, the ECC results from EIVD and QC revealed that this influence was acceptable in our study. Overall, this study presented a comprehensive application and comparison of all collocation analysis methods for error characterization of ET products. The findings suggested that collocation analysis methods could be reliable tools to serve as alternatives for tower observations at the global scale, which could be helpful for further data assimilation and merging.