Benchmarking large-scale evapotranspiration estimates: A perspective from a calibration-free complementary relationship approach and FLUXCOM

Abstract

Accurately quantifying large-scale terrestrial evapotranspiration (ET) remains hampered by poor parameterization of the physical processes that relate to ET. Previous studies suggested that the calibration-free complementary relationship (CR) method that requires only routine meteorological data performed better than main-stream atmospheric reanalyses, land surface or remote sensing models in estimating large-scale ET. Here we simultaneously evaluate the latest machine learning-based upscaling of eddy-covariance measurements (FLUXCOM) and the CR estimates against the water-balance derived ET rates of 18 large Hydrologic Unit Code-2 (HUC2) and 327 medium HUC6 basins across the conterminous United States. Overall, CR and FLUXCOM perform comparably in representing the multiyear mean and temporal variations in annual ET at both, HUC2 and HUC6, scales for the 1979–2013 period. Such equally good skills also hold true for the 2003–2015 period, during which FLUXCOM was driven solely by remote sensing data. However, the CR generally captures the long-term linear tendencies in annual ET rates somewhat better than FLUXCOM. Because of its minimal data requirement, the calibration-free version of the CR may continue to serve as a benchmarking tool for large-scale ET simulations.