Abstract
Remote sensing (RS) evapotranspiration (ET) models are commonly used to estimate ET at a large scale. These models often include spatially varying parameters that need to be determined. Typically, these parameters are either held as constants or obtained from a pre-specified lookup table, potentially introducing bias into ET estimates. This study aimed to evaluate the feasibility of calibrating a RS ET model using Budyko-derived mean annual ET. We applied this approach to calibrate the single parameter of the Penman-Monteith-Leuning (PML) model. Instead of using the default Budyko-Fu parameter ω (ω=2.6), we developed a data-driven model that relates the basin-calibrated ω and its primary controls. Subsequently, we mapped the spatial pattern of ω across China. The calibrated PML model was evaluated using flux observations from 12 ChinaFlux sites and water balance-based ET estimates from 80 basins in China. Results indicate that the Budyko-based calibration significantly improved the performance of the PML model at both site and basin scales compared to the uncalibrated model. This study highlights the potential of leveraging the Budyko framework to constrain the parameter estimates of RS ET models, thereby enhancing the accuracy of large-scale ET estimates.
Published Version
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