Lake evaporation is an important link connecting the water cycle and the surface energy cycle and remains one of the most uncertain terms in the local catchment’s water balance. Quantifying lake evaporation and its variability is crucial to improve water resource management and understand the response of the lake system towards climate change. In this study, we evaluated the performances of nine evaporation methods at different timescales and calibrated them by using the continuous eddy covariance (EC) observation data during 2015–2018 over Erhai Lake, a highland open freshwater lake situated in the Dali valley, China. The nine evaporation methods could be classified into combination methods (Bowen-ratio energy budget, Penman, Priestley–Taylor, DeBruin–Keijman and Brutsaert–Stricker), solar radiation-based methods (Jensen–Haise and Makkink) and Dalton-based method (mass transfer and Ryan–Harleman) based on their parameterization schemes. The Dalton-based Ryan–Harleman method is most suitable for estimating evaporation at daily to weekly scales, while the combination methods and solar radiation-based method had good estimates at monthly timescale. After calibration, the biases of the Jensen–Haise and Ryan–Harleman method were slightly reduced, while the biases of the Makkink and mass transfer methods were reduced substantially. The calibrated Jensen–Haise method with small annual bias (−2.2~2.8%) and simple input variables was applied to estimate the long-term trend of evaporation during 1981–2018. The annual total evaporation showed an insignificant increasing trend of 0.30 mm year−1,
mainly caused by the significant rising air temperature. This study showed the performance of evaporation methods over water bodies had large discrepancies on different time scales, which indicated the importance of the choice of evaporation methods and provided instruction for water resource management of this region under climate change.
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