| Summary: | The two-layer Shuttleworth–Wallace (SW) evapotranspiration (<i>ET</i>) model has been widely used for predicting <i>ET</i> with good results. Since the SW model has a large number of specific parameters, these parameters have been estimated using a simple non-hierarchical Bayesian (SB) approach. To further improve the performance of the SW model, we aimed to assess parameter estimation using a two-level hierarchical Bayesian (HB) approach that takes into account the variation in observed conditions through the comparison with a traditional one-layer Penman–Monteith (PM) model. The difference between the SB and HB approaches were evaluated using a field-based <i>ET</i> dataset collected from five agricultural fields over three seasons in Myanmar. For a calibration period with large variation in environmental factors, the models with parameters calibrated by the HB approach showed better fitting to observed <i>ET</i> than that with parameters estimated using the SB approach, indicating the potential importance of accounting for seasonal fluctuations and variation in crop growth stages. The validation of parameter estimation showed that the <i>ET</i> estimation of the SW model with calibrated parameters was superior to that of the PM model, and the SW model provided acceptable estimations of <i>ET</i>, with little difference between the SB and HB approaches.
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