Bayesian performance evaluation of evapotranspiration models based on eddy covariance systems in an arid region
Evapotranspiration (ET) is a major component of the land surface process involved in energy fluxes and energy balance, especially in the hydrological cycle of agricultural ecosystems. While many models have been developed as powerful tools to simulate ET, there is no agreement on which model best describes the loss of water to the atmosphere. This study focuses on two aspects, evaluating the performance of four widely used ET models and identifying parameters, and the physical mechanisms that have significant impacts on the model performance. The four tested models are the Shuttleworth–Wallace (SW) model, Penman–Monteith (PM) model, Priestley–Taylor and Flint–Childs (PT–FC) model, and advection–aridity (AA) model. By incorporating the mathematically rigorous thermodynamic integration algorithm, the Bayesian model evidence (BME) approach is adopted to select the optimal model with half-hourly ET observations obtained at a spring maize field in an arid region. Our results reveal that the SW model has the best performance, and the extinction coefficient is not merely partitioning the total available energy into the canopy and surface but also including the energy imbalance correction. The extinction coefficient is well constrained in the SW model and poorly constrained in the PM model but not considered in PT–FC and AA models. This is one of the main reasons that the SW model outperforms the other models. Meanwhile, the good fitting of SW model to observations can counterbalance its higher complexity. In addition, the detailed analysis of the discrepancies between observations and model simulations during the crop growth season indicate that explicit treatment of energy imbalance and energy interaction will be the primary way of further improving ET model performance.