资源环境科技发展态势分析平台

Input uncertainty and deficiencies in the key elements of hydrologic models are fundamental challenges to improving model performance. Evapotranspiration (ET) models are sensitive to energy imbalance and energy interaction between the canopy and surface, and these errors can bias model simulation. This paper presents a Bayesian framework that accounts for energy imbalance and energy interaction uncertainty in calibration of the Shuttleworth–Wallace (SW) model and discusses whether these efforts can improve the SW model performance. Specifically, the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm is used to analyze the energy imbalance error and energy interaction correction during SW model calibration, and Bayesian model evidence (BME) is used to scrutinize multiple hypotheses about alternative representations of energy. The results indicate that explicit treatment of the energy imbalance and conceptual corrections of the energy interaction significantly reduce the uncertainty of model parameters and provide energy corrections that are more appropriate and largely improve the SW model performance. Moreover, the optimal model selected by BME is mostly consistent with that evaluated using traditional statistical criteria (TSC) during a validation period. This result shows that the improved SW model also provides the best predictions, and thus consistent model selection should also be asymptotically efficient. The findings may allow improvement of ET model performance, and hence the accuracy of hydrologic and climatic simulations, toward fully understanding the key process underlying hydrologic and climatic variability and change over land.