Optimizing Parameter Estimation to Improve Evapotranspiration Calculation for Maize Fields

【Objective】 Evapotranspiration is an important factor in agricultural water management, but its calculation is not trial, especially in areas lacking weather stations where measured meteorological data are incomplete or unavailable. The purpose of this paper is to propose an optimal method to estimate parameters which cannot be measured directly but required for estimating evapotranspiration. 【Method】 The analysis was based meteorological data measured from weather stations at Daman in the basin of Hei River We took corn fields in the basin as an example and assumed latent heat flux and sensible heat flux were the parameters. Differential evolution adaptive algorithms were compared to optimize the parameters in the evapotranspiration model by introducing an energy-unclosed-factor to the multi-objective function in the parameter estimation. The model was built on the Bayesian inference with the values of the parameters calculated by the Markov chain Markov chain Monte Carlo method. Based on traditional indexes