An improved canopy transpiration model and parameter uncertainty analysis by Bayesian approach

In this paper, an improved canopy transpiration (Ec) model that considered the unidirectional influence of soil evaporation on Ec was presented by extending Penman-Monteith model for increasing accuracy of modelling in sub-humid regions, and a Bayesian approach was used to fit the transpiration model to half-hourly transpiration rates for the 14-year-old cherry (Prunus avium L.) orchard collected over 4-month period and probabilistically estimated its parameters and prediction uncertainties. The probabilistic model was extended by adding a normally distributed error term, and the Markov chain Monte Carlo simulation method was used to determine the posterior parameter distributions. Seasonal variation of the Ec was analyzed by the experiments of Sap Flow method in Sijiqing Orchard in Beijing, north of China. The result showed there were larger uncertainties of the parameter and transpiration. The average value of parameters was used for the model, and long series data from simulated value of the model were compared with the measured data, and it showed that the improved transpiration model possessed high accuracy.