Multi-objective parameter conditioning of a three-source wheat canopy model

A three-source canopy model, which distinguishes the energy budgets for sunlit and shaded leaves and the underlying soil surface, is applied within the generalised likelihood uncertainty estimation (GLUE) methodology for a site near Beijing, China. Parameter sensitivities and uncertainty bounds for CO2 and heat fluxes were analysed based on a multi-objective evaluation of Monte-Carlo realisations of model parameters. Two data sets acquired before and after an irrigation event in a wheat field were used to constrain the model. The results show that some of the six parameters varied are strongly conditioned by the observed fluxes, especially by the observations of CO2 flux above the canopy, but the scatter plots and cumulative distributions of parameter spaces are quite different between the two data sets. The predicted canopy photosynthesis rate demonstrates wider 95% uncertainty bounds than the latent and sensible heat fluxes. Comparison of model performances between two-source and three-source models shows that the parameter sensitivities are different and that the three-source model gives more constrained uncertainty bounds. Finally, a ‘best’ parameter set is used to estimate the energy budgets at the three sources. It is shown that the net radiation on shaded leaves is about 20% of the sunlit leaves, whereas the ratio is 50% for latent heat flux around noon. Hence, the shaded leaves are predicted as acting as sinks of sensible heat, reducing the predicted temperature differences between the two groups of leaves.

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