Differences in parameter estimates derived from various methods for the ORYZA (v3) Model |
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| Authors: | TAN Jun-wei DUAN Qing-yun GONG Wei DI Zhen-hua |
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| Affiliation: | 1 College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, P.R.China
2 College of Water Resources and Hydrology, Hohai University, Nanjing 210098, P.R.China
3 Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, P.R.China |
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| Abstract: | Parameter estimation is always a difficult issue for crop model users, and inaccurate parameter values will result in deceptive model predictions. Parameter values may vary with different inversion methods due to equifinality and differences in the estimating processes. Therefore, it is of great importance to evaluate the factors which may influence parameter estimates and to make a comparison of the current widely-used methods. In this study, three popular frequentist methods (SCE-UA, GA and PEST) and two Bayesian-based methods (GLUE and MCMC-AM) were applied to estimate nine cultivar parameters using the ORYZA (v3) Model. The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. Among the frequentist methods, the SCE-UA was recommended due to the balance between stable convergence and high efficiency. All the parameter estimates remarkably improved the goodness of model-fit, and the parameter estimates derived from the Bayesian-based methods had relatively worse performance compared to the frequentist methods. In particular, the parameter estimates with the highest probability density of posterior distributions derived from the MCMC-AM method (MCMC_Pmax) led to results equivalent to those derived from the frequentist methods, and even better in some situations. Additionally, model accuracy was greatly influenced by the values of phenology parameters in validation. |
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| Keywords: | parameter estimation frequentist method Bayesian method crop model calibration |
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