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Subtropical forests have strong carbon sequestration potential; however, the spatiotemporal patterns of their carbon sink are unclear. The BIOME-BGC model is a powerful tool for forest carbon sink estimation while the numerous parameters, as well as the localization, limit their application. This study takes three typical subtropical forests (evergreen broadleaf forest, EBF; evergreen needleleaf forest, ENF; and bamboo forest, BF) in China as examples, assesses the sensitivity of 43 ecophysiological parameters in the BIOME-BGC model both by the Morris method and the extended Fourier amplitude sensitivity test (EFAST), and then evaluates the net ecosystem productivity (NEP) estimation accuracy based on the dataset of the fiveFi long-term carbon flux sites of those three typical forests from 2000 to 2015. The results showed that (1) both sensitivity analysis methods can effectively screen out important parameters affecting NEP simulation while the Morris method is more computationally efficient and the EFAST is better in the quantitative evaluation of sensitivity. (2) The highly sensitive parameters obtained using the two methods are basically the same; however, their importance varies across sites and vegetation types, e.g., the most sensitive parameters are k for the EBF and ENF and Ract25 for the BF, respectively. (3) The optimized parameters successfully improved the NEP simulation accuracy in subtropical forests, with average correlation coefficients increased by 25.19% and normalized root mean square error reduced by 21.74% compared with those simulated by original parameters. This study provides a theoretical basis for the optimization of process model parameters and important technical support for accurate NEP simulations of subtropical forest ecosystems.
Sun et al. (Mon,) studied this question.