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Sensitivity analysis and estimation using a hierarchical Bayesian method for the parameters of the FvCB biochemical photosynthetic model

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Abstract

Photosynthesis is a major process included in land surface models. Accurately estimating the parameters of the photosynthetic sub-models can greatly improve the ability of these models to accurately simulate the carbon cycle of terrestrial ecosystems. Here, we used a hierarchical Bayesian approach to fit the Farquhar–von Caemmerer–Berry model, which is based on the biochemistry of photosynthesis using 236 curves for the relationship between net CO2 assimilation and changes in the intercellular CO2 concentration. An advantage of the hierarchical Bayesian algorithm is that parameters can be estimated at multiple levels (plant, species, plant functional type, and population level) simultaneously. The parameters of the hierarchical strategy were based on the results of a sensitivity analysis. The Michaelis–Menten constant (Kc25), enthalpies of activation (EJ and EV), and two optical parameters (θ and α) demonstrated considerable variation at different levels, which suggests that this variation cannot be ignored. The maximum electron transport rate (Jmax25), maximum rate of Rubisco activity (Vcmax25), and dark respiration in the light (Rd25) were higher for broad-leaved plants than for needle-leaved plants. Comparison of the model’s simulated outputs with observed data showed strong and significant positive correlations, particularly when the model was parameterized at the plant level. In summary, our study is the first effort to combine sensitivity analysis and hierarchical Bayesian parameter estimation. The resulting realistic parameter distributions for the four levels provide a reference for current and future land surface models. Furthermore, the observed variation in the parameters will require attention when using photosynthetic parameters in future models.

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Acknowledgements

The research was funded by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA19040500), the National Natural Science Foundation of China (Nos. 41871078 and 41571016), National Key R & D Program of China (Grant No. 2016YFC0501002), and the Fundamental Research Funds for the Central Universities (Nos. 862851).

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  1. Key Laboratory of Western China’s Environmental Systems (Ministry of Education), Lanzhou University, Tianshui Road 222, Lanzhou, 730000, Gansu, People’s Republic of China

    Tuo Han, Gaofeng Zhu, Jinzhu Ma, Shangtao Wang, Kun Zhang, Xiaowen Liu, Ting Ma & Shasha Shang

  2. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China

    Tuo Han, Gaofeng Zhu, Jinzhu Ma, Shangtao Wang, Kun Zhang, Xiaowen Liu, Ting Ma & Shasha Shang

  3. Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 730000, China

    Chunlin Huang

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Han, T., Zhu, G., Ma, J. et al. Sensitivity analysis and estimation using a hierarchical Bayesian method for the parameters of the FvCB biochemical photosynthetic model. Photosynth Res 143, 45–66 (2020). https://doi.org/10.1007/s11120-019-00684-z

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