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Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation

Author

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  • Hanqing Ma

    (Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Chunfeng Ma

    (Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China)

  • Xin Li

    (Institute of Tibetan Plateau Research, Chinese Academy of Sciences. Beijing 100101, China
    CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China)

  • Wenping Yuan

    (School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou 510275, China)

  • Zhengjia Liu

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Gaofeng Zhu

    (Key Laboratory of Western China’s Environmental Systems (Ministry of Education), Lanzhou University, Lanzhou 730000, China)

Abstract

An ecosystem model serves as an important tool to understand the carbon cycle in the forest ecosystem. However, the sensitivities of parameters and uncertainties of the model outputs are not clearly understood. Parameter sensitivity analysis (SA) and uncertainty analysis (UA) play a crucial role in the improvement of forest gross primary productivity GPP simulation. This study presents a global SA based on an extended Fourier amplitude sensitivity test (EFAST) method to quantify the sensitivities of 16 parameters in the Flux-based ecosystem model (FBEM). To systematically evaluate the parameters’ sensitivities, various parameter ranges, different model outputs, temporal variations of parameters sensitivity index (SI) were comprehensively explored via three experiments. Based on the numerical experiments of SA, the UA experiments were designed and performed for parameter estimation based on a Markov chain Monte Carlo (MCMC) method. The ratio of internal CO 2 to air CO 2 ( f C i ) , canopy quantum efficiency of photon conversion ( α q ) , maximum carboxylation rate at 25 ° C ( V m 25 ) were the most sensitive parameters for the GPP. It was also indicated that α q , E V m and Q 10 were influenced by temperature throughout the entire growth stage. The result of parameter estimation of only using four sensitive parameters (RMSE = 1.657) is very close to that using all the parameters (RMSE = 1.496). The results of SA suggest that sensitive parameters, such as f c i , α q , E V m , V m 25 strongly influence on the forest GPP simulation, and the temporal characteristics of the parameters’ SI on GPP and NEE were changed in different growth. The sensitive parameters were a major source of uncertainty and parameter estimation based on the parameter SA could lead to desirable results without introducing too great uncertainties.

Suggested Citation

  • Hanqing Ma & Chunfeng Ma & Xin Li & Wenping Yuan & Zhengjia Liu & Gaofeng Zhu, 2020. "Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation," Sustainability, MDPI, vol. 12(7), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2584-:d:336690
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    References listed on IDEAS

    as
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    Cited by:

    1. Abbasian, Hassan & Solgi, Eisa & Mohsen Hosseini, Seyed & Hossein Kia, Seyed, 2022. "Modeling terrestrial net ecosystem exchange using machine learning techniques based on flux tower measurements," Ecological Modelling, Elsevier, vol. 466(C).

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