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Open AccessArticle

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

1
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
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University of Chinese Academy of Sciences, Beijing 100049, China
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Institute of Tibetan Plateau Research, Chinese Academy of Sciences. Beijing 100101, China
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CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China
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School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou 510275, China
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Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
7
Key Laboratory of Western China’s Environmental Systems (Ministry of Education), Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(7), 2584; https://doi.org/10.3390/su12072584
Received: 19 February 2020 / Revised: 14 March 2020 / Accepted: 16 March 2020 / Published: 25 March 2020
(This article belongs to the Special Issue Agroforestry and Ecosystem Regeneration)
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 CO2 to air CO2 ( 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. View Full-Text
Keywords: sensitivity analysis; flux-based ecosystem model; extended Fourier amplitude sensitivity test (EFAST); Howland forest; Markov chain Monte Carlo sensitivity analysis; flux-based ecosystem model; extended Fourier amplitude sensitivity test (EFAST); Howland forest; Markov chain Monte Carlo
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Ma, H.; Ma, C.; Li, X.; Yuan, W.; Liu, Z.; Zhu, G. Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation. Sustainability 2020, 12, 2584.

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