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Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation
Ma, Hanqing1,2; Ma, Chunfeng1; Li, Xin3,4; Yuan, Wenping5; Liu, Zhengjia6; Zhu, Gaofeng7
2020-04-01
Source PublicationSUSTAINABILITY
Volume12Issue:7Pages:18
Corresponding AuthorMa, Chunfeng(machf@lzb.ac.cn)
AbstractAn 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(Ci)), canopy quantum efficiency of photon conversion (alpha(q)), maximum carboxylation rate at 25 degrees C (V-m(25)) were the most sensitive parameters for the GPP. It was also indicated that alpha(q), E-Vm 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(ci), alpha(q), E-Vm, 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.
Keywordsensitivity analysis flux-based ecosystem model extended Fourier amplitude sensitivity test (EFAST) Howland forest Markov chain Monte Carlo
DOI10.3390/su12072584
WOS KeywordGROSS PRIMARY PRODUCTION ; EDDY COVARIANCE MEASUREMENTS ; PARAMETER-ESTIMATION ; TERRESTRIAL ; INVERSION ; VARIABILITY ; EXCHANGE ; PHOTOSYNTHESIS ; IMPACT
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of the Chinese Academy of Sciences[XDA20100104] ; National Natural Science Foundation of China[91425303] ; CAS Light of West China Program
Funding OrganizationStrategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; CAS Light of West China Program
WOS Research AreaScience & Technology - Other Topics ; Environmental Sciences & Ecology
WOS SubjectGreen & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS IDWOS:000531558100008
PublisherMDPI
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/159497
Collection中国科学院地理科学与资源研究所
Corresponding AuthorMa, Chunfeng
Affiliation1.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Tibetan Plateau Res, Beijing 100101, Peoples R China
4.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China
5.Sun Yat Sen Univ, Sch Atmospher Sci, Guangzhou 510275, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
7.Lanzhou Univ, Minist Educ, Key Lab Western Chinas Environm Syst, Lanzhou 730000, Peoples R China
Recommended Citation
GB/T 7714
Ma, Hanqing,Ma, Chunfeng,Li, Xin,et al. Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation[J]. SUSTAINABILITY,2020,12(7):18.
APA Ma, Hanqing,Ma, Chunfeng,Li, Xin,Yuan, Wenping,Liu, Zhengjia,&Zhu, Gaofeng.(2020).Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation.SUSTAINABILITY,12(7),18.
MLA Ma, Hanqing,et al."Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation".SUSTAINABILITY 12.7(2020):18.
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