气候变化领域集成服务门户(CCPortal): Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation

CCPortal

DOI	10.3390/su12072584
	Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation
	Ma, Hanqing; Ma, Chunfeng; Li, Xin; Yuan, Wenping; Liu, Zhengjia; Zhu, Gaofeng
通讯作者	Ma, CF (通讯作者)
发表日期	2020
EISSN	2071-1050
卷号	12 期号:7
英文摘要	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(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.
关键词	GROSS PRIMARY PRODUCTION EDDY COVARIANCE MEASUREMENTS PARAMETER-ESTIMATION TERRESTRIAL INVERSION VARIABILITY EXCHANGE PHOTOSYNTHESIS IMPACT
英文关键词	sensitivity analysis; flux-based ecosystem model; extended Fourier amplitude sensitivity test (EFAST); Howland forest; Markov chain Monte Carlo
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS记录号	WOS:000531558100008
来源期刊	SUSTAINABILITY
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259955
推荐引用方式 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]. 中国科学院青藏高原研究所,2020,12(7).
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).
MLA	Ma, Hanqing,et al."Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation".SUSTAINABILITY 12.7(2020).