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	Variance-based sensitivity analysis of a forest growth model
	Song X. D. ; Bryan B. A. ; Paul K. I. ; Zhao G.
	2012
关键词	3-PG2 Sensitivity analysis Variance-based Elementary effects Group effect environmental-model productivity model low-rainfall 3-pg carbon plantations uncertainty australia climate parameterization
英文摘要	Computer models are increasingly used to simulate and predict the behaviour of forest systems. Uncertainties in both parameter calibration and outputs co-exist in these models due to both the incomplete understanding of the system under simulation, and biased model structure. We used sensitivity analysis, including both screening and global variance-based methods, to explore these uncertainties. We applied these techniques to the widely used forest growth model Physiological Principles for Predicting Growth (3-PG2) using field data from 141 plots of anymbia maculata and Eucalyptus cladocalyx in Australia. The screening method was used to select influential input parameters for the subsequent variance-based analysis and thereby reduce its computational cost. We assessed model outputs including biomass partitioning and water balance, and the sensitivities of the soil texture group, which includes 7 parameters. We also compared the screening and variance-based methods, and assessed the convergence of the variance-based method, and the change in sensitivities overtime. Using these techniques, we quantified the relative sensitivities of each model output to each input parameter. The variance-based method exhibited good convergence and stable sensitivity rankings. The results indicated changes in input parameter sensitivities over longer simulation periods. The variance-based global sensitivity analysis can be very effective in calibration and identification of important processes within forest models. (C) 2012 Elsevier B.V. All rights reserved.
出处	Ecological Modelling
卷	247
页	135-143
收录类别	SCI
语种	英语
ISSN号	0304-3800
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/30777]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Song X. D.,Bryan B. A.,Paul K. I.,et al. Variance-based sensitivity analysis of a forest growth model. 2012.

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