DOI: 10.1016/j.foreco.2012.09.043
Scopus记录号: 2-s2.0-84870163680
论文题名: Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe
作者: van Oijen M. ; Reyer C. ; Bohn F.J. ; Cameron D.R. ; Deckmyn G. ; Flechsig M. ; Härkönen S. ; Hartig F. ; Huth A. ; Kiviste A. ; Lasch P. ; Mäkelä A. ; Mette T. ; Minunno F. ; Rammer W.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2013
卷: 289 起始页码: 255
结束页码: 268
语种: 英语
英文关键词: Dynamic modelling
; Forest management models
; Growth prediction
; National forest inventories
; Permanent sample plots
; Uncertainty
Scopus关键词: Austria
; Bayesian calibration
; Bayesian model
; Bayesian model averaging
; Belgium
; Bridging models
; Complex model
; Degree of uncertainty
; Estonia
; Finland
; Forest growth
; Forest models
; Generic parameters
; Intermediate complexity
; Inventory data
; Measurement uncertainty
; Model parameters
; Model performance
; Model prediction
; National forest inventories
; Parameter values
; Permanent sample plots
; Plausible model
; Posterior distributions
; Process-based models
; Relative probability
; Scots pine
; Scots pine stands
; Structural uncertainty
; Tree height
; Uncertainty
; Bayesian networks
; C (programming language)
; Calibration
; Dynamic models
; Forecasting
; Probability distributions
; Uncertainty analysis
; Forestry
; Bayesian analysis
; calibration
; comparative study
; coniferous forest
; forest inventory
; forestry modeling
; growth rate
; sampling
; uncertainty analysis
; Computer Programing
; Forest Management
; Growth
; Inventories
; Mathematical Models
; Probability
; Austria
; Belgium
; Estonia
; Finland
; Pinus sylvestris
英文摘要: Forest management requires prediction of forest growth, but there is no general agreement about which models best predict growth, how to quantify model parameters, and how to assess the uncertainty of model predictions. In this paper, we show how Bayesian calibration (BC), Bayesian model comparison (BMC) and Bayesian model averaging (BMA) can help address these issues.We used six models, ranging from simple parameter-sparse models to complex process-based models: 3PG, 4C, ANAFORE, BASFOR, BRIDGING and FORMIND. For each model, the initial degree of uncertainty about parameter values was expressed in a prior probability distribution. Inventory data for Scots pine on tree height and diameter, with estimates of measurement uncertainty, were assembled for twelve sites, from four countries: Austria, Belgium, Estonia and Finland. From each country, we used data from two sites of the National Forest Inventories (NFIs), and one Permanent Sample Plot (PSP). The models were calibrated using the NFI-data and tested against the PSP-data. Calibration was done both per country and for all countries simultaneously, thus yielding country-specific and generic parameter distributions. We assessed model performance by sampling from prior and posterior distributions and comparing the growth predictions of these samples to the observations at the PSPs.We found that BC reduced uncertainties strongly in all but the most complex model. Surprisingly, country-specific BC did not lead to clearly better within-country predictions than generic BC. BMC identified the BRIDGING model, which is of intermediate complexity, as the most plausible model before calibration, with 4C taking its place after calibration. In this BMC, model plausibility was quantified as the relative probability of a model being correct given the information in the PSP-data. We discuss how the method of model initialisation affects model performance. Finally, we show how BMA affords a robust way of predicting forest growth that accounts for both parametric and model structural uncertainty. © 2012 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66835
Appears in Collections: 影响、适应和脆弱性
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作者单位: Centre for Ecology and Hydrology, CEH-Edinburgh, Bush Estate, Penicuik EH26 0QB, United Kingdom; Potsdam Institute for Climate Impact Research, Telegrafenberg, P.O. Box 601203, Potsdam, Germany; UFZ - Helmholtz-Centre for Environmental Research, Department of Ecological Modeling, Permoserstr. 15, 04318 Leipzig, Germany; Plant and Vegetation Ecology, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk/Antwerpen, Belgium; Finnish Forest Research Institute, PL 68, FI-80101 Joensuu, Finland; Institute of Forestry and Rural Engineering, Estonian University of Life Sciences, Kreutzwaldi 5, 51014 Tartu, Estonia; Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 Helsinki, Finland; Forest Growth and Yield Science, Technical University of Munich, 85354 Freising, Germany; Institute of Agronomy, Forest Research Centre, Tapada da Ajuda, 1349-017 Lisbon, Portugal; Institute of Silviculture, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
Recommended Citation:
van Oijen M.,Reyer C.,Bohn F.J.,et al. Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe[J]. Forest Ecology and Management,2013-01-01,289