Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe

FOREST ECOLOGY AND MANAGEMENT

卷:289

Article

van Oijen, M.¹  Reyer, C.²  Bohn, F. J.³  Cameron, D. R.¹  Deckmyn, G.⁴  Flechsig, M.²  Harkonen, S.⁵  Hartig, F.³  Huth, A.³  Kiviste, A.⁶  Lasch, P.²  Makela, A.⁷  Mette, T.⁸  Minunno, F.⁹  Rammer, W.¹⁰ 

[1] CEH Edinburgh, Ctr Ecol & Hydrol, Penicuik EH26 0QB, Midlothian, Scotland

[2] Potsdam Inst Climate Impact Res, Potsdam, Germany

[3] UFZ Helmholtz Ctr Environm Res, Dept Ecol Modeling, D-04318 Leipzig, Germany

[4] Univ Antwerp, B-2610 Antwerp, Belgium

[5] Finnish Forest Res Inst, FI-80101 Joensuu, Finland

[6] Estonian Univ Life Sci, Inst Forestry & Rural Engn, EE-51014 Tartu, Estonia

[7] Univ Helsinki, Dept Forest Sci, FI-00014 Helsinki, Finland

[8] Tech Univ Munich, D-85354 Freising Weihenstephan, Germany

[9] Forest Res Ctr, Inst Agron, P-1349017 Lisbon, Portugal

[10] Univ Nat Resources & Life Sci BOKU, Inst Silviculture, Vienna, Austria

关键词: Dynamic modelling;    Forest management models;    Growth prediction;    National forest inventories;    Permanent sample plots;    Uncertainty;   

DOI  :  10.1016/j.foreco.2012.09.043

来源: Elsevier

【 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. (C) 2012 Elsevier B.V. All rights reserved.

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