Incorporating uncertainty into the parameters of a forest process model

David W. MacFarlane, Edwin J. Green, Harry T. Valentine

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

'Process-based' models have been advanced to incorporate current knowledge regarding forest processes explicitly into model structure, yet uncertainty regarding these processes is often omitted from parameter estimation. This problem reflects the fact that parameters have been traditionally viewed as constants. In process models this is often unrealistic, since physiological rates and morphological characteristics, which have known variation, are often parametrized. Reasonable estimates for parameters can, and should be, abstracted from the vast body of forestry literature, and formulated into probability distributions which reflect uncertainty in their potential value. Here probability distributions are estimated for 14 physiological or morphological parameters of Pipestem, a stand-level model of carbon allocation and growth for loblolly pine (Pinus taeda), based on an extensive review of published information. Investigation of parameters revealed a wide range of variation in accumulated knowledge regarding their value, and led to the development of generic parameters which may be transferrable to other similar models. Parameter uncertainty also appeared tractable in some cases and might be reduced through reformulation of the model. Some parameters investigated had known co-dependency on model variables or other parameters, and may be better expressed as dependent variables. This study was part of a larger study in which a Bayesian analysis was used to assess the uncertainty in the predictions of a forest growth model. (C) 2000 Elsevier Science B.V.

Original languageEnglish (US)
Pages (from-to)27-40
Number of pages14
JournalEcological Modelling
Volume134
Issue number1
DOIs
StatePublished - Sep 30 2000

ASJC Scopus subject areas

  • Ecological Modeling

Keywords

  • Bayesian synthesis
  • Loblolly pine
  • Parameter estimation
  • Process-based model
  • Uncertainty

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