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Bayesian Melding of a Forest Ecosystem Model with Correlated Inputs

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Bayesian melding, a method for assessing uncertainties in deterministic simulation models, was augmented to make use of prior knowledge about correlations between model inputs. The augmentation involved the use of a nonparametric correlation induction algorithm. The modified Bayesian melding technique was applied to the process-based forest ecosystem computer model PnET-II. The Bayesian posterior distribution for this analysis did not reflect prior knowledge of input correlations for five input pairs tested unless the correlations were explicitly accounted for in the Bayesian prior distribution. For other input pairs not known to be correlated prior to the analysis, numerous significant posterior correlations were identified. For one such pair of model inputs, a moderate posterior correlation was substantiated by empirical evidence that had not previously been taken into consideration. We conclude that, when possible, efforts should be made to account for prior knowledge of correlated inputs; however, Bayesian melding may elucidate input correlations in its posterior sample, even when no prior knowledge of such correlations exists. FOR. SCI. 48(4):701–711.

Keywords: Bayesian melding; Latin hypercube; big leaf model; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; model evaluation; natural resource management; natural resources; process model; simulation

Document Type: Miscellaneous

Affiliations: 1: Assistant Professor Department of Forestry (0324), Virginia Tech, Blacksburg, VA, 24061, Phone: (540) 231-8863; Fax: (540) 31-3698 2: Professor Department of Forest Resources, University of Minnesota, 1530 Cleveland Ave N., St. Paul, MN, 55108, Phone: (612) 624-6741 3: Associate Professor Department of Forest Resources, University of Minnesota, 1530 Cleveland Ave N. St. Paul, MN, 55108, Phone: (612) 625-1703

Publication date: November 1, 2002

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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