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Identifying Influences on Model Uncertainty: An Application Using a Forest Carbon Budget Model

Author

Listed:
  • James E. Smith

    (USDA Forest Service, Pacific Northwest Research Station, P.O. Box 3890, Portland, Oregon 97208, USA)

  • Linda S. Heath

    (USDA Forest Service, Northeastern Research Station, P.O. Box 640, Durham, New Hampshire 03824, USA)

Abstract

Uncertainty is an important consideration for both developers and users of environmental simulation models. Establishing quantitative estimates of uncertainty for deterministic models can be difficult when the underlying bases for such information are scarce. We demonstrate an application of probabilistic uncertainty analysis that provides for refinements in quantifying input uncertainty even with little information. Uncertainties in forest carbon budget projections were examined with Monte Carlo analyses of the model FORCARB. We identified model sensitivity to range, shape, and covariability among model probability density functions, even under conditions of limited initial information. Distributional forms of probabilities were not as important as covariability or ranges of values. Covariability among FORCARB model parameters emerged as a very influential component of uncertainty, especially for estimates of average annual carbon flux.

Suggested Citation

  • James E. Smith & Linda S. Heath, 2001. "Identifying Influences on Model Uncertainty: An Application Using a Forest Carbon Budget Model," Environmental Management, Springer, vol. 27(2), pages 253-267, February.
    • Handle: RePEc:spr:envman:v:27:y:2001:i:2:d:10.1007_s002670010147
    DOI: 10.1007/s002670010147
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