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

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.