Process based models have been widely used for assessing forest above ground carbon content and fluxes, as they enable deeper insights into the drivers of forest production and fluxes while providing higher flexibility than conventional production tables. However, in spite of the numerous models that exist, few have reached an operational status beyond that of the research realm. The lack of data and knowledge about their reliability has hampered their practical use. In this paper, we present a Bayesian calibration as a solution to this setback, where the parameters and the data used in the calibration process are presented in the form of probability distributions, reflecting our degree of certainty about them. As further information is gained, the distributions are updated. In using this approach, the presentation of uncertainties, over the derivation of an optimised set of parameter based on a goodness-of-fit approach is advocated. The approach is tested on the 3-PG model for the estimation of above ground carbon stocks of UK Corsicants pine forests. The results show the ability of the approach to produce model outputs and parameter uncertainty distributions.
|Journal||USDA Forest Service - General Technical Report PNW|
|Publication status||Published - 1 Nov 2006|