A Framework for Dealing With Uncertainty due to Model Structure Error

Although uncertainty about structures of environmental models (conceptual uncertainty) has been recognised often to be the main source of uncertainty in model predictions, it is rarely considered in environmental modelling. Rather, formal uncertainty analyses have traditionally focused on model parameters and input data as the principal source of uncertainty in model predictions. The traditional approach to model uncertainty analysis that considers only a single conceptual model, fails to adequately sample the relevant space of plausible models. As such, it is prone to modelling bias and underestimation of model uncertainty. In this paper we review a range of strategies for assessing structural uncertainties. The existing strategies fall into two categories depending on whether field data are available for the variable of interest. Most research attention has until now been devoted to situations, where model structure uncertainties can be assessed directly on the basis of field data. This corresponds to a situation of `interpolation'. However, in many cases environmental models are used for `extrapolation' beyond the situation and the field data available for calibration. A framework is presented for assessing the predictive uncertainties of environmental models used for extrapolation. The key elements are the use of alternative conceptual models and assessment of their pedigree and the adequacy of the samples of conceptual models to represent the space of plausible models by expert elicitation. Keywords: model error, model structure, conceptual uncertainty, scenario analysis, pedigree