2.2 Reducible and Irreducible Uncertainty

Uncertainty associated with model formulation and application can also be classified as ``reducible'' and ``irreducible''. Natural uncertainty is ``inherent'' or irreducible, whereas data and model uncertainty contain both reducible and irreducible components. The irreducible uncertainty in data and models is generally a result of the presence of natural uncertainty. Reducible uncertainty can be lowered, e.g., by better inventorying methods, improved instrumentation, improvements in model formulation, etc. Nevertheless, the distinction between reducible and irreducible model and data uncertainties is to a great extent a matter of convention since it may not be feasible to eliminate the presence of an error (reducible uncertainty) in measurement or modeling beyond a certain level. Furthermore, what is perceived as irreducible natural uncertainty may be quantified in a statistical sense, and via mechanistic modeling, better than ``artificial'' reducible uncertainty. Irreducible modeling uncertainty reflects the ``current'' model formulation and may actually change when improved theories describing the phenomena under consideration become available. Also, the averaging processes involved in model formulation unavoidably ``lump'' together natural and modeling uncertainty and only a quantification of this lumped uncertainty may be possible or desirable. Figure 2.1 depicts schematically the types of uncertainties present in transport-transformation models, and their interrelationships.