Uncertainty and Sensitivity Analysis offer valid tools for characterizing the uncertainty associated with a model.

Uncertainty analysis ( UA ) quantifies the uncertainty in the outcome of a model.

Uncertainty arising from different sources — errors in the data, parameter estimation procedure, alternative model structures — are propagated through the model for uncertainty analysis and their relative importance is quantified via sensitivity analysis.

:* Uncertainty / deterministic nodes and incoming conditional arcs collectively model the information ( what are known and their probabilistic / deterministic relationships )

Uncertainty and errors within regional models are introduced by the global model used for the boundary conditions of the edge of the regional model, as well as errors attributable to the regional model itself.