Euradwaste '08 - EU Bookshop - Europa

2. Uncertainties associated with the values of the parameters that are used in the implemented
models. They are variously termed “parameter,” or “data” uncertainties.
3. Uncertainties associated with the possible occurrence of features, events and processes (FEPs)
external to the disposal system that may impact the natural or engineered parts of the disposal
system over time. These are usually referred to as “scenario” or “system” uncertainties.
All three classes of uncertainty are related to each other, and particular uncertainties can be handled
in different ways, such that they might be dealt with in one class or another for any single iteration
of a PA/safety case, depending on programmatic decisions (e.g., on how to best to implement PA
calculations or to communicate results) and practical limitations (e.g., on funding or timescales).
The classification system for uncertainties given above essentially arises from the way the PA is
implemented, and says little about the nature of the uncertainties. With respect to nature, a useful
distinction can be made between epistemic and aleatory uncertainties. Epistemic uncertainties are
knowledge-based, and therefore, reducible by nature. Aleatory uncertainties, on the other hand, are
random in nature and are irreducible.
All three classes of uncertainty contain elements that are epistemic and aleatory, although it may be
generally true that “scenario” uncertainties contain a larger element of aleatory uncertainty than the
other two groups. To take an example, typically “parameter” uncertainties may arise for the following
reasons:
The parameter values have not been determined exactly. This type of uncertainty is largely epistemic
in quality, and can be reduced with further effort.
The models use single (or spatially averaged) values for parameters, derived from measurements
at discrete locations, whereas in reality there is continuous variation in parameter values
over space - as well as over time. This type of uncertainty is partly aleatory in quality and cannot
be reduced by further effort.
2.2 Dealing with Uncertainty in the Quantitative PA
2.2.1 Parameter uncertainty
Uncertainties associated with model parameter values can be treated conveniently within most computational
schemes. Common approaches to treating parameter value uncertainty include the following:
1. Setting probability distributions functions (PDFs) for parameter values, which are sampled during
the course of a probabilistic assessment.
2. Repeat deterministic calculations where individual parameter values are varied across a range of
likely or possible values, including deterministic calculations using values representing the best
understanding available (“best estimate”) to better understand the system, e.g., with regard to
sensitivities.
3. Deterministic calculations where deliberately pessimistic values of parameters are taken, producing
a “conservative” estimate of the value of receptor quantities in order to demonstrate
compliance with limits.
379