What is uncertainty?

Nature of uncertainty

Walker et al. (2003) explain that the Nature of uncertainty can be categorised into:

·        Epistemic uncertainty, i.e. the uncertainty due to imperfect knowledge.

·        Stochastic uncertainty, i.e. uncertainty due to inherent variability, e.g. climate variability.

Epistemic uncertainty is reducible by more studies: e.g. comprising research and data collection. Stochastic uncertainty is non-reducible.

Often the uncertainty on a certain event includes both epistemic and stochastic uncertainty. An example is the uncertainty of the 100 year flood at a given site. This flood event can be estimated: e.g. by use of standard flood frequency analysis on the basis of existing flow data. The (epistemic) uncertainty may be reduced by improving the data analysis, by making additional monitoring (longer time series) or by a deepening our understanding of how the modelled system works. Note that this does not imply that data collection and further research automatically leads to a reduction of epistemic uncertainty. More research sometimes increases epistemic uncertainty in the short term. For instance, the recognised epistemic ignorance has temporary increased if new data show that our mechanistic understanding of the system (as represented in a model structure) cannot be right and science has not yet discovered what mechanisms or internal system feedbacks, or other factors were overlooked. The epistemic uncertainty is reduced only after we have discovered what was wrong about our earlier mechanistic understanding of the system. However, no matter how perfect both the data collection and the mechanistic understanding of the system are, and, no matter for how long historical data time series exist, there will always be some (stochastic) uncertainty inherent to the natural system, related to the stochastic and chaotic nature of several natural phenomena, such as weather. Perfect knowledge on these phenomena cannot give us a deterministic prediction, but would have the form of a perfect characterisation of the natural variability; for example, a probability density function for rainfall in a month of the year.