Uncertainty analysis


Uncertainty is a general concept that reflects our lack of sureness about someone or something, ranging from just short of complete sureness to an almost complete lack of conviction about an outcome.

Uncertainty analysis deals with the different types of uncertainties associated with risk assessment and their sources. All these uncertainties can be divided into two main classes [*1]:

1. Natural variability refers to uncertainties associated with the inherent randomness of natural processes, that is:

  • annual maximum discharge
  • changes in river channel over time
  • hysteresis during a flood wave

Therefore the effectiveness of the flood protection system can varie from year to year. A system that eliminates all damage one year may not be resistant enough to eliminate all damage the next year. 


2. Knowledge uncertainty results from incomplete knowledge of the system under consideration and is related to the ability to understand, measure and/or describe the system. This kind of uncertainty can be further divided into:

  • model uncertainty, reflecting the inability of the simulation model to represent precisely the true physical behaviour of the system;
  • model parameter uncertainties that relates to the accuracy and precision with which parameters can be derived from field data, judgment, and the technical literature;
  • data uncertainties, which are the principal contributors to parameter uncertainty, including measurement errors, limited, non-representative or unavailable data (due to limitations in time, space, or financial means), data handling errors

To see a review of the sources of the knowledge uncertainties and their coresponding types, please click here.

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Uncertainty analysis



*1 Tung and Yen (1993)



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