Extending Monte Carlo Simulations to Represent and Propagate Uncertainties in Presence of Incomplete Knowledge: Application to the Transfer of a Radionuclide in the Environment

Abstract

This work is devoted to some recent developments in uncertainty analysis of environmental models in the presence of incomplete knowledge. The classical uncertainty methodology based on probabilistic modeling provides direct estimations of relevant statistical measures to quantify the uncertainty on the model responses thanks to a nice mixing between Monte Carlo simulations and the use of efficient statistical treatments. However, this approach may lead to unrealistic results when not enough information is available to specify the probability distribution functions (pdfs) of input parameters. For example, if a fixed (i.e., the pdf is a Dirac distribution) variable is unknown between