Numerical uncertainty identification, classification and quantification in radioactive waste management

Abstract. The work package “Uncertainty Management multi-Actor Network – UMAN” within EURAD European Joint Programme on Radioactive Waste Management was dedicated to the management of uncertainties potentially relevant to the safety of different radioactive waste management stages and programs. One important goal there was to compile, review, compare and refine strategies, approaches and tools for the management of uncertainties in the safety analysis and the safety case that are being used, planned to be used or being developed in different countries. This paper presents major findings from the UMAN deliverable D10.3 "Uncertainty identification, classification and quantification" that addresses approaches to identify and classify uncertainties that might be of relevance in the various stages of radioactive waste management as well as on the quantification of numerical uncertainties. The section on methodology compares Bottom-up and Top-down strategies, describes which sources were used for the report as input: expert elicitation (here primarily based on a respective questionnaire send out to UMAN participants) and literature survey. It then advices on how uncertainties can be structured to pave the way to a comprehensive assessment of numerical uncertainties: fishbone diagrams and tables for uncertainty characteristics. Results support the identification of uncertainties with high relevance for RWM. Nine suitable categories are identified; the uncertainties are then grouped (including representative examples utilizing fishbone diagrams and tables) according to the occurrence by system phenomena, following the themes and subthemes of the EURAD Roadmap. The last part is treating with the evaluation as well as quantification of uncertainties. The paper closes with recommendations aimed at future research directions for parameter uncertainties. Finally, it provides definitions for some terms frequently used (uncertainty in general, parameter uncertainty, uncertainty models, and aleatory vs. epistemic uncertainties) in a glossary.