Numerical uncertainty identification, classification and quantification in radioactive waste management

Abstract

Abstract. The work package “Uncertainty Management multi-Actor Network”, UMAN, within EURAD, the 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 programmes. One important goal there was to compile, review, compare and refine strategies, approaches and tools for the management of uncertainties in the safety assessment that are being used, planned to be used or being developed in different countries. This paper presents major findings from the UMAN Deliverable 10.3 “Uncertainty identification, classification and quantification” (Brendler and Pospiech, 2024) and reflects on the outcome of that work. It addresses approaches to identify and categorize uncertainties that might be of relevance in the various stages of radioactive waste management as well as in the quantification of numerical uncertainties. After an introduction, bottom-up and top-down strategies are compared. Sources used for the paper as input were expert elicitation (here primarily based on a respective questionnaire send out to UMAN participants) and literature surveys. This paper then advises on how uncertainties can be structured, rendering it suitable for a comprehensive assessment of numerical uncertainties: fishbone diagrams and tables for uncertainty characteristics. Results support the identification of uncertainties with high relevance for radioactive waste management. Nine potentially useful 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 Sub-themes of the EURAD Roadmap. The last part deals with the evaluation as well as quantification of uncertainties. The paper closes with recommendations aimed at future research directions for parameter uncertainties. Finally, a glossary provides definitions for some terms frequently used (uncertainty in general, parameter uncertainty, uncertainty models and aleatory vs. epistemic uncertainties).