Extracting Risk Insights from Performance Assessments for High-Level Radioactive Waste Repositories

Abstract

Performance assessments (PAs) are important sources of information for societal decisions in high-level radioactive waste (HLW) management, particularly in evaluating safety cases for proposed HLW repository development. Assessing risk from geologic repositories for HLW poses a significant challenge due to the uncertainties in modeling complex systems of such large temporal and spatial scales. Because of the extensive uncertainties, a typical safety case for a proposed HLW repository is comprised of PA results coupled with various defense-in-depth elements, such as the multibarrier requirement for repository design, and insights from supplementary analyses. This paper proposes an additional supplementary analysis, the Strategic Partitioning of Assumption Ranges and Consequences (SPARC), that could be used (a) in a safety case to help build confidence in a repository system and (b) to provide risk information for decisions on how to allocate resources for future research. The SPARC method extracts risk information from existing PAs and supporting databases by uncovering new information—namely, what sets of model parameter values taken together could produce substantially increased doses (SIDs) from the repository—and displays the results in SPARC trees. These sets of parameter values correspond to the failure scenarios of reactor probabilistic risk assessments. The SPARC method is applied to the proposed Yucca Mountain HLW repository, as a demonstrative example, and the results indicate that just one or a couple of the repository features working alone could “save” the repository from SIDs even in extremely challenging conditions. Such insights produced with the SPARC method could help significantly in focusing resources on future research to build confidence in the repository.