Publications

Development of a Risk-Based Probabilistic Performance-Assessment Method for Long-Term Cover Systems - 2nd Edition

Ho, Clifford K.; Arnold, Bill W.; Cochran, John R.

A probabilistic, risk-based performance-assessment methodology has been developed to assist designers, regulators, and stakeholders in the selection, design, and monitoring of long-term covers for contaminated subsurface sites. This report describes the method, the software tools that were developed, and an example that illustrates the probabilistic performance-assessment method using a repository site in Monticello, Utah. At the Monticello site, a long-term cover system is being used to isolate long-lived uranium mill tailings from the biosphere. Computer models were developed to simulate relevant features, events, and processes that include water flux through the cover, source-term release, vadose-zone transport, saturated-zone transport, gas transport, and exposure pathways. The component models were then integrated into a total-system performance-assessment model, and uncertainty distributions of important input parameters were constructed and sampled in a stochastic Monte Carlo analysis. Multiple realizations were simulated using the integrated model to produce cumulative distribution functions of the performance metrics, which were used to assess cover performance for both present- and long-term future conditions. Performance metrics for this study included the water percolation reaching the uranium mill tailings, radon gas flux at the surface, groundwater concentrations, and dose. Results from uncertainty analyses, sensitivity analyses, and alternative design comparisons are presented for each of the performance metrics. The benefits from this methodology include a quantification of uncertainty, the identification of parameters most important to performance (to prioritize site characterization and monitoring activities), and the ability to compare alternative designs using probabilistic evaluations of performance (for cost savings).