PNNL

Abstract

The reliability of and confidence in predictions from model simulations are crucial—these predictions can significantly affect risk assessment decisions. For example, the fate of contaminants at the U.S. Department of Energy’s Hanford Site has critical impacts on long-term waste management strategies. In the uncertainty estimation efforts for the Hanford Site-Wide Groundwater Modeling program, computational issues severely constrain both the number of uncertain parameters that can be considered and the degree of realism that can be included in the models. Substantial improvements in the overall efficiency of uncertainty analysis are needed to fully explore and quantify significant sources of uncertainty. We have combined state-of-the-art statistical and mathematical techniques in a unique iterative, limited sampling approach to efficiently quantify both local and global prediction uncertainties resulting from model input uncertainties. The approach is designed for application to widely diverse problems across multiple scientific domains. Results are presented for both an analytical model where the response surface is “known” and a simplified contaminant fate transport and groundwater flow model. The results show that our iterative method for approximating a response surface (for subsequent calculation of uncertainty estimates) of specified precision requires less computing time than traditional approaches based upon noniterative sampling methods.