PNNL

Abstract

The development and application of a methodology to systematically and quantitatively assess predictive uncertainty in groundwater flow and transport modeling is described. The methodology considers the combined impact of hydrogeologic uncertainties associated with the conceptual-mathematical basis of a model, model parameters, and the scenario to which the model is applied. The methodology is based on an extension of a Maximum Likelihood implementation of Bayesian Model Averaging to include the impact of uncertainty in the future hydrologic scenario to which the models are applied. Scenario uncertainty is represented as a discrete set of alternative future conditions affecting boundary conditions, source/sink terms, or other aspects of the models. The associated prior scenario probabilities reflect a subjective belief about the relative plausibility of the alternative scenarios. A joint assessment of uncertainty results from combining model predictions computed under each scenario using as weights the posterior model and prior scenario probabilities. The computed model predictions incorporate parameter uncertainties using, for example, Monte Carlo simulation. An application of the uncertainty methodology to modeling of groundwater flow and uranium transport at the Hanford Site 300 Area is presented.