Scenario and parametric uncertainty in GESAMAC: A methodological study in nuclear waste disposal risk assessment

Abstract

We examine a conceptual framework for accounting for all sources of uncertainty in complex prediction problems, involving six ingredients: past data, future observables, and scenario, structural, parametric, and predictive uncertainty. We apply this framework to nuclear waste disposal using a computer simulation environment — GTMCHEM — which “deterministically” models the one-dimensional migration of radionuclides through the geosphere up to the biosphere. Focusing on scenario and parametric uncertainty, we show that mean predicted maximum doses to humans on the earth's surface due to 1–129, and uncertainty bands around those predictions, are larger when scenario uncertainty is properly assessed and propagated. We also illustrate the value of a new method for global sensitivity analysis of model output called extended FAST.