PNNL

Abstract

Groundwater systems are complex, exhibiting significant heterogeneity across multiple scales, multiple interacting processes, and nonlinear behaviors. Groundwater systems are also open, often with poorly defined boundaries and time-dependent, uncertain boundary forcings (e.g., recharge). As a consequence, a groundwater model cannot be expected to precisely reflect the system it represents and the model results are uncertain quantities. The dominant language of uncertainty is probability and the majority of research in the treatment of uncertainty in groundwater modeling has been directed at developing and improving probabilistic methods for these models. One area of recent research has been methods for assessing the impact of model uncertainty in groundwater analyses. This line of research is part of a long-term trend of increasing appreciation for the importance of providing estimates of uncertainty in groundwater model outputs. The research was motivated by observations of nonuniqueness in model parameterizations, the sensitivity of groundwater model output to model structural choices, and a history of practice that established the importance of iterative improvement in the conceptual basis of groundwater models. Among the methods proposed for assessing model uncertainty, we discuss Bayesian model averaging, it’s theoretical basis and research development. We outline critical arguments and discuss alternative approaches. Finally, we briefly describe a regulatory application of groundwater modeling that used Bayesian model averaging concepts to resolve a key conceptual model uncertainty.