  We develop a novel application of hybrid information divergences to analyze
uncertainty in steady-state subsurface flow problems. These hybrid information
divergences are non-intrusive, goal-oriented uncertainty quantification tools
that enable robust, data-informed predictions in support of critical decision
tasks such as regulatory assessment and risk management. We study the
propagation of model-form or epistemic uncertainty with numerical experiments
that demonstrate uncertainty quantification bounds for (i) parametric
sensitivity analysis and (ii) model misspecification due to sparse data.
Further, we make connections between the hybrid information divergences and
certain concentration inequalities that can be leveraged for efficient
computing and account for any available data through suitable statistical
quantities.
