Water quality impairments remain a pressing concern in the United States. Selecting appropriate management actions (i.e., best management practices) to improve water quality involves tradeoffs between cost and effectiveness, both of which are prone to uncertainty. In addition, significant uncertainties exist in the scientific understanding of the natural system. To address these concerns, we have developed a framework to identify the optimal set of actions to reduce turbidity and sedimentation in the Minnesota River Basin, while explicitly incorporating uncertainty. The framework combines Bayesian inference with multiobjective programming models to select the optimal combination of research actions, which improve our understanding [...]