Much progress has been made in the standardization of uncertainty analysis techniques for simulation modeling but less progress has been made in optimization modeling. Among the various techniques used for optimization modeling under uncertainty, robust optimization (RO) uniquely allows for evaluation and control of the various risks of poor system performance resulting from input parameter uncertainties in water-resources problems. A model formulation was developed that addresses an inadequacy in a previous RO formulation. The importance of evaluating, through postprocessing, RO model results with respect to a range of performance metrics, has been demonstrated rather than a single metric, as has been common in previous studies. An analysis of the tradeoffs between solution robustness (nearness to optimality across all scenarios) and feasibility robustness (nearness to feasibility across all scenarios) illustrates the importance of including these terms in multiobjective water resources decision models.