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Groundwater systems of different densities are often mathematically modeled to understand and predict environmental behavior such as seawater intrusion or submarine groundwater discharge. Additional data collection is justified if it will cost-effectively aid in reducing the uncertainty of a model s prediction. The collection of salinity or temperature data could aid in reducing predictive uncertainty in a variable-density model. Before numerical models can be created, rigorous testing of the modeling code needs to be completed. This research documents benchmark testing of SEAWAT. The benchmark problems include various combinations of density-dependent flow resulting from variations in concentration and temperature. SEAWAT was then applied to two different hydrological analyses to explore the capacity of a variable-density model to guide data collection. One analysis utilized a linear method to guide data collection to reduce predictive uncertainty in a nonlinear variable- density model. The other analysis used a numerical model to guide the collection of data related to quantification of submarine groundwater discharge.