The ability to model freshwater stream habitat and species distributions is limited by the spatially sparse flow data available from long-term gauging stations. Flow data beyond the immediate vicinity of gauging stations would enhance our ability to explore and characterize hydrologic habitat suitability. The southeastern USA supports high aquatic biodiversity, but threats, such as landuse alteration, climate change, conflicting water-resource demands, and pollution, have led to the imperilment and legal protection of many species. The ability to distinguish suitable from unsuitable habitat conditions, including hydrologic suitability, is a key criterion for successful conservation and restoration of aquatic species. We used the example of the critically endangered Tar River Spinymussel (Parvaspina steinstansana) and associated species to demonstrate the value of modeled flow data (WaterFALL™) to generate novel insights into population structure and testable hypotheses regarding hydrologic suitability. With ordination models, we: 1) identified all catchments with potentially suitable hydrology, 2) identified 2 distinct hydrologic environments occupied by the Tar River Spinymussel, and 3) estimated greater hydrological habitat niche breadth of assumed surrogate species associates at the catchment scale. Our findings provide the first demonstrated application of complete, continuous, regional modeled hydrologic data to freshwater mussel distribution and management. This research highlights the utility of modeling and data-mining methods to facilitate further exploration and application of such modeled environmental conditions to inform aquatic species management. We conclude that such an approach can support landscape-scale management decisions that require spatial information at fine resolution (e.g., enhanced National Hydrology Dataset catchments) and broad extent (e.g., multiple river basins).