A data model to store and retrieve surface watershed boundaries using graph theoretic approaches is proposed. This data model integrates output from a standard digital elevation models (DEM) derived stream catchment boundaries, and vector representation of stream centerlines then applies them to three novel algorithms. The first is called Modified Nested Set (MNS), which is a depth first graph traversal algorithm that searches across stream reaches (vertices) and stream junctions (edges) labeling vertices by their discovery time, finish time, and distance from the root. The second is called Log Reduced Graphs (LRG), which creates a set S of logarithmically reduced graphs from the original data, to store the watershed boundaries. The final algorithm is called Stitching Watershed, which provides a technique to merge watershed boundaries across the set of graphs created in the LRG algorithm. This technique was applied to the ~ 30,600 km2 Delaware River Watershed and compared to hypothetical data storage models in terms of prep-processing, data storage, and query complexity costs. Results show that the proposed technique provides significant benefits vs. the hypothetical methods with a 99-98% reduction in prepossessing, 96-80% reduction in query complexity and a 76% reduction in storage costs. The increasing availability of high resolution elevation data within the United States and the internationally provides an opportunity to extend these results to other watersheds through the world.