  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.
