Development of a data model to facilitate rapid Watershed Delineation
Scott Haag, Ali Shokoufandeh

TL;DR
This paper introduces a novel graph-based data model and algorithms for rapid watershed delineation, significantly reducing preprocessing, storage, and query costs compared to traditional methods.
Contribution
The paper presents three innovative algorithms—Modified Nested Set, Log Reduced Graphs, and Stitching Watershed—that enhance watershed boundary storage and retrieval efficiency.
Findings
Achieved 99-98% reduction in preprocessing time.
Reduced query complexity by 96-80%.
Lowered storage costs by 76%.
Abstract
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…
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Taxonomy
TopicsHydrology and Watershed Management Studies · Environmental Monitoring and Data Management · Soil and Water Nutrient Dynamics
