Efficient Delineation of Nested Depression Hierarchy in Digital Elevation Models for Hydrological Analysis Using Level‐Set Method

摘要: In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts thus filled removed to create depressionless DEM. Various algorithms have been developed identify fill DEMs during the past decades. However, few studies attempted delineate quantify nested hierarchy of actual depressions, which can provide crucial information for characterizing hydrologic connectivity simulating fill-merge-spill process. this paper, we present an innovative efficient algorithm delineating quantifying using level-set method based on graph theory. The proposed emulates water level decreasing from spill point along depression boundary lowest at bottom depression. By tracing dynamic topological changes (i.e., splitting/merging) within compound depression, construct graphs derive geometric properties depressions. experimental results two fine-resolution Light Detection Ranging-derived show that raster-based is much more (~150 times faster) than vector-based contour tree method. has great potential being applied large-scale ecohydrological watershed modeling.