Stream network conflation with topographic DEMs. (April 2018)

• Record Type: Journal Article
• Authors: Yadav, Bidhyananda; Hatfield, Kirk
• Is Part Of: Environmental modelling & software. Volume 102(2018)
• Page Start: 241
• Page End: 249
• Publication Date: 2018-04
• Subjects: Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.01.009
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper presents DEM-Stream-Conflation (DSC) algorithm – a scale-independent robust technique of aligning vector streams with flowpaths dictated by raster DEMs. Designed as an alternative to both stream-burning and threshold-dependent stream segmentation techniques, DSC utilizes the existing vector flowlines to identify the channel heads and a sink filled hydrologically conditioned DEM to resolve the flowpaths. The algorithm conceptually initiates the movement of water on a DEM at the starting node of channel heads, from which it traces the path of water to its ultimate watershed outlet. Each trace represents a stream, which is in perfect alignment with the direction dictated by the raster DEM. The algorithm is tested with different DEMs, and its efficacy is demonstrated through the replication of the original vector drainage pattern, derivation of geomorphic attributes that are independent of tested DEM scale, and the visualization of monotonically decreasing longitudinal stream profiles. Highlights: An algorithm for aligning vector stream network with topographic DEM is presented. Avoids ambiguity of channel initiation threshold, and the pitfalls of stream-burning. Does not require any user-defined parameters for stream segmentation. Developed in Python using ArcPy and Numpy libraries. Fits seamlessly into existing catchment modeling framework.