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ARS Home » Southeast Area » Oxford, Mississippi » National Sedimentation Laboratory » Watershed Physical Processes Research » Research » Publications at this Location » Publication #385467
Research Project: Computational Tools and a Decision Support System for Management of Sediment and Water Quality in Agricultural Watersheds

Location: Watershed Physical Processes Research

Title: Watershed-merging: A simple and effective algorithm for channel network identification and extraction

Author
item ZHANG, YAOXIN - University Of Mississippi
item JIA, YAFEI - University Of Mississippi

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/12/2020
Publication Date: 9/17/2020
Citation: Zhang, Y., Jia, Y. 2020. Watershed-merging: A simple and effective algorithm for channel network identification and extraction. Water Resources Research. 56.e2019WR026943. https://doi.org/10.1029/2019WR026943.
DOI: https://doi.org/10.1029/2019WR026943

Interpretive Summary: Channel network identi'cation is an important practice in not only hydrologic analysis but also hydraulic computation. In this study, a simple and effective algorithm for channel network extraction and identification from a DEM was developed. In this watershed-merging algorithm, the discontinuities caused by the depression and flat areas are considered as watershed-to-watershed connection. Correspondingly, the flow direction in those depression and flat areas is determined using information of the two neighboring watersheds, instead of the neighboring cells that the conventional algorithms use. This progressive merging process starts from small neighboring watersheds to larger ones. The example and applications provided in the study demonstrated that the proposed watershed-merging algorithm is effective in resolving depression and 'at areas problems and identifying channel networks.

Technical Abstract: Channel network identi'cation is an important practice in not only hydrologic analysis but also hydraulic computation. In this paper, a new algorithm, watershed merging, is proposed to automatically identify and extract channel networks. In the water-merging algorithm, based on the fact that the sink cell of a dendritic watershed is either a depression cell or a 'at cell, a macroscale approach is proposed to treat the depression and 'at areas (DAFA) and determine the 'ow direction within those areas, where the conventional D8 slope calculation fails. The separated neighboring watersheds are merged together using information of neighboring watersheds instead of the D8 cells. This progressive merging process starts from small neighboring watershed to larger ones. The example and applications demonstrated that the proposed watershed-merging algorithm is effective in resolving the DAFA problems and identifying channel networks.