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Earth Surface Dynamics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/esurf-2019-34
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-2019-34
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 17 Jun 2019

Submitted as: research article | 17 Jun 2019

Review status
A revised version of this preprint was accepted for the journal ESurf and is expected to appear here in due course.

Computing water flow through complex landscapes, Part 2: Finding hierarchies in depressions and morphological segmentations

Richard Barnes1,2,3, Kerry L. Callaghan4, and Andrew D. Wickert4,5 Richard Barnes et al.
  • 1Energy & Resources Group (ERG), University of California, Berkeley, USA
  • 2Electrical Engineering & Computer Science, University of California, Berkeley, USA
  • 3Berkeley Institute for Data Science (BIDS), University of California, Berkeley, USA
  • 4Department of Earth Sciences, University of Minnesota, Minneapolis, USA
  • 5Saint Anthony Falls Laboratory, University of Minnesota, Minneapolis, USA

Abstract. Depressions – inwardly-draining regions of digital elevation models – present difficulties for terrain analysis and hydrological modeling. Analogous depressions also arise in image processing and morphological segmentation where they may represent noise, features of interest, or both. Here we provide a new data structure – the depression hierarchy – that captures the full topologic and topographic complexity of depressions in a region. We treat depressions as networks, in a way that is analogous to surface-water flow paths, in which individual sub-depressions merge together to form meta-depressions in a process that continues until they begin to drain externally. The hierarchy can be used to selectively fill or breach depressions, or to accelerate dynamic models of hydrological flow. Complete, well-commented, open-source code and correctness tests are available on Github and Zenodo.

Richard Barnes et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Richard Barnes et al.

Model code and software

Source Code R. Barnes and K. Callaghan https://doi.org/10.5281/zenodo.3238558

Richard Barnes et al.

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Latest update: 28 Mar 2020
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Short summary
Maps of elevation are used to help predict the flow of water so we can better understand landslides, floods, and global climate change. However, modeling the flow of water is difficult when elevation maps include swamps, lakes, and other depressions. This paper explains a new method which overcomes these difficulties, allowing models to run faster and more accurately.
Maps of elevation are used to help predict the flow of water so we can better understand...
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