RichDEM: High-performance terrain analysis
- Published
- Accepted
- Subject Areas
- Computational Science, Spatial and Geographic Information Science
- Keywords
- algorithm, parallel computing, high-performance computing, terrain analysis, raster, graph theory, open source, flow accumulation, depression-filling, hydrological modeling
- Copyright
- © 2018 Barnes
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. RichDEM: High-performance terrain analysis. PeerJ Preprints 6:e27099v1 https://doi.org/10.7287/peerj.preprints.27099v1
Abstract
To answer geomorphological questions at unprecedented spatial and temporal scales, we need to (a) parse terabyte-scale datasets (DEMs), (b) perform millions of model realizations to pinpoint the parameters which govern landscape evolution, and (c) do so with statistical rigor, which may require thousands of additional realizations. A core set of operations underpin many geomorphic models. These include determination of terrain attributes such as slope and curvature; flow routing; depression flooding and breaching; flat resolution; and flow accumulation. Here, I present RichDEM, a high-performance C++ library and set of wrappers for performing these operations. The library incorporates a number of options for performing each operation and makes full use of modern high-performance capabilities. The library can scale to process DEMs of over one trillion cells and operates effectively on laptops or supercomputers.
Author Comment
This paper was presented at the Geomorphometry 2018 conference in Boulder, CO in August 2018.