A scalable high-performance topographic flow direction algorithm for hydrological information analysis

Kornelijus Survila, Ahmet Artu Yildirim, Ting Li, Yan Y. Liu, David G. Tarboton, Shaowen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hydrological information analyses based on Digital Eleva-tion Models (DEM) provide hydrological properties derived from high-resolution topographic data represented as an el-evation grid. Flow direction is one of the most computa-tionally intensive functions in the current implementation of TauDEM, a broadly used high-performance hydrological analysis software in hydrology community. Hydrologic flow direction defines a flow field on the DEM that directs flow from each grid cell to one or more of its neighbors. This is a local computation for the majority of grid cells, but becomes a global calculation for the geomorphologically motivated procedure in TauDEM to route flow across flat regions. As the resolution of DEM becomes higher, the computational bottleneck of this function hinders the use of these DEM data in large-scale studies. This paper presents an efficient parallel flow direction algorithm that identifies spatial fea-tures (e.g., flats) and reduces the number of sequential and parallel iterations needed to compute their geomorphologi-cally motivated flow direction. Numerical experiments show that our algorithm outperformed the existing parallel D8 algorithm in TauDEM by two orders of magnitude. The new parallel algorithm exhibited desirable scalability on Stam-pede and ROGER supercomputers.

Original languageEnglish (US)
Title of host publicationProceedings of XSEDE 2016
Subtitle of host publicationDiversity, Big Data, and Science at Scale
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450347556
DOIs
StatePublished - Jul 17 2016
EventConference on Diversity, Big Data, and Science at Scale, XSEDE 2016 - Miami, United States
Duration: Jul 17 2016Jul 21 2016

Publication series

NameACM International Conference Proceeding Series
Volume17-21-July-2016

Other

OtherConference on Diversity, Big Data, and Science at Scale, XSEDE 2016
CountryUnited States
CityMiami
Period7/17/167/21/16

Keywords

  • D8 flow algorithm
  • High-performance computing
  • Parallel flow direction assignment
  • Tau-DEM

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Survila, K., Yildirim, A. A., Li, T., Liu, Y. Y., Tarboton, D. G., & Wang, S. (2016). A scalable high-performance topographic flow direction algorithm for hydrological information analysis. In Proceedings of XSEDE 2016: Diversity, Big Data, and Science at Scale [a11] (ACM International Conference Proceeding Series; Vol. 17-21-July-2016). Association for Computing Machinery. https://doi.org/10.1145/2949550.2949571