Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 24, issue 9
Articles | Volume 24, issue 9
https://doi.org/10.5194/hess-24-4389-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-24-4389-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 9
Research article
 | 
09 Sep 2020
Research article |  | 09 Sep 2020

Adaptive clustering: reducing the computational costs of distributed (hydrological) modelling by exploiting time-variable similarity among model elements

Uwe Ehret, Rik van Pruijssen, Marina Bortoli, Ralf Loritz, Elnaz Azmi, and Erwin Zehe

Viewed

Total article views: 3,408 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,255 1,082 71 3,408 101 128
  • HTML: 2,255
  • PDF: 1,082
  • XML: 71
  • Total: 3,408
  • BibTeX: 101
  • EndNote: 128
Views and downloads (calculated since 28 Feb 2020)
Cumulative views and downloads (calculated since 28 Feb 2020)

Viewed (geographical distribution)

Total article views: 3,408 (including HTML, PDF, and XML) Thereof 3,122 with geography defined and 286 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Nov 2025
Download
Short summary
In this paper we propose adaptive clustering as a new method for reducing the computational efforts of distributed modelling. It consists of identifying similar-acting model elements during the runtime, clustering them, running the model for just a few representatives per cluster, and mapping their results to the remaining model elements in the cluster. With the example of a hydrological model, we show that this saves considerable computation time, while largely maintaining the output quality.
Read more
Share
Special issue
Linking landscape organisation and hydrological functioning:...