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

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

doi:https://doi.org/10.5194/hess-24-4389-2020

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:

Linking landscape organisation and hydrological functioning:...

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

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Final revised paper (published on 09 Sep 2020)
Preprint (discussion started on 28 Feb 2020)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review of “Adaptive clustering: A method to analyze dynamical similarity and to reduce redundancies in distributed (hydrological) modeling”', Shervan Gharari, 19 Mar 2020
- AC1: 'Replies to comments by referee #1 (Shervan Gharari)', Uwe Ehret, 06 Apr 2020
  - RC3: 'Re-Rely to Review of “Adaptive clustering: A method to analyze dynamical similarity and to reduce redundancies in distributed (hydrological) modeling"', Shervan Gharari, 13 Apr 2020
    - AC3: 'Replies', Uwe Ehret, 23 Apr 2020
RC2: 'Comments for the manuscript: “Adaptive clustering: A method to analyze the dynamical similarity and to reduce redundancies in distributed (hydrological) modeling”', Anonymous Referee #2, 27 Mar 2020
- AC2: 'Replies to comments by referee #2', Uwe Ehret, 06 Apr 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (further review by editor and referees) (05 May 2020) by Hilary McMillan

AR by Uwe Ehret on behalf of the Authors (29 May 2020) Author's response

ED: Referee Nomination & Report Request started (15 Jun 2020) by Hilary McMillan

RR by Kairong Lin (02 Jul 2020)

ED: Publish subject to minor revisions (review by editor) (23 Jul 2020) by Hilary McMillan

AR by Uwe Ehret on behalf of the Authors (29 Jul 2020) Author's response Manuscript

ED: Publish as is (31 Jul 2020) by Hilary McMillan

AR by Uwe Ehret on behalf of the Authors (02 Aug 2020)

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.