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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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HESS | Articles | Volume 24, issue 9
Hydrol. Earth Syst. Sci., 24, 4389–4411, 2020
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:...

Hydrol. Earth Syst. Sci., 24, 4389–4411, 2020
https://doi.org/10.5194/hess-24-4389-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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
Publications Copernicus
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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.
In this paper we propose adaptive clustering as a new method for reducing the computational...
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