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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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Cited articles

Abbott, M. B., Bathurst, J. C., Cunge, J. A., O'Connell, P. E., and Rasmussen, J.: An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system, J. Hydrol., 87, 45–59, https://doi.org/10.1016/0022-1694(86)90114-9, 1986. 
Administration de la gestion de l'eau (AGE): https://www.inondations.lu/, last access: 7 September 2020. 
Administration des services techniques de l'agriculture (ASTA): http://www.agrimeteo.lu/, last access: 7 September 2020. 
Aydogdu, A., Carrassi, A., Guider, C. T., Jones, C., and Rampal, P.: Data assimilation using adaptive, non-conservative, moving mesh models, Nonlin. Processes Geophys., 26, 175–193, https://doi.org/10.5194/npg-26-175-2019, 2019. 
Bacon, D. P., Ahmad, N. N., Boybeyi, Z., Dunn, T. J., Hall, M. S., Lee, P. C. S., Sarma, R. A., Turner, M. D., Waight, K. T., Young, S. H., and Zack, J. W.: A dynamically adapting weather and dispersion model: The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA), Mon. Weather Rev., 128, 2044–2076, https://doi.org/10.1175/1520-0493(2000)128<2044:Adawad>2.0.Co;2, 2000. 
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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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