Articles | Volume 24, issue 9
https://doi.org/10.5194/hess-24-4389-2020
https://doi.org/10.5194/hess-24-4389-2020
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

Data sets

Precipitation data Administration des services techniques de l'agriculture (ASTA) http://www.agrimeteo.lu/

Corine Land Cover (CLC) 2012 European Environment Agency (EEA) http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/view

Model code and software

KIT-HYD/SHM-Attert-Adaptive-Clustering: Release 1 (Version v1.0) Uwe Ehret https://doi.org/10.5281/zenodo.4017427

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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.