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

Ehret, Uwe 1; Pruijssen, Rik van 1; Bortoli, Marina 1; Loritz, Ralf 1; Azmi, Elnaz ORCID iD icon 2; Zehe, Erwin 1
1 Institut für Wasser und Gewässerentwicklung (IWG), Karlsruher Institut für Technologie (KIT)
2 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)


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 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. Key requirements for the application of adaptive clustering are the existence of (i) many model elements with (ii) comparable structural and functional properties and (iii) only weak interaction (e.g. hill slopes, subcatchments, or surface grid elements in hydrological and land surface models). The clustering of model elements must not only consider their time-invariant structural and functional properties but also their current state and forcing, as all these aspects influence their current functioning. Joining model elements into clusters is therefore a continuous task during model execution rather than a one-time exercise that can be done beforehand. Adaptive clustering takes this into account by continuously checking the clustering and re-clustering when necessary.
We explain the steps of adaptive clustering and provide a proof of concept at the example of a distributed, conceptual hydrological model fit to the Attert basin in Luxembourg. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000124809
Veröffentlicht am 19.10.2020
DOI: 10.5194/hess-24-4389-2020
Zitationen: 9
Web of Science
Zitationen: 9
Zitationen: 9
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wasser und Gewässerentwicklung (IWG)
Universität Karlsruhe (TH) – Interfakultative Einrichtungen (Interfakultative Einrichtungen)
KIT-Zentrum Klima und Umwelt (ZKU)
Scientific Computing Center (SCC)
Universität Karlsruhe (TH) – Zentrale Einrichtungen (Zentrale Einrichtungen)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1607-7938
KITopen-ID: 1000124809
HGF-Programm 46.12.01 (POF III, LK 01) Data Life Cycle Labs
Erschienen in Hydrology and earth system sciences
Verlag Copernicus Publications
Band 24
Heft 9
Seiten 4389–4411
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 09.09.2020
Nachgewiesen in Dimensions
Web of Science
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