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Karst modelling challenge 1: Results of hydrological modelling

Jeannin, Pierre-Yves; Artigue, Guillaume; Butscher, Christoph; Chang, Yong; Charlier, Jean-Baptiste; Duran, Lea; Gill, Laurence; Hartmann, Andreas; Johannet, Anne; Jourde, Hervé; Kavousi, Alireza; Liesch, Tanja 1; Liu, Yan; Lüthi, Martin; Malard, Arnauld; Mazzilli, Naomi; Pardo-Igúzquiza, Eulogio; Thiéry, Dominique; Reimann, Thomas; ... mehr

Abstract (englisch):

The complexity of karst groundwater flow modelling is reflected by the amount of simulation approaches. The goal of the Karst Modelling Challenge (KMC) is comparing different approaches on one single system using the same data set. Thirteen teams with different computational models for simulating discharge variations at karst springs have applied their respective models on one single data set coming from the Milandre Karst Hydrogeological System (MKHS). The approaches include neural networks, reservoir models, semi-distributed models and fully distributed groundwater models. Four and a half years of hourly or daily meteorological input and hourly discharge data were provided for model calibration. The validation comprised forecasting one year of discharge, without the observed discharge data. The model performance was evaluated using the volume conservation, Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) applied on the total discharge and individual flow components. As a result, the comparison of model performances is a challenging task due to the differences in the model architecture but also required time steps: some of the models require aggregated daily steps while others could be run using hourly data, which provided some interesting differences depending on how the data was transformed. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000134180
Veröffentlicht am 21.06.2021
DOI: 10.1016/j.jhydrol.2021.126508
Zitationen: 9
Web of Science
Zitationen: 7
Zitationen: 12
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Geowissenschaften (AGW)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 0022-1694
KITopen-ID: 1000134180
Erschienen in Journal of hydrology
Verlag Elsevier
Band 600
Seiten 126508
Nachgewiesen in Web of Science
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