Dominique Thiéry, Naomi Mazzilli, Guillaume Artigue, Yong Chang, Yan Liu, Philip Schuler, Tanja Liesch, Pierre-Yves Jeannin, Eulogio Pardo-Igúzquiza, Hervé Jourde, Martin P. Lüthi, Laurence Gill, Jean-Baptiste Charlier, Thomas Wöhling, Andreas Wunsch, Arnauld Malard, Lea Duran, Anne Johannet, Thomas Reimann, Andreas Hartmann, Christoph Butscher, Alireza Kavousi, Swiss Institute for Speleology and Karst Studies (SISKA), Centre for Hydrogeology and Geothermics [Switzerland], Université de Neuchâtel (UNINE), IMT Mines Alès - ERT (ERT), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), HYTAKE : Hydrogéologie et Transferts dans les Aquifères Karstiques (HYTAKE), Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Technishe Universität Bergakademie Freiberg (TU Bergakademie Freiberg), Nanjing University (NJU), Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Gestion de l'Eau, Acteurs, Usages (UMR G-EAU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Trinity College Dublin, University of Freiburg [Freiburg], Service National d'Observation sur le KARST (SNO Karst), Institut national des sciences de l'Univers (INSU - CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Karlsruhe Institute of Technology (KIT), Universität Zürich [Zürich] = University of Zurich (UZH), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Instituto Geológico y Minero de España (IGME), Lincoln Agritech Ltd, Swiss National Research Foundation (SNF) for Swisskarst and Thermokarst projects (Grant Numbers 406140-125962 and 200021_188636), the Deutsche Forschungsgemeinschaft, DFG for the iKarst project (Grant Numbers: LI 727/31-1 and RE 4001/2-1). The work of E. Pardo-Igúzquiza was supported by research project PID2019-106435 GB-I00 of the Ministerio de Ciencia e Innovación of Spain. This TCD-Dublin was conducted within the Irish Centre for Research in Applied Geosciences (ICRAG), supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number 13/RC/2092. The authors would like to thank the French Karst National Observatory Service (SNO KARST) initiative at the INSU/CNRS for their diffusion of KarstMod platform., University of Zurich, Jeannin, Pierre-Yves, Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
International audience; 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. The use of instantaneous data (e.g. value at noon) produces less bias that averaging hourly data over one day. The transformation of hourly into daily data produces a decrease of Nash and KGE of 0.05 to 0.08 (i.e. from 1 to ~0.93). The resulting simulations (forecasted values for year 2016) produced KGEs ranging between 0.83 and 0.37 (0.83 to −0.24 for NSE). Although the simulations matched the monitored flows reasonably well, most models struggled to simulate baseflow conditions accurately. In general, the models that performed the best for this exercise were the global ones (Gardenia and Varkarst), with a limited number of parameters, which can be calibrated using automatic calibration procedures. The neural network models also showed a fair potential, with one providing reasonable results despite the relatively short dataset available for warming-up (4.5 years). Semi-and fully distributed models also suggested that with some more effort they could perform well. The accuracy of model predictions does not seem to increase by using models with more than 9–12 calibration parameters. An evaluation of the relative errors between the forecasted and the observed values revealed that for most models, 50% of the forecasted values contained more than 50% of difference against the observed discharge rate, with 25% having a difference larger than 100%. A significant part of the poorly forecasted values corresponded to base-flow which was surprising given that as base-flow is generally much easier to predict than peak flow. Hence, this shows that modelling approaches and criteria for the calibration are too oriented towards peak-flow sections of the hydrographs, and that improvements could be gained by more focus on the base-flow.