Articles | Volume 27, issue 10
https://doi.org/10.5194/hess-27-1961-2023
https://doi.org/10.5194/hess-27-1961-2023
Research article
 | 
23 May 2023
Research article |  | 23 May 2023

Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions

Guillaume Cinkus, Andreas Wunsch, Naomi Mazzilli, Tanja Liesch, Zhao Chen, Nataša Ravbar, Joanna Doummar, Jaime Fernández-Ortega, Juan Antonio Barberá, Bartolomé Andreo, Nico Goldscheider, and Hervé Jourde

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When best is the enemy of good – critical evaluation of performance criteria in hydrological models
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Hydrol. Earth Syst. Sci., 27, 2397–2411, https://doi.org/10.5194/hess-27-2397-2023,https://doi.org/10.5194/hess-27-2397-2023, 2023
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Improvement of the KarstMod modeling platform for a better assessment of karst groundwater resources
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-17,https://doi.org/10.5194/hess-2023-17, 2023
Revised manuscript under review for HESS
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Karst spring discharge modeling based on deep learning using spatially distributed input data
Andreas Wunsch, Tanja Liesch, Guillaume Cinkus, Nataša Ravbar, Zhao Chen, Naomi Mazzilli, Hervé Jourde, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 26, 2405–2430, https://doi.org/10.5194/hess-26-2405-2022,https://doi.org/10.5194/hess-26-2405-2022, 2022
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Cited articles

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Short summary
Numerous modelling approaches can be used for studying karst water resources, which can make it difficult for a stakeholder or researcher to choose the appropriate method. We conduct a comparison of two widely used karst modelling approaches: artificial neural networks (ANNs) and reservoir models. Results show that ANN models are very flexible and seem great for reproducing high flows. Reservoir models can work with relatively short time series and seem to accurately reproduce low flows.