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

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

doi:https://doi.org/10.5194/hess-27-1961-2023

Articles | Volume 27, issue 10

https://doi.org/10.5194/hess-27-1961-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-27-1961-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 10

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

Data sets

Time series of type hydrology-hydrogeology in Le Lez (Méditerranée) basin - MEDYCYSS observatory - KARST observatory network - OZCAR Critical Zone network Research Infrastructure SNO KARST https://doi.org/10.15148/CFD01A5B-B7FD-41AA-8884-84DBDDAC767E

Archive of meteorological data ARSO http://www.meteo.si/

DWD Opendata DWD https://opendata.dwd.de/

Model code and software

Model code: 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 https://doi.org/10.5281/zenodo.7242077

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.