Articles | Volume 27, issue 22
https://doi.org/10.5194/hess-27-4205-2023
https://doi.org/10.5194/hess-27-4205-2023
Research article
 | 
28 Nov 2023
Research article |  | 28 Nov 2023

Improving understanding of groundwater flow in an alpine karst system by reconstructing its geologic history using conduit network model ensembles

Chloé Fandel, Ty Ferré, François Miville, Philippe Renard, and Nico Goldscheider

Related authors

Towards understanding the influence of seasons on low groundwater periods based on explainable machine learning
Andreas Wunsch, Tanja Liesch, and Nico Goldscheider
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-284,https://doi.org/10.5194/hess-2023-284, 2023
Revised manuscript accepted for HESS
Short summary
When best is the enemy of good – critical evaluation of performance criteria in hydrological models
Guillaume Cinkus, Naomi Mazzilli, Hervé Jourde, Andreas Wunsch, Tanja Liesch, Nataša Ravbar, Zhao Chen, and Nico Goldscheider
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
Short summary
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
Hydrol. Earth Syst. Sci., 27, 1961–1985, https://doi.org/10.5194/hess-27-1961-2023,https://doi.org/10.5194/hess-27-1961-2023, 2023
Short summary
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
Short summary
Using machine learning to predict optimal electromagnetic induction instrument configurations for characterizing the shallow subsurface
Kim Madsen van't Veen, Ty Paul Andrew Ferré, Bo Vangsø Iversen, and Christen Duus Børgesen
Hydrol. Earth Syst. Sci., 26, 55–70, https://doi.org/10.5194/hess-26-55-2022,https://doi.org/10.5194/hess-26-55-2022, 2022
Short summary

Related subject area

Subject: Groundwater hydrology | Techniques and Approaches: Stochastic approaches
An ensemble-based approach for pumping optimization in an island aquifer considering parameter, observation and climate uncertainty
Cécile Coulon, Jeremy T. White, Alexandre Pryet, Laura Gatel, and Jean-Michel Lemieux
Hydrol. Earth Syst. Sci., 28, 303–319, https://doi.org/10.5194/hess-28-303-2024,https://doi.org/10.5194/hess-28-303-2024, 2024
Short summary
The effects of rain and evapotranspiration statistics on groundwater recharge estimations for semi-arid environments
Tuvia Turkeltaub and Golan Bel
Hydrol. Earth Syst. Sci., 27, 289–302, https://doi.org/10.5194/hess-27-289-2023,https://doi.org/10.5194/hess-27-289-2023, 2023
Short summary
Characterization of the highly fractured zone at the Grimsel Test Site based on hydraulic tomography
Lisa Maria Ringel, Mohammadreza Jalali, and Peter Bayer
Hydrol. Earth Syst. Sci., 26, 6443–6455, https://doi.org/10.5194/hess-26-6443-2022,https://doi.org/10.5194/hess-26-6443-2022, 2022
Short summary
Influence of low-frequency variability on high and low groundwater levels: example of aquifers in the Paris Basin
Lisa Baulon, Nicolas Massei, Delphine Allier, Matthieu Fournier, and Hélène Bessiere
Hydrol. Earth Syst. Sci., 26, 2829–2854, https://doi.org/10.5194/hess-26-2829-2022,https://doi.org/10.5194/hess-26-2829-2022, 2022
Short summary
Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks
Huiying Ren, Erol Cromwell, Ben Kravitz, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 1727–1743, https://doi.org/10.5194/hess-26-1727-2022,https://doi.org/10.5194/hess-26-1727-2022, 2022
Short summary

Cited articles

Audra, P. and Palmer, A. N.: The pattern of caves: controls of epigenic speleogenesis, Géomorphologie, 17, 359–378, https://doi.org/10.4000/geomorphologie.9571, 2011. 
Borghi, A., Renard, P., and Jenni, S.: A pseudo-genetic stochastic model to generate karstic networks, J. Hydrol. 414, 516–529, https://doi.org/10.1016/j.jhydrol.2011.11.032, 2012. 
Chen, Z. and Goldscheider, N.: Modeling spatially and temporally varied hydraulic behavior of a folded karst system with dominant conduit drainage at catchment scale, Hochifen–Gottesacker, Alps. J. Hydrol., 514, 41–52, https://doi.org/10.1016/j.jhydrol.2014.04.005, 2014. 
Chen, Z., Hartmann, A., and Goldscheider, N.: A new approach to evaluate spatiotemporal dynamics of controlling parameters in distributed environmental models, Environ. Modell. Softw., 87, 1–16, https://doi.org/10.1016/j.envsoft.2016.10.005, 2017. 
Chen, Z., Hartmann, A., Wagener, T., and Goldscheider, N.: Dynamics of water fluxes and storages in an Alpine karst catchment under current and potential future climate conditions, Hydrol. Earth Syst. Sci., 22, 3807–3823, https://doi.org/10.5194/hess-22-3807-2018, 2018. 
Download
Short summary
From the surface, it is hard to tell where underground cave systems are located. We developed a computer model to create maps of the probable cave network in an area, based on the geologic setting. We then applied our approach in reverse: in a region where an old cave network was mapped, we used modeling to test what the geologic setting might have been like when the caves formed. This is useful because understanding past cave formation can help us predict where unmapped caves are located today.