Articles | Volume 26, issue 9
Hydrol. Earth Syst. Sci., 26, 2405–2430, 2022
https://doi.org/10.5194/hess-26-2405-2022
Hydrol. Earth Syst. Sci., 26, 2405–2430, 2022
https://doi.org/10.5194/hess-26-2405-2022
Research article
09 May 2022
Research article | 09 May 2022

Karst spring discharge modeling based on deep learning using spatially distributed input data

Andreas Wunsch et al.

Related authors

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. Discuss., https://doi.org/10.5194/hess-2022-380,https://doi.org/10.5194/hess-2022-380, 2022
Preprint under review for HESS
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. Discuss., https://doi.org/10.5194/hess-2022-365,https://doi.org/10.5194/hess-2022-365, 2022
Preprint under review for HESS
Short summary
Spatiotemporal optimization of groundwater monitoring networks using data-driven sparse sensing methods
Marc Ohmer, Tanja Liesch, and Andreas Wunsch
Hydrol. Earth Syst. Sci., 26, 4033–4053, https://doi.org/10.5194/hess-26-4033-2022,https://doi.org/10.5194/hess-26-4033-2022, 2022
Short summary
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
Andreas Wunsch, Tanja Liesch, and Stefan Broda
Hydrol. Earth Syst. Sci., 25, 1671–1687, https://doi.org/10.5194/hess-25-1671-2021,https://doi.org/10.5194/hess-25-1671-2021, 2021

Related subject area

Subject: Groundwater hydrology | Techniques and Approaches: Modelling approaches
Machine-learning-based downscaling of modelled climate change impacts on groundwater table depth
Raphael Schneider, Julian Koch, Lars Troldborg, Hans Jørgen Henriksen, and Simon Stisen
Hydrol. Earth Syst. Sci., 26, 5859–5877, https://doi.org/10.5194/hess-26-5859-2022,https://doi.org/10.5194/hess-26-5859-2022, 2022
Short summary
Frequency domain water table fluctuations reveal impacts of intense rainfall and vadose zone thickness on groundwater recharge
Luca Guillaumot, Laurent Longuevergne, Jean Marçais, Nicolas Lavenant, and Olivier Bour
Hydrol. Earth Syst. Sci., 26, 5697–5720, https://doi.org/10.5194/hess-26-5697-2022,https://doi.org/10.5194/hess-26-5697-2022, 2022
Short summary
Characterizing groundwater heat transport in a complex lowland aquifer using paleo-temperature reconstruction, satellite data, temperature–depth profiles, and numerical models
Alberto Casillas-Trasvina, Bart Rogiers, Koen Beerten, Laurent Wouters, and Kristine Walraevens
Hydrol. Earth Syst. Sci., 26, 5577–5604, https://doi.org/10.5194/hess-26-5577-2022,https://doi.org/10.5194/hess-26-5577-2022, 2022
Short summary
Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components
Tunde Olarinoye, Tom Gleeson, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 26, 5431–5447, https://doi.org/10.5194/hess-26-5431-2022,https://doi.org/10.5194/hess-26-5431-2022, 2022
Short summary
Exploring river–aquifer interactions and hydrological system response using baseflow separation, impulse response modeling, and time series analysis in three temperate lowland catchments
Min Lu, Bart Rogiers, Koen Beerten, Matej Gedeon, and Marijke Huysmans
Hydrol. Earth Syst. Sci., 26, 3629–3649, https://doi.org/10.5194/hess-26-3629-2022,https://doi.org/10.5194/hess-26-3629-2022, 2022
Short summary

Cited articles

Afzaal, H., Farooque, A. A., Abbas, F., Acharya, B., and Esau, T.: Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning, Water, 12, 5, https://doi.org/10.3390/w12010005, 2020. a
Anderson, S. and Radić, V.: Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling, Hydrol. Earth Syst. Sci., 26, 795–825, https://doi.org/10.5194/hess-26-795-2022, 2022. a, b, c, d, e, f, g, h, i, j
ARSO – Slovenian Environment Agency: Archive of Hydrological Data, ARSO [data set], http://vode.arso.gov.si/hidarhiv/ (last access: 5 December 2020), 2020a. a, b, c
ARSO – Slovenian Environment Agency: Archive of Meteorological Data, ARSO [data set], http://www.meteo.si (last access: 5 December 2020), 2020b. a, b
Download
Short summary
Modeling complex karst water resources is difficult enough, but often there are no or too few climate stations available within or close to the catchment to deliver input data for modeling purposes. We apply image recognition algorithms to time-distributed, spatially gridded meteorological data to simulate karst spring discharge. Our models can also learn the approximate catchment location of a spring independently.