Articles | Volume 26, issue 9
https://doi.org/10.5194/hess-26-2405-2022
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, Tanja Liesch, Guillaume Cinkus, Nataša Ravbar, Zhao Chen, Naomi Mazzilli, Hervé Jourde, and Nico Goldscheider

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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
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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.