Articles | Volume 28, issue 19
https://doi.org/10.5194/hess-28-4407-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-4407-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Oct 2024
Research article |
| 07 Oct 2024
| 07 Oct 2024
Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
Institute of Groundwater Management, TU Dresden, Dresden, Germany
Maximilian Nölscher
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
Andreas Hartmann
Institute of Groundwater Management, TU Dresden, Dresden, Germany
Stefan Broda
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
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Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
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Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
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Tesfalem Abraham, Yan Liu, Sirak Tekleab, and Andreas Hartmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-271, https://doi.org/10.5194/hess-2021-271, 2021
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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
Nicolas Massei, Daniel G. Kingston, David M. Hannah, Jean-Philippe Vidal, Bastien Dieppois, Manuel Fossa, Andreas Hartmann, David A. Lavers, and Benoit Laignel
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Short summary
To understand the impact of external factors on groundwater level modelling using a 1-D convolutional neural network (CNN) model, we train, validate, and tune individual CNN models for 505 wells distributed across Lower Saxony, Germany. We then evaluate the performance of these models against available geospatial and time series features. This study provides new insights into the relationship between these factors and the accuracy of groundwater modelling.
To understand the impact of external factors on groundwater level modelling using a 1-D...
