Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3555-2021
© Author(s) 2021. 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-25-3555-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jun 2021
Research article |
| 23 Jun 2021
| 23 Jun 2021
Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe
Institute of Bio and Geosciences, Agrosphere (IBG-3),
Forschungszentrum Jülich, 52428 Jülich, Germany
Center for High-Performance Scientific Computing in Terrestrial
Systems, Geoverbund ABC/J, 52428 Jülich, Germany
Carsten Montzka
Institute of Bio and Geosciences, Agrosphere (IBG-3),
Forschungszentrum Jülich, 52428 Jülich, Germany
Bagher Bayat
Institute of Bio and Geosciences, Agrosphere (IBG-3),
Forschungszentrum Jülich, 52428 Jülich, Germany
Stefan Kollet
Institute of Bio and Geosciences, Agrosphere (IBG-3),
Forschungszentrum Jülich, 52428 Jülich, Germany
Center for High-Performance Scientific Computing in Terrestrial
Systems, Geoverbund ABC/J, 52428 Jülich, Germany
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Short summary
This study utilized spatiotemporally continuous precipitation anomaly (pra) and water table depth anomaly (wtda) data from integrated hydrologic simulation results over Europe in combination with Long Short-Term Memory (LSTM) networks to capture the time-varying and time-lagged relationship between pra and wtda in order to obtain reliable models to estimate wtda at the individual pixel level.
This study utilized spatiotemporally continuous precipitation anomaly (pra) and water table...
