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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Cited
18 citations as recorded by crossref.
- Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions R. Xiong et al. https://doi.org/10.1021/acs.est.2c02232
- Using Simple LSTM Models to Evaluate Effects of a River Restoration on Groundwater in Kushiro Wetland, Hokkaido, Japan T. Yamaguchi et al. https://doi.org/10.3390/w15061115
- Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method H. Cai et al. https://doi.org/10.1016/j.jhydrol.2022.128495
- Estimation of Monthly Water Table Depth Anomalies Based on the Integration of GRACE and ERA5-Land with Large-Scale Simulations Using Random Forest and LSTM Networks L. Avila et al. https://doi.org/10.1007/s11269-025-04097-7
- Deep dependence in hydroclimatological variables T. Lee & J. Kim https://doi.org/10.1007/s10489-024-05345-w
- A Hybrid Triple Collocation-Deep Learning Approach for Improving Soil Moisture Estimation from Satellite and Model-Based Data W. Ming et al. https://doi.org/10.3390/rs14071744
- Integrating groundwater pumping data with regression-enhanced random forest models to improve groundwater monitoring and management in a coastal region J. Kim et al. https://doi.org/10.3389/frwa.2024.1509945
- Advancing AI-based pan-European groundwater monitoring Y. Ma et al. https://doi.org/10.1088/1748-9326/ac9c1e
- Soil moisture as a key predictor for regional groundwater levels: a deep learning study from Brandenburg, Germany M. Eckert & A. Rudolph https://doi.org/10.1088/3033-4942/ae4266
- Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al. https://doi.org/10.5194/hess-28-945-2024
- Relationship between meteorological and hydrological droughts in the upstream regions of the Lancang–Mekong River J. Li et al. https://doi.org/10.2166/wcc.2021.445
- An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Y. Ma et al. https://doi.org/10.3389/frwa.2021.723548
- Machine learning for predicting shallow groundwater levels in urban areas A. LaBianca et al. https://doi.org/10.1016/j.jhydrol.2024.130902
- Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML H. Tran et al. https://doi.org/10.3390/w13233393
- An advanced approach for the precise prediction of water quality using a discrete hidden markov model D. Li et al. https://doi.org/10.1016/j.jhydrol.2022.127659
- Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning R. Maity et al. https://doi.org/10.1016/j.acags.2024.100206
- From meteorological to hydrological drought: a case study using standardized indices in the Nakanbe River Basin, Burkina Faso T. Fowé et al. https://doi.org/10.1007/s11069-023-06194-5
- Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al. https://doi.org/10.1111/1752-1688.13170
18 citations as recorded by crossref.
- Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions R. Xiong et al. https://doi.org/10.1021/acs.est.2c02232
- Using Simple LSTM Models to Evaluate Effects of a River Restoration on Groundwater in Kushiro Wetland, Hokkaido, Japan T. Yamaguchi et al. https://doi.org/10.3390/w15061115
- Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method H. Cai et al. https://doi.org/10.1016/j.jhydrol.2022.128495
- Estimation of Monthly Water Table Depth Anomalies Based on the Integration of GRACE and ERA5-Land with Large-Scale Simulations Using Random Forest and LSTM Networks L. Avila et al. https://doi.org/10.1007/s11269-025-04097-7
- Deep dependence in hydroclimatological variables T. Lee & J. Kim https://doi.org/10.1007/s10489-024-05345-w
- A Hybrid Triple Collocation-Deep Learning Approach for Improving Soil Moisture Estimation from Satellite and Model-Based Data W. Ming et al. https://doi.org/10.3390/rs14071744
- Integrating groundwater pumping data with regression-enhanced random forest models to improve groundwater monitoring and management in a coastal region J. Kim et al. https://doi.org/10.3389/frwa.2024.1509945
- Advancing AI-based pan-European groundwater monitoring Y. Ma et al. https://doi.org/10.1088/1748-9326/ac9c1e
- Soil moisture as a key predictor for regional groundwater levels: a deep learning study from Brandenburg, Germany M. Eckert & A. Rudolph https://doi.org/10.1088/3033-4942/ae4266
- Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al. https://doi.org/10.5194/hess-28-945-2024
- Relationship between meteorological and hydrological droughts in the upstream regions of the Lancang–Mekong River J. Li et al. https://doi.org/10.2166/wcc.2021.445
- An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Y. Ma et al. https://doi.org/10.3389/frwa.2021.723548
- Machine learning for predicting shallow groundwater levels in urban areas A. LaBianca et al. https://doi.org/10.1016/j.jhydrol.2024.130902
- Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML H. Tran et al. https://doi.org/10.3390/w13233393
- An advanced approach for the precise prediction of water quality using a discrete hidden markov model D. Li et al. https://doi.org/10.1016/j.jhydrol.2022.127659
- Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning R. Maity et al. https://doi.org/10.1016/j.acags.2024.100206
- From meteorological to hydrological drought: a case study using standardized indices in the Nakanbe River Basin, Burkina Faso T. Fowé et al. https://doi.org/10.1007/s11069-023-06194-5
- Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al. https://doi.org/10.1111/1752-1688.13170
Saved (final revised paper)
Latest update: 09 Jun 2026
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...
