Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3555-2021
https://doi.org/10.5194/hess-25-3555-2021
Research article
 | 
23 Jun 2021
Research article |  | 23 Jun 2021

Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe

Yueling Ma, Carsten Montzka, Bagher Bayat, and Stefan Kollet

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Cited articles

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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.