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

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

doi:https://doi.org/10.5194/hess-25-3555-2021

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.

https://doi.org/10.5194/hess-25-3555-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 6

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

Data sets

Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe Y. Ma, C. Montzka, B. Bayat, and S. Kollet https://doi.org/10.26165/JUELICH-DATA/WPRA1F

Short summary

This study utilized spatiotemporally continuous precipitation anomaly (pr_a) and water table depth anomaly (wtd_a) 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 pr_a and wtd_a in order to obtain reliable models to estimate wtda at the individual pixel level.