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

10.26165/JUELICH-DATA/WPRA1F Ma, Yuelinghttps://orcid.org/0000-0002-1869-7702(Forschungszentrum Jülich)Montzka, Carsten(Forschungszentrum Jülich)Bayat, Bagher(Forschungszentrum Jülich)Kollet, Stefan(Forschungszentrum Jülich) Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe Jülich DATA 2021 This study utilized spatiotemporally continuous precipitation anomaly (pr_a) and water table depth anomaly (wtd_a) from integrated hydrologic simulation results (i.e., the TSMP-G2A data set) 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 wtd_a at the individual pixel level. The data files provide the TSMP-G2A pr_a, the TSMP-G2A wtd_a, and the LSTM wtd_a data from 1996 to 2016, with a spatial resolution of 0.11 degree. Ma, Yueling(Forschungszentrum Jülich)