Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europedoi:10.26165/JUELICH-DATA/WPRA1FJülich DATA2021-05-311Ma, Yueling; Montzka, Carsten; Bayat, Bagher; Kollet, Stefan, 2021, "Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe", https://doi.org/10.26165/JUELICH-DATA/WPRA1F, Jülich DATA, V1Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europedoi:10.26165/JUELICH-DATA/WPRA1FMa, YuelingMontzka, CarstenBayat, BagherKollet, StefanJülich DATAMa, YuelingMa, Yueling2021-05-27Earth and Environmental SciencesGroundwaterLong Short-Term Memory (LSTM) networksAnomaliesEuropeThis 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.CC0 Waiver10.5194/hess-25-3555-2021Ma, Y., Montzka, C., Bayat, B. and Kollet, S.: Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe, Hydrol. Earth Syst. Sci., 25(6), 3555–3575, 2021.LSTM_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_LSTM_0.1_degree_v1.1996_2016.ncMonthly groundwater table depth anomalies generated from the proposed LSTM networksapplication/x-netcdfTSMP_G2A_monthly_groundwater_table_depth_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.ncMonthly groundwater table depth anomalies calculated from the TSMP-G2A data setapplication/x-netcdfTSMP_G2A_monthly_precipitation_anomaly_FZJ-IBG3_TSMP_0.1_degree_v1.1996_2016.ncMonthly precipitation anomalies calculated from the TSMP-G2A data setapplication/x-netcdf