10.26165/JUELICH-DATA/ZBLDIRMa, YuelingYuelingMa 0000-0002-1869-7702Forschungszentrum JülichMontzka, CarstenCarstenMontzkaForschungszentrum JülichNaz, BibiBibiNazForschungszentrum JülichKollet, StefanStefanKolletForschungszentrum JülichJülich DATA2021Earth and Environmental SciencesGroundwaterAnomaliesLSTM-TLLong Short-Term Memory (LSTM) networksTransfer learning (TL)EuropeMa, YuelingForschungszentrum Jülich2021-12-132022-03-2820685142034380681233891554833891554883980343806812352191836352191836352191836654052818670822866413682130413682130677810386670822866176105883176105883application/x-netcdfapplication/x-netcdfapplication/x-netcdfapplication/x-netcdfapplication/x-ipynb+jsonapplication/x-netcdfapplication/x-netcdfapplication/x-netcdfapplication/x-netcdfapplication/x-netcdfapplication/x-netcdfapplication/x-netcdfapplication/x-netcdfapplication/x-netcdfapplication/x-netcdfapplication/x-netcdfapplication/x-netcdf2.0CC0 WaiverThis study proposes an AI-based methodology combining Long Short-Term Memory (LSTM) networks and transfer learning (TL) to estimate water table depth anomalies (wtd_a) at the European scale in the absence of consistent water table depth (wtd) observational data sets, which is named LSTM-TL. The data repository provides: i) data utilized for evaluating LSTM-TL performance, i.e., input averaged monthly precipitation and soil moisture anomalies from common observational data sets (pr_a,o and θ_a,o), wtd_a estimates obtained from LSTM-TL (wtd_a,lstm-tl), wtd_a estimates obtained from LSTM networks with modeling data as input (wtd_a,lstm(m)), and wtd_a estimates obtained from LSTM networks trained on observations (wtd_a,lstm(o)); ii) reconstructed European monthly wtd_a,lstm-tl data RD1-6 from the early 1980s to the near present and their input pr_a,o and θ_a,o data; and iii) a Jupyter Notebook showing an example about the implementation of LSTM-TL. All the data sets in the repository have a spatial resolution of 0.11 degrees (~12.5 km).