{"@context":"http://schema.org","@type":"Dataset","@id":"https://doi.org/10.26165/JUELICH-DATA/ZBLDIR","identifier":"https://doi.org/10.26165/JUELICH-DATA/ZBLDIR","name":"Advancing AI-based pan-European groundwater monitoring","creator":[{"name":"Ma, Yueling","affiliation":"Forschungszentrum Jülich"},{"name":"Montzka, Carsten","affiliation":"Forschungszentrum Jülich"},{"name":"Naz, Bibi","affiliation":"Forschungszentrum Jülich"},{"name":"Kollet, Stefan","affiliation":"Forschungszentrum Jülich"}],"author":[{"name":"Ma, Yueling","affiliation":"Forschungszentrum Jülich"},{"name":"Montzka, Carsten","affiliation":"Forschungszentrum Jülich"},{"name":"Naz, Bibi","affiliation":"Forschungszentrum Jülich"},{"name":"Kollet, Stefan","affiliation":"Forschungszentrum Jülich"}],"datePublished":"2021-12-14","dateModified":"2022-03-28","version":"2","description":["This 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)."],"keywords":["Earth and Environmental Sciences","Groundwater","Anomalies","LSTM-TL","Long Short-Term Memory (LSTM) networks","Transfer learning (TL)","Europe"],"citation":[{"@type":"CreativeWork","text":"Ma, Y., Montzka, C., Naz, B. and Kollet, S.: Advancing AI-based pan-European groundwater monitoring, in preparation."}],"license":{"@type":"Dataset","text":"CC0","url":"https://creativecommons.org/publicdomain/zero/1.0/"},"includedInDataCatalog":{"@type":"DataCatalog","name":"Jülich DATA","url":"https://data.fz-juelich.de"},"publisher":{"@type":"Organization","name":"Jülich DATA"},"provider":{"@type":"Organization","name":"Jülich DATA"},"distribution":[{"@type":"DataDownload","name":"COSMO_REA6_monthly_precipitation_anomaly_FZJ-IBG3_COSMO_0.1_degree_v1.1995_082019.nc","fileFormat":"application/x-netcdf","contentSize":206851420,"description":"Monthly precipitation anomalies (pr_a) derived from the COSMO-REA6 dataset (referred to: \r\nBollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl, S. and Steinke, S.: Towards a high‐resolution regional reanalysis for the European CORDEX domain, Q. J. R. Meteorol. Soc., 141(686), 1–15, doi:10.1002/qj.2486, 2015.)","contentUrl":"https://data.fz-juelich.de/api/access/datafile/4580"},{"@type":"DataDownload","name":"ERA5_bias_corrected_monthly_precipitation_anomaly_FZJ-IBG3_WFDE5_0.1_degree_v1..nc","fileFormat":"application/x-netcdf","contentSize":343806812,"description":"Monthly precipitation anomalies (pr_a) derived from the ERA5 bias corrected dataset (referred to: \r\nMuñoz Sabater, J.: Near surface meteorological variables from 1979 to 2019 derived from bias-corrected reanalysis, Copernicus Clim. Chang. Serv. Clim. 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Model Dev., 10(5), 1903–1925, doi:10.5194/gmd-10-1903-2017, 2017.\r\nMiralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A. and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. 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