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@INPROCEEDINGS{Ma:888981,
      author       = {Ma, Yueling and Montzka, Carsten and Bayat, Bagher and
                      Kollet, Stefan},
      title        = {{A}n optimized indirect method to estimate groundwater
                      table depth anomalies over {E}urope based on {L}ong
                      {S}hort-{T}erm {M}emory networks},
      reportid     = {FZJ-2020-05368},
      year         = {2020},
      abstract     = {Long Short-Term Memory (LSTM) networks are a deep learning
                      technology to exploit long-term dependencies in the
                      input-output relationship, which has been observed in the
                      response of groundwater dynamics to atmospheric and land
                      surface processes. We introduced an indirect method based on
                      LSTM networks to estimate monthly water table depth
                      anomalies $(wtd_a)$ across Europe from monthly precipitation
                      anomalies $(pr_a).$ The network has further been optimized
                      by including supplementary hydrometeorological variables,
                      which are routinely measured and available at large scales.
                      The data were obtained from daily integrated hydraulic
                      simulation results over Europe from 1996 to 2016, with a
                      spatial resolution of $0.11\°$ (Furusho-Percot et al.,
                      2019), and separated into a training set, a validation set
                      and a test set at individual pixels. We compared test
                      performances of the LSTM networks locally at selected pixels
                      in eight PRUDENCE regions with random combinations of
                      monthly $pr_a,$ evapotranspiration anomaly, and soil
                      moisture anomaly $(θ_a)$ as input variables. The optimal
                      combination of input variables was $pr_a$ and $θ_a,$ and
                      the networks with this combination achieved average test
                      $R^2$ between $47.88\%$ and $91.62\%$ in areas with
                      simulated wtd ≤ 3 m. Moreover, we found that introducing
                      $θ_a$ improved the ability of the trained networks to
                      handle new data, indicating the substantial contribution of
                      $θ_a$ to explain groundwater state variation. Therefore,
                      including information about $θ_a$ is beneficial, for
                      instance in the estimation of groundwater drought, and the
                      proposed optimized method may be transferred to a real-time
                      monitoring of groundwater drought at the continental scale
                      using remotely sensed soil moisture
                      observations.Furusho-Percot, C., Goergen, K., Hartick, C.,
                      Kulkarni, K., Keune, J. and Kollet, S.: Pan-European
                      groundwater to atmosphere terrestrial systems climatology
                      from a physically consistent simulation, Sci. data, 6(1),
                      320, doi:10.1038/s41597-019-0328-7, 2019.},
      month         = {Dec},
      date          = {2020-12-01},
      organization  = {AGU 2020 Fall Meeting, Online
                       (Online), 1 Dec 2020 - 17 Dec 2020},
      subtyp        = {After Call},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / ERA-PLANET - The European network for observing
                      our changing planet (689443)},
      pid          = {G:(DE-HGF)POF3-255 / G:(EU-Grant)689443},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/888981},
}