% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Ma:875351,
      author       = {Ma, Yueling and Montzka, Carsten and Bayat, Bagher and
                      Kollet, Stefan},
      title        = {{M}odeling of groundwater table depth anomalies using
                      {L}ong {S}hort-{T}erm {M}emory networks over {E}urope},
      reportid     = {FZJ-2020-01969},
      year         = {2020},
      abstract     = {<p>Groundwater is the dominant source of fresh water in
                      many European countries. However, due to a lack of
                      near-real-time water table depth (wtd) observations,
                      monitoring of groundwater resources is not feasible at the
                      continental scale. Thus, an alternative approach is required
                      to produce wtd data from other available observations
                      near-real-time. In this study, we propose Long Short-Term
                      Memory (LSTM) networks to model monthly wtd anomalies over
                      Europe utilizing monthly precipitation anomalies as input.
                      LSTM networks are a special type of artificial neural
                      networks, showing great promise in exploiting long-term
                      dependencies between time series, which is expected in the
                      response of groundwater to precipitation. To establish the
                      methodology, spatially and temporally continuous data from
                      terrestrial simulations at the continental scale were
                      applied with a spatial resolution of $0.11\&#176;,$ ranging
                      from the year 1996 to 2016 (Furusho-Percot et al., 2019).
                      They were divided into a training set (1996 $\&#8211;$
                      2012), a validation set (2012 $\&#8211;$ 2014) and a testing
                      set (2015 -2016) to construct local models on selected
                      pixels over eight PRUDENCE regions. The outputs of the LSTM
                      networks showed good agreement with the simulation results
                      in locations with a shallow wtd (~3m). It is important to
                      note, the quality of the models was strongly affected by the
                      amount of snow cover. Moreover, with the introduction of
                      monthly evapotranspiration anomalies as additional input,
                      pronounced improvements of the network performances were
                      only obtained in more arid regions (i.e., Iberian Peninsula
                      and Mediterranean). Our results demonstrate the potential of
                      LSTM networks to produce high-quality wtd anomalies from
                      hydrometeorological variables that are monitored at the
                      large scale and part of operational forecasting systems
                      potentially facilitating the implementation of an efficient
                      groundwater monitoring system over
                      Europe.</p><p>Reference:</p><p>Furusho-Percot, C., Goergen,
                      K., Hartick, C., Kulkarni, K., Keune, J. and Kollet, S.
                      (2019). Pan-European groundwater to atmosphere terrestrial
                      systems climatology from a physically consistent simulation.
                      Scientific Data, 6(1).</p>},
      month         = {May},
      date          = {2020-05-04},
      organization  = {EGU General Assembly 2020, Online
                       (Online), 4 May 2020 - 8 May 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)6},
      doi          = {10.5194/egusphere-egu2020-5367},
      url          = {https://juser.fz-juelich.de/record/875351},
}