% 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”.

@ARTICLE{Ma:893348,
      author       = {Ma, Yueling and Montzka, Carsten and Bayat, Bagher and
                      Kollet, Stefan},
      title        = {{U}sing {L}ong {S}hort-{T}erm {M}emory networks to connect
                      water table depth anomalies to precipitation anomalies over
                      {E}urope},
      journal      = {Hydrology and earth system sciences},
      volume       = {25},
      number       = {6},
      issn         = {1607-7938},
      address      = {Katlenburg-Lindau},
      publisher    = {EGU},
      reportid     = {FZJ-2021-02700},
      pages        = {3555 - 3575},
      year         = {2021},
      abstract     = {Many European countries rely on groundwater for public and
                      industrial water supply. Due to a scarcity of near-real-time
                      water table depth (wtd) observations, establishing a
                      spatially consistent groundwater monitoring system at the
                      continental scale is a challenge. Hence, it is necessary to
                      develop alternative methods for estimating wtd anomalies
                      (wtda) using other hydrometeorological observations
                      routinely available near real time. In this work, we explore
                      the potential of Long Short-Term Memory (LSTM) networks for
                      producing monthly wtda using monthly precipitation anomalies
                      (pra) as input. LSTM networks are a special category of
                      artificial neural networks that are useful for detecting a
                      long-term dependency within sequences, in our case time
                      series, which is expected in the relationship between pra
                      and wtda. In the proposed methodology, spatiotemporally
                      continuous data were obtained from daily terrestrial
                      simulations of the Terrestrial Systems Modeling Platform
                      (TSMP) over Europe (hereafter termed the TSMP-G2A data set),
                      with a spatial resolution of 0.11°, ranging from the years
                      1996 to 2016. The data were separated into a training set
                      (1996–2012), a validation set (2013–2014), and a test
                      set (2015–2016) to establish local networks at selected
                      pixels across Europe. The modeled wtda maps from LSTM
                      networks agreed well with TSMP-G2A wtda maps on spatially
                      distributed dry and wet events, with 2003 and 2015
                      constituting drought years over Europe. Moreover, we
                      categorized the test performances of the networks based on
                      intervals of yearly averaged wtd, evapotranspiration (ET),
                      soil moisture (θ), snow water equivalent (Sw), soil type
                      (St), and dominant plant functional type (PFT). Superior
                      test performance was found at the pixels with
                      wtd < 3 m, ET > 200 mm, θ>0.15 m3 m−3,
                      and Sw<10 mm, revealing a significant impact of the local
                      factors on the ability of the networks to process
                      information. Furthermore, results of the cross-wavelet
                      transform (XWT) showed a change in the temporal pattern
                      between TSMP-G2A pra and wtda at some selected pixels, which
                      can be a reason for undesired network behavior. Our results
                      demonstrate that LSTM networks are useful for producing
                      high-quality wtda based on other hydrometeorological data
                      measured and predicted at large scales, such as pra. This
                      contribution may facilitate the establishment of an
                      effective groundwater monitoring system over Europe that is
                      relevant to water management.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / ERA-PLANET - The European network for observing
                      our changing planet (689443)},
      pid          = {G:(DE-HGF)POF4-2173 / G:(EU-Grant)689443},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000667601500002},
      doi          = {10.5194/hess-25-3555-2021},
      url          = {https://juser.fz-juelich.de/record/893348},
}