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@ARTICLE{Ma:902254,
      author       = {Ma, Yueling and Montzka, Carsten and Bayat, Bagher and
                      Kollet, Stefan},
      title        = {{A}n {I}ndirect {A}pproach {B}ased on {L}ong {S}hort-{T}erm
                      {M}emory {N}etworks to {E}stimate {G}roundwater {T}able
                      {D}epth {A}nomalies {A}cross {E}urope {W}ith an
                      {A}pplication for {D}rought {A}nalysis},
      journal      = {Frontiers in water},
      volume       = {3},
      issn         = {2624-9375},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2021-04124},
      pages        = {723548},
      year         = {2021},
      abstract     = {The lack of high-quality continental-scale groundwater
                      table depth observations necessitates developing an indirect
                      method to produce reliable estimation for water table depth
                      anomalies (wtda) over Europe to facilitate European
                      groundwater management under drought conditions. Long
                      Short-Term Memory (LSTM) networks are a deep learning
                      technology to exploit long-short-term dependencies in the
                      input-output relationship, which have been observed in the
                      response of groundwater dynamics to atmospheric and land
                      surface processes. Here, we introduced different input
                      variables including precipitation anomalies (pra), which is
                      the most common proxy of wtda, for the networks to arrive at
                      improved wtda estimates at individual pixels over Europe in
                      various experiments. All input and target data involved in
                      this study were obtained from the simulated TSMP-G2A data
                      set. We performed wavelet coherence analysis to gain a
                      comprehensive understanding of the contributions of
                      different input variable combinations to wtda estimates.
                      Based on the different experiments, we derived an indirect
                      method utilizing LSTM networks with pra and soil moisture
                      anomaly (θa) as input, which achieved the optimal network
                      performance. The regional medians of test R2 scores and
                      RMSEs obtained by the method in the areas with wtd ≤ 3.0 m
                      were $76\%-95\%$ and 0.17-0.30, respectively, constituting a
                      $20\%-66\%$ increase in median R2 and a 0.19-0.30 decrease
                      in median RMSEs compared to the LSTM networks only with pra
                      as input. Our results show that introducing θa
                      significantly improved the performance of the trained
                      networks to predict wtda, indicating the substantial
                      contribution of θa to explain groundwater anomalies. Also,
                      the European wtda map reproduced by the method had good
                      agreement with that derived from the TSMP-G2A data set with
                      respect to drought severity, successfully detecting $~41\%$
                      of strong drought events (wtda ≥ 1.5) and $~29\%$ of
                      extreme drought events (wtda ≥ 2) in August 2015. The
                      study emphasizes the importance to combine soil moisture
                      information with precipitation information in quantifying or
                      predicting groundwater anomalies. In the future, the
                      indirect method derived in this study can be transferred to
                      real-time monitoring of groundwater drought at the
                      continental scale using remotely sensed soil moisture and
                      precipitation observations or respective information from
                      weather prediction models.},
      cin          = {IBG-3},
      ddc          = {333.7},
      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:000720866900001},
      doi          = {10.3389/frwa.2021.723548},
      url          = {https://juser.fz-juelich.de/record/902254},
}