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@PHDTHESIS{Ma:909348,
      author       = {Ma, Yueling},
      title        = {{M}achine learning for monitoring groundwater resources
                      over {E}urope},
      volume       = {583},
      school       = {Univ. Bonn},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2022-03138},
      isbn         = {978-3-95806-638-0},
      series       = {Schriften des Forschungszentrums Jülich Reihe Energie $\&$
                      Umwelt / Energy $\&$ Environment},
      pages        = {viii, 125},
      year         = {2022},
      note         = {Dissertation, Univ. Bonn, 2022},
      abstract     = {Groundwater (GW) is an important natural resource for
                      Europe and the world, and has been affected by extreme
                      weather and climate, e.g., summer heat waves and droughts,
                      and human overexploitation. As climate change and human
                      interventions increase, extreme events and GW depletion are
                      expected to become more frequent and severe in many parts of
                      Europe in the future, aggravating the vulnerability of GW
                      systems. This emphasizes the necessity of GW monitoring in
                      GW management. Up to date, however, it is still challenging
                      to monitor GW at the large, continental scale, mainly due to
                      the lack of water table depth (wtd) observations. In order
                      to address the challenge, the PhD work proposes an indirect,
                      generic methodology based on advanced machine learning (ML)
                      techniques, that are Long Short-Term Memory (LSTM) networks
                      andtransfer learning (TL), to produce reliable monthly wtd
                      anomaly (wtda) estimates at the continental scale. The
                      methodology is named LSTM-TL. While in this work, LSTM-TL
                      has been implemented over Europe, it is transferable to
                      other regions in the world. The methodology relies on the
                      close connection between GW and other atmospheric and
                      terrestrial compartments in the water cycle, using
                      precipitation and soil moisture anomalies (pra and θa) as
                      input, which have data available at large scales from, e.g.,
                      remotely sensed observations. Several steps were involved in
                      the development of LSTM-TL for GW monitoring. In the first
                      step, LSTM networks were applied in combination with
                      spatiotemporally continuous pra and wtda data from
                      uncalibrated integrated hydrologic simulation results (named
                      the TSMP-G2A data set) over Europe to capture the
                      time-varying and time-lagged relationship between pra and
                      wtda in order to obtain reliable networks to estimate wtda
                      at the individual pixel level assuming that pra is a useful
                      proxy for wtda. In most European regions, LSTM networks
                      showed good skill with respect to the TSMP-G2A data set
                      inpredicting wtda with pra as input. The results indicated
                      that the local factors, that are yearly averaged wtd,
                      evapotranspiration (ET), soil moisture (θ), and snow water
                      equivalent (SWE), had a significant impact on the
                      performance of the LSTM networks. Moreover, the decrease in
                      the network test performance at some pixels was attributed
                      to a change in the temporal TSMP-G2A pra-wtda pattern during
                      the study period. In the second step, a number of input
                      hydrometeorological variables, in addition to pra, were
                      included in the construction of LSTM networks to arrive at
                      improved wtda estimates at individual pixels over Europe in
                      various experiments. All input and target data were derived
                      from the TSMP-G2A data set. Improved LSTM networks were
                      found with pra and θa as input. Considering θa strongly
                      increased the network testperformance particularly in the
                      areas with wtd ≤ 3 m (i.e., the major wtd category of
                      Europe), suggesting the substantial contribution of θa to
                      the estimation of wtda over Europe. The results highlight
                      the importance to combine θ information with precipitation
                      information in quantifying and predicting wtda.},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2022090708},
      url          = {https://juser.fz-juelich.de/record/909348},
}