% 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:892347,
      author       = {Ma, Yueling and Montzka, Carsten and Bayat, Bagher and
                      Kollet, Stefan},
      title        = {{K}nowledge transfer from simulation to reality via {L}ong
                      {S}hort-{T}erm {M}emory networks:  {E}stimating groundwater
                      table depth anomalies over {E}urope},
      reportid     = {FZJ-2021-02015},
      year         = {2021},
      abstract     = {Near real-time groundwater table depth measurements are
                      scarce over Europe, leading to challenges in monitoring
                      groundwater resources at the continental scale. In this
                      study, we leveraged knowledge learned from simulation
                      results by Long Short-Term Memory (LSTM) networks to
                      estimate monthly groundwater table depth anomaly (wtda) data
                      over Europe. The LSTM networks were trained, validated, and
                      tested at individual pixels on anomaly data derived from
                      daily integrated hydrologic simulation results over Europe
                      from 1996 to 2016, with a spatial resolution of 0.11°
                      (Furusho-Percot et al., 2019), to predict monthly wtda based
                      on monthly precipitation anomalies (pra) and soil moisture
                      anomalies (θa). Without additional training, we directly
                      fed the networks with averaged monthly pra and θa data from
                      1996 to 2016 obtained from commonly available observational
                      datasets and reanalysis products, and compared the network
                      outputs with available borehole in situ measured wtda. The
                      LSTM network estimates show good agreement with the in situ
                      observations, resulting in Pearson correlation coefficients
                      of regional averaged wtda data in seven PRUDENCE regions
                      ranging from $42\%$ to $76\%,$ which are ~ $10\%$ higher
                      than the original simulation results except for the Iberian
                      Peninsula. Our study demonstrates the potential of LSTM
                      networks to transfer knowledge from simulation to reality
                      for the estimation of wtda over Europe. The proposed method
                      can be used to provide spatiotemporally continuous
                      information at large spatial scales in case of sparse
                      ground-based observations, which is common for groundwater
                      table depth measurements. Moreover, the results highlight
                      the advantage of combining physically-based models with
                      machine learning techniques in data processing.
                      Reference: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).},
      month         = {Apr},
      date          = {2021-04-19},
      organization  = {EGU General Assembly 2021, Online
                       (Online), 19 Apr 2021 - 30 Apr 2021},
      subtyp        = {After Call},
      cin          = {IBG-3},
      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)6},
      doi          = {10.5194/egusphere-egu21-590},
      url          = {https://juser.fz-juelich.de/record/892347},
}