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@ARTICLE{Avila:1038246,
      author       = {Avila, Leandro and de Lavenne, Alban and Ramos,
                      Maria-Helena and Kollet, Stefan},
      title        = {{E}stimation of {M}onthly {W}ater {T}able {D}epth
                      {A}nomalies {B}ased on the {I}ntegration of {GRACE} and
                      {ERA}5-{L}and with {L}arge-{S}cale {S}imulations {U}sing
                      {R}andom {F}orest and {LSTM} {N}etworks},
      journal      = {Water resources management},
      volume       = {39},
      issn         = {0920-4741},
      address      = {Dordrecht [u.a.]},
      publisher    = {Springer Science + Business Media B.V},
      reportid     = {FZJ-2025-01282},
      pages        = {20},
      year         = {2025},
      abstract     = {Increasing pressure on groundwater resources, exacerbated
                      by climate change, highlights the need to develop advanced
                      methods for monitoring groundwater storage and levels. While
                      numerical and physics-based models are widely used to
                      analyze the spatial and temporal dynamics of groundwater
                      levels, they require extensive input data and can be
                      computationally expensive for high-resolution and
                      large-scale simulations. In contrast, remote sensing
                      products such as the Gravity Recovery and Climate Experiment
                      (GRACE) provide global-scale information on total water
                      storage anomalies. However, due to its coarse spatial
                      resolution (0.25), GRACE data cannot be used directly to
                      assess groundwater conditions at local and regional scales.
                      In order to obtain local groundwater levels that can be
                      quickly accessed by stakeholders to monitor and define
                      appropriate groundwater management, this study implements a
                      methodology based on data-driven models to estimate monthly
                      water table depth anomalies (wtda), integrating simulations
                      from the Terrestrial Systems Modeling Platform (TSMP) with
                      GRACE and reanalysis ERA5-Land datasets. Considering the
                      spatial resolution of current TSMP simulations (TSMP-G2A -
                      0.11 degrees), we tested and compared multiple Random Forest
                      (RF) and LSTM networks at the pixel scale over the Seine
                      River Basin, combining different hydrological and
                      climatological variables with GRACE as input features. For
                      each data-driven approach, we selected the model that best
                      represents the temporal pattern of the wtda during the test
                      period and compared the results with the original TSMP
                      simulation, as well as in-situ groundwater observations. The
                      results indicate that both RF and LSTM networks can well
                      reproduce the temporal patterns of groundwater levels across
                      the Seine Basin obtained by the TSMP simulations, with
                      average Pearson correlations of 0.65 and average KGE of 0.6,
                      respectively. A comparison with multiple groundwater wells
                      allowed us to identify the regions where the applied models
                      are more reliable for representing wtda over the Seine River
                      Basin. In general, the proposed models show good agreement
                      with in-situ observations, independent of the groundwater
                      well depth. However, we found significant differences
                      between observed and simulated water table depths in the
                      downstream regions of the Seine River Basin, where coastal
                      systems and the presence of karst in the chalk might
                      influence groundwater levels and the performance of the
                      adopted models, respectively. The proposed methods provide
                      end users with an extremely lightweight reconstruction and
                      prediction tool for wtda at the pixel level, including
                      reliability estimates, which is easy to implement in an ad
                      hoc fashion in any evaluation and groundwater management
                      workflow.},
      cin          = {IBG-3},
      ddc          = {630},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / STARS4Water - Supporting STakeholders for
                      Adaptive, Resilient and Sustainable Water Management
                      (101059372)},
      pid          = {G:(DE-HGF)POF4-2173 / G:(EU-Grant)101059372},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001397138500001},
      doi          = {10.1007/s11269-025-04097-7},
      url          = {https://juser.fz-juelich.de/record/1038246},
}