@COMMENT This file was generated by bib2html_grace.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <https://sourceforge.net/users/patstg/>
@COMMENT This file was prepared using the NASA Astrophysics Data System (ADS)
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@ARTICLE{2027PCE...14504765B,
       author = {{Bhunia}, Partha Sarathi and {Abhishek}},
        title = "{On the improved reconstruction of GRACE-TWSA for assessing droughts and their governing factors in a dam-regulated region}",
      journal = {Physics and Chemistry of the Earth},
     keywords = {GRACE, Drought, Deccan plateau, Terrestrial water storage, Anthropogenic activities},
         year = 2027,
        month = jan,
       volume = {145},
          eid = {104765},
        pages = {104765},
     abstract = "{Accurate drought assessment in heavily managed basins requires
        continuous terrestrial water storage anomaly (TWSA) records that
        capture both climatic and anthropogenic signals. This study asks
        whether data-driven reconstructions of GRACE/GRACE-FO TWSA
        require complex models with large predictor sets, and how
        different approaches perform in regions with intensive human
        activity. We use a classical artificial neural network (ANN),
        forced with a targeted set of six predictors, including two
        storage components that are strongly influenced by anthropogenic
        interventions over the dam-regulated Deccan Plateau, India. ANN-
        based reconstructed TWSA yields high skill across 11
        meteorological subdivisions (correlation = 0.96--0.99, NSE =
        0.80--0.99) and performs comparably to, or better than, existing
        global reconstruction products, particularly within heavily
        managed regions. Experiments excluding SWS or GWS increase RMSE
        by up to two-fold, demonstrating that explicitly representing
        these anthropogenically sensitive storage components is critical
        for improving TWSA reconstruction in such managed basins.
        Drought analysis using GRACE-Drought Severity Index (DSI) and
        Combined Climatologic Deviation Index (CCDI) reveals hotspots
        and extremes: the maximum number of events (seven) occurs in
        Marathwada and South Interior Karnataka, while the 2015--2019
        event in North Interior Karnataka records the largest duration
        (49 months) and severity (CCDI = -52.99; GRACE-DSI = -56.69).
        When assessing drought-induced water losses for 2003--2016, our
        product shows the closest agreement with GRACE, outperforming
        alternative datasets. Among the four large-scale climate
        oscillations examined, ENSO is the dominant mode associated with
        drought variability. Overall, the findings indicate that
        physically informed predictor selection is more important than
        increasing model complexity or predictor quantity for improving
        regional TWSA reconstruction in heavily managed basins,
        highlighting the value of incorporating anthropogenically
        sensitive storage components for drought assessment and water-
        resources management.}",
          doi = {10.1016/j.pce.2026.104765},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2027PCE...14504765B},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}