@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{2026JHyd..67135275H,
       author = {{Hu}, Ying and {Cai}, Xiaoming and {Xu}, Yue-Ping and {Gu}, Haiting and {Xie}, Jingkai and {Dai}, Sirui},
        title = "{A semi-supervised LSTM framework for spatiotemporal downscaling of GRACE-derived terrestrial water storage anomalies to improve flood monitoring in the Yarlung Tsangpo River basin}",
      journal = {Journal of Hydrology},
     keywords = {GRACE/GRACE-FO, YarlungTsangpo River Basin, Semi-supervised learning, Spatiotemporal downscaling, Flood monitoring},
         year = 2026,
        month = may,
       volume = {671},
          eid = {135275},
        pages = {135275},
     abstract = "{The Gravity Recovery and Climate Experiment (GRACE) and its follow-on
        mission (GRACE-FO) provide essential observations for flood
        monitoring in medium and large basins. However, their
        applications are constrained by coarse spatiotemporal
        resolution, data gaps, and product inconsistencies. In this
        study, GRACE-derived terrestrial water storage anomalies (GRACE-
        TWSA) products from three major agencies were fused using the
        Bayesian three cornered hat (BTCH) to reduce uncertainty in the
        Yarlung Tsangpo River Basin (YTRB), China. Building on this, we
        developed a novel semi-supervised spatiotemporal downscaling
        framework based on long short-term memory networks (SSDF-LSTM).
        This framework leveraged both labeled and unlabeled data,
        applied physical constraint during pseudo-label refinement, and
        adopted a spatially stratified training strategy that preserved
        complete temporal signals. As a result, the monthly
        0.5{\textdegree} TWSA was downscaled to daily 0.25{\textdegree}
        resolution while filling missing data and maintaining high
        consistency with GRACE observations, achieving a correlation
        coefficients (CC) of 0.97, Nash-Sutcliffe efficiencies (NSE) of
        0.92, and root mean square error (RMSE) of 22.26 mm.
        Furthermore, we proposed a daily detrended normalized flood
        potential index (D-DNFPI) which demonstrated higher accuracy
        (0.85) and precision (0.80) than conventional indices. Overall,
        this study established a robust methodological framework for
        enhancing the spatiotemporal resolution of GRACE-TWSA and
        advancing its applications in flood monitoring.}",
          doi = {10.1016/j.jhydrol.2026.135275},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026JHyd..67135275H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}