Publications related to the GRACE Missions (no abstracts)

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A spatiotemporal deep learning framework integrating CNN–BiLSTM and attention mechanisms for GRACE data downscaling in Yunnan Province

He, Yang, Chen, Qi, Zhao, Zhifang, Cai, Dayu, Ouyang, Liu, Zhang, Xiaoxiao, Gao, Yu, and Zhou, Junrong, 2026. A spatiotemporal deep learning framework integrating CNN–BiLSTM and attention mechanisms for GRACE data downscaling in Yunnan Province. Computers and Geosciences, 209:106117, doi:10.1016/j.cageo.2026.106117.

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@ARTICLE{2026CG....20906117H,
       author = {{He}, Yang and {Chen}, Qi and {Zhao}, Zhifang and {Cai}, Dayu and {Ouyang}, Liu and {Zhang}, Xiaoxiao and {Gao}, Yu and {Zhou}, Junrong},
        title = "{A spatiotemporal deep learning framework integrating CNN-BiLSTM and attention mechanisms for GRACE data downscaling in Yunnan Province}",
      journal = {Computers and Geosciences},
     keywords = {Deep learning, GRACE, Spatial downscaling, Water storage, Drought},
         year = 2026,
        month = mar,
       volume = {209},
          eid = {106117},
        pages = {106117},
     abstract = "{The Gravity Recovery and Climate Experiment (GRACE) dataset has emerged
        as a pivotal tool for quantifying terrestrial water storage
        (TWS) anomalies at regional scales. However, its coarse spatial
        resolution ({\ensuremath{\sim}}3{\textdegree}) introduces
        substantial uncertainties in localized hydrological analyses. To
        overcome this limitation, we developed a spatiotemporal deep
        learning framework that synergistically integrates Convolutional
        Neural Networks (CNNs) and Bidirectional Long Short-Term Memory
        networks (BiLSTM), enhanced by a time-space attention mechanism.
        Applied to Yunnan Province, China, this framework achieved a
        tenfold resolution enhancement
        (1{\textdegree}─0.1{\textdegree}), preserving high consistency
        with raw GRACE data (cc = 0.94). Validation against independent
        datasets demonstrated a 6─15 \% improvement in Coefficient of
        Determination (R$^{2}$) over conventional downscaling methods,
        while maintaining moderate to strong correlations (r =
        0.53─0.74) with WGHM products and river-lake water level data.
        Multivariate analysis revealed statistically significant
        couplings between downscaled TWS variations and key
        environmental drivers, including soil moisture (SoilMoi), land
        surface temperature (LST), evapotranspiration (E), the
        Normalized Difference Vegetation Index (NDVI), and precipitation
        (TP). The refined GRACE Drought Severity Index (GRACE-DSI)
        exhibited enhanced synchronization with the Standardized
        Precipitation Evapotranspiration Index (SPEI), showing a >10 \%
        increase in correlation coefficients compared to pre-downscaling
        values. This methodological advancement enabled precise
        spatiotemporal characterization of drought dynamics during the
        2002─2023 period, particularly capturing the 2009─2012 extreme
        drought and 2019─2021 pluvial anomalies with sub-basin spatial
        fidelity. Our framework provides an operational solution for
        high-resolution hydrological monitoring, offering critical
        insights for adaptive water resource management in
        topographically complex regions.}",
          doi = {10.1016/j.cageo.2026.106117},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026CG....20906117H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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