GRACE and GRACE-FO Related Publications (no abstracts)

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Enhanced Flood Monitoring in the Pearl River Basin via GAIN-Reconstructed GRACE Terrestrial Water Storage Anomalies

Wang, Jing, Li, Haiyang, Wu, Shuguang, Nie, Guigen, and Wang, Yawei, 2024. Enhanced Flood Monitoring in the Pearl River Basin via GAIN-Reconstructed GRACE Terrestrial Water Storage Anomalies. Remote Sensing, 16(24):4727, doi:10.3390/rs16244727.

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@ARTICLE{2024RemS...16.4727W,
       author = {{Wang}, Jing and {Li}, Haiyang and {Wu}, Shuguang and {Nie}, Guigen and {Wang}, Yawei},
        title = "{Enhanced Flood Monitoring in the Pearl River Basin via GAIN-Reconstructed GRACE Terrestrial Water Storage Anomalies}",
      journal = {Remote Sensing},
     keywords = {flood, GRACE-FO, Pearl River Basin, reconstruction, generative adversarial imputation networks},
         year = 2024,
        month = dec,
       volume = {16},
       number = {24},
          eid = {4727},
        pages = {4727},
     abstract = "{Floods are a significant and pervasive threat globally, exacerbated by
        climate change and increasing extreme weather events. The
        Gravity Recovery and Climate Experiment (GRACE) and its follow-
        on mission (GRACE-FO) provide crucial insights into terrestrial
        water storage anomalies (TWSA), which are vital for
        understanding flood dynamics. However, the observational gap
        between these missions presents challenges for flood monitoring,
        affecting the estimation of long-term trends and limiting the
        analysis of interannual variability, thereby impacting overall
        analysis accuracy. Reconstructing the missing data between GRACE
        and GRACE-FO is essential for systematically understanding the
        spatiotemporal distribution characteristics and driving
        mechanisms of interannual changes in regional water reserves. In
        this study, the Generative Adversarial Imputation Network (GAIN)
        is applied to improve the monitoring capability for flood events
        in the Pearl River Basin (PRB). First, the GRACE/GRACE-FO TWSA
        data gap is imputed with GAIN and compared with long short-term
        memory (LSTM) and k-Nearest Neighbors (KNN) methods. Using the
        reconstructed data, we develop the Flood Potential Index (FPI)
        by integrating GRACE-based TWSA with precipitation data and
        analyze key characteristics of FPI variability against actual
        flood events. The results indicate that GAIN effectively
        predicts the GRACE/GRACE-FO TWSA gap, with an average
        improvement of approximately 50.94\% over LSTM and 68.27\% over
        KNN. The reconstructed FPI proves effective in monitoring flood
        events in the PRB, validating the reliability of the
        reconstructed TWSA. Additionally, the FPI achieves a predictive
        accuracy of 79.7\% for real flood events, indicating that short-
        term flood characteristics are better captured using TWSA. This
        study demonstrates the effectiveness of GAIN in enhancing data
        continuity, providing a reliable framework for large-scale flood
        risk assessment and offering valuable insights for flood
        management in vulnerable regions.}",
          doi = {10.3390/rs16244727},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024RemS...16.4727W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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