GRACE and GRACE-FO Related Publications (no abstracts)

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Groundwater Storage Response to Extreme Hydrological Events in Poyang Lake, China's Largest Fresh-Water Lake

Yu, Xilin, Lu, Chengpeng, Park, Edward, Zhang, Yong, Wu, Chengcheng, Li, Zhibin, Chen, Jing, Hannan, Muhammad, Liu, Bo, and Shu, Longcang, 2025. Groundwater Storage Response to Extreme Hydrological Events in Poyang Lake, China's Largest Fresh-Water Lake. Remote Sensing, 17(6):988, doi:10.3390/rs17060988.

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@ARTICLE{2025RemS...17..988Y,
       author = {{Yu}, Xilin and {Lu}, Chengpeng and {Park}, Edward and {Zhang}, Yong and {Wu}, Chengcheng and {Li}, Zhibin and {Chen}, Jing and {Hannan}, Muhammad and {Liu}, Bo and {Shu}, Longcang},
        title = "{Groundwater Storage Response to Extreme Hydrological Events in Poyang Lake, China's Largest Fresh-Water Lake}",
      journal = {Remote Sensing},
     keywords = {extreme floods and droughts, groundwater storage, GRACE/GRACE-FO, CNN-A-LSTM, Poyang Lake},
         year = 2025,
        month = mar,
       volume = {17},
       number = {6},
          eid = {988},
        pages = {988},
     abstract = "{Groundwater systems are important for maintaining ecological balance and
        ensuring water supplies. However, under the combined pressures
        of shifting climate patterns and human activities, their
        responses to extreme events have become increasingly complex. As
        China's largest freshwater lake, Poyang Lake supports critical
        water resources, ecological health, and climate adaptation
        efforts. Yet, the relationship between groundwater storage (GWS)
        and extreme hydrological events in this region remains
        insufficiently studied, hindering effective water management.
        This study investigates the GWS response to extreme events by
        downscaling Gravity Recovery and Climate Experiment (GRACE) data
        and validating it with five years of observed daily groundwater
        levels. Using GRACE, the Global Land Data Assimilation System
        (GLDAS), and ERA5 data, a convolutional neural network
        (CNN){\textendash}attention mechanism (A){\textendash}long
        short-term memory (LSTM) model was selected to downscale with
        high resolution (0.1{\textdegree} {\texttimes} 0.1{\textdegree})
        and estimate recovery times for GWS to return to baseline. Our
        analysis revealed seasonal GWS fluctuations that are in phase
        with precipitation, evapotranspiration, and groundwater runoff.
        Recovery durations for extreme flood (2020) and drought (2022)
        events ranged from 0.8 to 3.1 months and 0.2 to 4.8 months,
        respectively. A strong correlation was observed between
        groundwater and meteorological droughts, while the correlation
        with agricultural drought was significantly weaker. These
        results indicate that precipitation and groundwater runoff are
        more sensitive to extreme events than evapotranspiration in
        influencing GWS changes. These findings highlight the
        significant sensitivity of precipitation and runoff to GWS,
        despite improved management efforts.}",
          doi = {10.3390/rs17060988},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025RemS...17..988Y},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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