Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
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.
• from the NASA Astrophysics Data System • by the DOI System •
@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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