Mon Oct 13, F. Flechtner
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Deng, Xiaoya, Wang, Guangyan, Han, Feifei, Gong, Yanming, Hao, Xingming, Zhang, Guangpeng, Zhang, Pei, and Shan, Qianjuan, 2025. Groundwater storage anomalies projection by optimized deep learning refines groundwater management in typical arid basins. Journal of Hydrology, 649:132452, doi:10.1016/j.jhydrol.2024.132452.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025JHyd..64932452D,
author = {{Deng}, Xiaoya and {Wang}, Guangyan and {Han}, Feifei and {Gong}, Yanming and {Hao}, Xingming and {Zhang}, Guangpeng and {Zhang}, Pei and {Shan}, Qianjuan},
title = "{Groundwater storage anomalies projection by optimized deep learning refines groundwater management in typical arid basins}",
journal = {Journal of Hydrology},
keywords = {GRACE-based groundwater storage anomalies, Multi-scale geographically weighted regression, Multi-strategy gray wolf optimization algorithm, Deep learning, Arid basins, Case study},
year = 2025,
month = mar,
volume = {649},
eid = {132452},
pages = {132452},
abstract = "{The GRACE satellite provides tools for accurately characterizing the
spatiotemporal variations of regional groundwater storage
anomalies (GWSA) under the background of climate change and
anthropogenic disturbances. However, its low spatial resolution
restricts the refined management of groundwater. Multi-scale
geographically weighted regression (MGWR) residuals are
innovatively introduced for bias correction, which improves the
GRACE-based GWSA downscaling accuracy (average R<SUP
loc=``post''>2</SUP> = 0.98). Further application of the K-means
identifies four spatial distribution patterns of GWSA in the
Tarim River mainstream (TRM), which showed a downward trend from
2003 to 2020. However, under effective groundwater management
(such as ecological water transfer, ecological gate water
diversion, etc.), the decline rate is gradually decreasing.
Feature contribution analysis demonstrates that soil moisture
storage (SMS), land surface temperature (LST), and normalized
difference vegetation index (NDVI) are the primary driving
factors of GWSA changes. Using the long short-term memory (LSTM)
deep learning model optimized by multi-strategy gray wolf
optimization algorithm (MSGWO), the GWSA of four spatial
patterns is predicted under two shared socioeconomic pathways
(SSPs, including SSP245 and SSP585). The model achieved a
maximum R/NSE of 0.95/0.91 on the train dataset and 0.88/0.71 on
the test dataset, outperforming similar models. The future
groundwater reserves of TRM will show an improving trend,
indicating that groundwater management has achieved significant
benefits. Notably, high emissions without government
intervention (SSP585) have exacerbated the risk of groundwater
resource shortages, and refined groundwater management needs to
be further strengthened in the future. Overall, the proposed
GRACE-based GWSA downscaling framework and MSGWO-LSTM predictive
model provide tools for the refined scientific management of
groundwater in arid basins.}",
doi = {10.1016/j.jhydrol.2024.132452},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025JHyd..64932452D},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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