Thu Dec 12, F.Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Ding, Kuiyuan, Zhao, Xiaowei, Cheng, Jianmei, Yu, Ying, Luo, Yiming, Couchot, Joaquin, Zheng, Kun, Lin, Yihang, and Wang, Yanxin, 2025. GRACE/ML-based analysis of the spatiotemporal variations of groundwater storage in Africa. Journal of Hydrology, 647:132336, doi:10.1016/j.jhydrol.2024.132336.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025JHyd..64732336D,
author = {{Ding}, Kuiyuan and {Zhao}, Xiaowei and {Cheng}, Jianmei and {Yu}, Ying and {Luo}, Yiming and {Couchot}, Joaquin and {Zheng}, Kun and {Lin}, Yihang and {Wang}, Yanxin},
title = "{GRACE/ML-based analysis of the spatiotemporal variations of groundwater storage in Africa}",
journal = {Journal of Hydrology},
year = 2025,
month = feb,
volume = {647},
eid = {132336},
pages = {132336},
abstract = "{Groundwater is a crucial factor influencing the ecological security and
social-economic development in Africa. To support comprehensive
and sustainable management of groundwater resources in Africa,
GRACE and GLDAS data were utilized to extract information about
groundwater storage anomaly (GWSA) in Africa and its different
basins. Theil-Sen Median method, Mann-Kendall (MK) trend test
and the seasonal and trend decomposition LOESS method (STL) were
applied to reveal the long-term and seasonal spatiotemporal
trends in GWSA. Additionally, an interpretable machine learning
algorithm namely Extreme Gradient Boosting and SHAP model
(XGBoost-SHAP) was employed to analyze the driving processes of
the factors impacting GWSA in different basins. The study
results indicate that GWSA in Africa exhibited an overall upward
trend, with significant seasonal characteristics. In sub-Saharan
African basins, GWSA showed a significant increase trend, with
annual growth rates ranging from 2.75 cm/a to 8.02 cm/a. In
contrast, a declining GWSA trend was observed in the Sahara
region, with an annual decrease rate of 2.62 cm/a. Quantitative
analysis identified population density and normalized difference
vegetation index (NDVI) as the key factors influencing GWSA.
These findings allowed us to categorize the underlying
mechanisms driving GWSA across African basins into three types:
(1) anthropogenic activity-dominated regions; (2) natural
factor-dominated regions; (3) regions controlled by the
interaction of natural factors and human activities.
Understanding and monitoring the spatiotemporal heterogeneity of
GWSA and the differences in driving factors across different
basins is critical for a substantial improvement in the
management of groundwater resources in the different basins
across Africa.}",
doi = {10.1016/j.jhydrol.2024.132336},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025JHyd..64732336D},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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