Mon Aug 24, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Wang, Yuhao, Zheng, Zhijie, Zhu, Guofeng, Huang, Enwei, Meng, Gaojia, Lu, Siyu, Qiu, Dongdong, Chen, Longhu, Li, Rui, Jiao, Yinying, Zhao, Ling, Qi, Xiaoyu, Wang, Qinqin, Li, Wenmin, Miao, Yuxin, and Wang, Qingyang, 2026. Multi–source remote sensing unveils hydrological dynamics and drought escalation in Central Asia's arid regions. Journal of Geographical Sciences, 36(5):1105–1129, doi:10.1007/s11442-026-2484-y.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026JGSci..36.1105W,
author = {{Wang}, Yuhao and {Zheng}, Zhijie and {Zhu}, Guofeng and {Huang}, Enwei and {Meng}, Gaojia and {Lu}, Siyu and {Qiu}, Dongdong and {Chen}, Longhu and {Li}, Rui and {Jiao}, Yinying and {Zhao}, Ling and {Qi}, Xiaoyu and {Wang}, Qinqin and {Li}, Wenmin and {Miao}, Yuxin and {Wang}, Qingyang},
title = "{Multi-source remote sensing unveils hydrological dynamics and drought escalation in Central Asia's arid regions}",
journal = {Journal of Geographical Sciences},
keywords = {Central Asia's arid regions, drought dynamics, multi-source remote sensing, GRACE, hydrometeorological drivers, Random Forest, Earth Sciences, Physical Geography and Environmental Geoscience},
year = 2026,
month = may,
volume = {36},
number = {5},
pages = {1105-1129},
abstract = "{Despite the reported ``warming-wetting'' trend, Central Asia faces
severe water insecurity due to climate shifts and anthropogenic
activities. This study integrates multi-source remote sensing
data (GRACE, TRMM, MODIS) with machine learning to analyze
drought dynamics from 2003 to 2022 using the Water Storage
Deficit Index (WSDI). Results reveal significant declines in
terrestrial water storage (TWS) particularly in the Tianshan
Mountains ({\ensuremath{-}}10.20 mm/yr) and the Central Desert
({\ensuremath{-}}6.19 mm/yr). Drought severity has intensified
since 2014, with 67\% of subregions transitioning to moderate
drought. Random Forest modeling indicates that drought is no
longer solely climate-driven but is increasingly dominated by
anthropogenic factors (GDP, urbanization, cropland), which
explain over 85\% of the variability. Furthermore, the WSDI
outperformed traditional indices by identifying 12 major drought
events linked to deep aquifer depletion{\textemdash}a ``hidden''
structural deficit often overlooked by surface-based metrics.
These findings challenge the optimistic ``warming-wetting''
narrative, highlighting the urgent need for storage-based
management strategies to address anthropogenic groundwater
depletion.}",
doi = {10.1007/s11442-026-2484-y},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026JGSci..36.1105W},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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