Thu Aug 14, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Ma, Xinjing, Huang, Haijun, Chen, Jinwen, Yu, Qiang, and Cai, Xitian, 2025. Exploring the Main Driving Factors for Terrestrial Water Storage in China Using Explainable Machine Learning. Remote Sensing, 17(12):2078, doi:10.3390/rs17122078.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025RemS...17.2078M,
author = {{Ma}, Xinjing and {Huang}, Haijun and {Chen}, Jinwen and {Yu}, Qiang and {Cai}, Xitian},
title = "{Exploring the Main Driving Factors for Terrestrial Water Storage in China Using Explainable Machine Learning}",
journal = {Remote Sensing},
keywords = {terrestrial water storage, machine learning, China, SHAP},
year = 2025,
month = jun,
volume = {17},
number = {12},
eid = {2078},
pages = {2078},
abstract = "{Terrestrial water storage (TWS) is a critical component of the
hydrological cycle and plays a key role in regional water
resource management. The launch of the Gravity Recovery and
Climate Experiment (GRACE) satellite mission in 2002 has
provided precise measurements of TWS, enabling systematic
investigations into its spatial pattern and driving mechanisms.
However, a comprehensive evaluation of the spatial drivers of
TWS variations across China is still lacking. In this study, we
employed a robust machine learning model to capture the spatial
patterns of TWS in China and further applied the Shapley
Additive Explanations (SHAP) method to disentangle the
individualized effects of hydroclimatic variables. Our findings
reveal that precipitation is the dominant driver in northern and
southern China, while soil moisture and snow water equivalent
are key contributors on the Tibetan Plateau. In northwestern
China, air pressure and groundwater runoff are the main
influencing factors, whereas temperature shows a pronounced
negative effect. Importantly, most variables demonstrate non-
monotonic influences: in particular, we found that the
importance of precipitation diminishes beyond a certain
threshold, and surface pressure shifts sharply toward a negative
impact. The explainable machine learning framework demonstrated
strong adaptability in identifying complex drivers of TWS,
offering a powerful methodological advancement for exploring TWS
dynamics and providing valuable insights for water resource
management in China.}",
doi = {10.3390/rs17122078},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025RemS...17.2078M},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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