Abstract
Terrestrial water storage (TWS) in China, with the world's largest irrigated expanse and extensive mid-low latitude glaciers, is essential for effective water resource management and socioeconomic risk adaptation. However, the responses of TWS to human intervention and climate change, both during historical periods and under future scenarios, remain inadequately quantified. We reconstruct and project long-term TWS using a data-driven framework that integrates remote sensing, Earth system model (ESM) and machine learning. Our reconstructed record reveals an amplified TWS decline in China's drylands and a moderate yet persistent TWS reduction in glacier regions during 1985–2015, accentuated since the 21st century with a 13% increase in affected areas. TWS changes in drylands are primarily attributed to human irrigation (∼39%) and precipitation (∼24%), with the impacts of irrigation magnified by 9%–12% during drought. Humid basins show a moderate TWS response to irrigation and precipitation, modulated by intricate but unexplored interactions between atmospheric drivers, glacier-snow dynamics and underlying hydrological processes. Such discrepant response highlights the necessity for region-specific water resource management strategies: northern drylands should prioritize optimized irrigation practices while southern humid basins would benefit from enhanced adaptation to climate variability. Projections from nine ESMs indicate a likely amplification of TWS decline (13%–43%) in drylands and glacial zones by mid-century if maintaining current human intervention levels. Our findings emphasize the need to reassess climate change-induced water scarcity and refine human management regulations, particularly as existing strategies may be overlooking broader sustainability challenges in a warming climate. © 2025. The Author(s). Earth's Future published by Wiley Periodicals LLC on behalf of American Geophysical Union.
| Original language | English |
|---|---|
| Article number | e2025EF006071 |
| Number of pages | 22 |
| Journal | Earth's Future |
| Volume | 13 |
| Issue number | 10 |
| Online published | 20 Oct 2025 |
| DOIs | |
| Publication status | Published - Oct 2025 |
| Externally published | Yes |
Funding
This study has been supported by the International Research Center of Big Data for Sustainable Development Goals (CBAS) (Grant No. CBASYX0906), the National Key R&D Program of China (Grant No. 2024YFF1308200), and the National Natural Science Foundation of China (Grant No. 42141007).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 6 Clean Water and Sanitation
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SDG 13 Climate Action
Research Keywords
- climate change
- glacier
- irrigation water
- machine learning
- remote sensing
- terrestrial water resources
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