Liu et al. (2025) Assessing groundwater sustainability across high mountain Asia using remote sensing

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Environmental Research Letters
• Year: 2025
• Date: 2025-12-17
• Authors: Kai Liu, Xueke Li, Yong Bo, Shudong Wang, Guangsheng Zhou
• DOI: 10.1088/1748-9326/ae2e1b

Research Groups

Not explicitly mentioned in the abstract, but the study involves expertise in remote sensing, Earth system modeling, and artificial intelligence.

Short Summary

This study integrates remote sensing, Earth system modeling, and artificial intelligence to quantify historical groundwater storage (GWS) trends and project future evolutions in High Mountain Asia, revealing widespread GWS declines (24.2 gigatonnes per year, 2003-2020) driven by climate (47%), human activities (38%), and cryospheric processes (15%), with projections indicating accelerated depletion by the century's end despite temporary cryospheric buffering.

Objective

• To assess historical trends and project future evolutions in groundwater storage (GWS) across High Mountain Asia (HMA), specifically quantifying the impacts of climate changes and human activities.

Study Configuration

• Spatial Scale: High Mountain Asia (HMA), including densely populated and irrigated downstream basins such as Ganges–Brahmaputra and Indus.
• Temporal Scale: Historical (2003–2020), long-term future projections (through the end of the century), with specific mention of cryospheric buffering until the 2060s.

Methodology and Data

• Models used: Earth system modeling, Transformer-based artificial intelligence (AI) framework.
• Data sources: Remote sensing, 2554 in situ groundwater well observations.

Main Results

• Approximately 69% of High Mountain Asia experienced groundwater storage (GWS) declines during 2003–2020.
• Annual GWS losses totaled −24.2 gigatonnes per year (Gt/year), predominantly in densely populated and irrigated downstream basins (e.g., Ganges–Brahmaputra, Indus).
• GWS variability attribution: 47% to direct climate drivers, 15% to cryospheric processes, and up to 38% to human activities.
• The identified GWS decline and its intensifying pattern were corroborated by 2554 in situ groundwater well observations.
• Future projections indicate a sustained climate-induced threat to groundwater sustainability, with cryospheric melt temporarily offsetting losses until the 2060s, after which this buffering effect will diminish, leading to accelerated depletion.

Contributions

• Provides a comprehensive assessment of historical and future groundwater storage (GWS) evolution in High Mountain Asia by integrating remote sensing, Earth system modeling, and artificial intelligence.
• Quantifies the relative contributions of climate drivers, cryospheric processes, and human activities to GWS variability and decline.
• Offers data-driven projections highlighting future threats to groundwater sustainability and the diminishing buffering capacity of cryospheric melt.
• Demonstrates the utility of combining remote sensing with explainable AI for improved groundwater assessment and climate-resilient water management in high mountain regions globally.

Funding

Not explicitly mentioned in the abstract.

Citation

@article{Liu2025Assessing,
  author = {Liu, Kai and Li, Xueke and Bo, Yong and Wang, Shudong and Zhou, Guangsheng},
  title = {Assessing groundwater sustainability across high mountain Asia using remote sensing},
  journal = {Environmental Research Letters},
  year = {2025},
  doi = {10.1088/1748-9326/ae2e1b},
  url = {https://doi.org/10.1088/1748-9326/ae2e1b}
}