Wang et al. (2025) Towards a Global Water Use Scarcity Risk Assessment Framework: Integration of Remote Sensing and Geospatial Datasets

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

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-12-11
• Authors: Yunhan Wang, Xueke Li, Guangqiu Jin, Luo Zhou, Muyun Sun, Yu Can Fu, Taixia Wu, Kai Liu
• DOI: 10.3390/rs17243999

Research Groups

Not specified in the provided text.

Short Summary

This study developed a storage-aware water scarcity risk assessment framework, integrating satellite remote sensing, geospatial data, and machine learning with the IPCC EHV paradigm, to evaluate global water scarcity dynamics over the past two decades, identifying high-risk regions and increasing vulnerability in Asia and Africa.

Objective

• To design and apply a storage-aware water-scarcity risk assessment framework to evaluate the spatiotemporal dynamics of global water scarcity risk over the past two decades.

Study Configuration

• Spatial Scale: Global
• Temporal Scale: Past two decades (approximately 20 years)

Methodology and Data

• Models used: Performance-weighted ensemble machine learning (for terrestrial water storage reconstruction and water withdrawal generation), IPCC Exposure-Hazard-Vulnerability (EHV) paradigm.
• Data sources: Satellite observations (for terrestrial water storage), glacier-mass calibration data, remote sensing, geospatial datasets (for water withdrawal and risk assessment).

Main Results

• Obvious terrestrial water storage (TWS) declines were observed in Asia, Northern Africa, and North America, particularly in irrigated drylands and glacier-dominated regions.
• High water scarcity risk was identified in Asia and Africa, especially in agricultural regions, using the EHV paradigm and big datasets.
• Water stress intensified in Africa over the past two decades, while a decreasing trend was observed in parts of Asia.
• Vulnerability levels in Asia and Africa were approximately eight times higher than in other global regions.
• A strong connection between water stress and socioeconomic factors was revealed in Asia and Africa, highlighting global disparities in water resource availability.

Contributions

• Development of a novel storage-aware water-scarcity risk assessment framework that integrates satellite remote sensing, geospatial datasets, and machine learning with the IPCC EHV paradigm.
• Reconstruction of long-term terrestrial water storage (TWS) using a performance-weighted ensemble machine learning approach with glacier-mass calibration, enhancing reliability in cryosphere-affected regions.
• Generation of a global water withdrawal dataset using remote sensing, geospatial data, and machine learning, reducing dependency on parameterized land surface hydrological models for consistent risk mapping.
• Comprehensive spatiotemporal evaluation of global water scarcity risk over two decades, providing detailed insights into regional trends and vulnerability disparities.

Funding

Not specified in the provided text.

Citation

@article{Wang2025Towards,
  author = {Wang, Yunhan and Li, Xueke and Jin, Guangqiu and Zhou, Luo and Sun, Muyun and Fu, Yu Can and Wu, Taixia and Liu, Kai},
  title = {Towards a Global Water Use Scarcity Risk Assessment Framework: Integration of Remote Sensing and Geospatial Datasets},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17243999},
  url = {https://doi.org/10.3390/rs17243999}
}