Ren et al. (2026) An enhanced GRACE-based index for nationwide flood-prone region monitoring in China from 2003 to 2022

Identification

• Journal: Remote Sensing of Environment
• Year: 2026
• Date: 2026-07-30
• Authors: Wei Ren, Yuyue Xu, Yubo Zhang, Shuang Luo, Jianli Chen, Chang‐Qing Ke
• DOI: 10.1016/j.rse.2026.115600

Research Groups

• Jiangsu Provincial Key Laboratory for Advanced Remote Sensing and Geographic Information Technology (Nanjing University)
• Department of Land Surveying and Geo-Informatics and Research Institute for Land and Space (The Hong Kong Polytechnic University)
• State Key Laboratory of Climate Resilience for Coastal Cities, Department of Land Surveying and Geospatial Science (The Hong Kong Polytechnic University)

Short Summary

This study developed the Optimal Flood Potential Index (OFPI) to improve large-scale monitoring of flood-prone regions in China from 2003 to 2022. OFPI showed better detection performance than the existing Flood Potential Index (FPI).

Objective

• Investigate the spatiotemporal evolution of flood-prone regions across China using a monthly terrestrial water storage anomaly dataset with improved spatial resolution.

Study Configuration

• Spatial Scale: Nationwide in China, with a spatial resolution of 0.5°.
• Temporal Scale: Monthly from 2003 to 2022.

Methodology and Data

• Models used: GRACE-based index for terrestrial water storage anomaly.
• Data sources: Satellite data (GRACE) and flood records from the Dartmouth Flood Observatory (DFO).

Main Results

• OFPI identified high-risk areas primarily located in southern China during June, shifting northward in July and August.
• Long-term trend analysis revealed regional differences: northern China showed a decreasing tendency in flood risk, while southern China exhibited an increasing trend.
• Seasonal analyses indicated that summer presented the most pronounced and spatially coherent trends.

Contributions

• OFPI provided a more effective framework for monitoring the spatiotemporal evolution of flood-prone regions compared to FPI.
• Improved detection performance was demonstrated through validation with DFO flood records, showing increased recall (0.715) and F1-score (0.659).

Funding

• This research was supported by the Jiangsu Provincial Key Laboratory for Advanced Remote Sensing and Geographic Information Technology (Nanjing University).
• The Hong Kong Polytechnic University provided funding through the State Key Laboratory of Climate Resilience for Coastal Cities.
• Additional support came from the Ministry of Natural Resources, China.

Citation

@article{Ren2026enhanced,
  author = {Ren, Wei and Xu, Yuyue and Zhang, Yubo and Luo, Shuang and Chen, Jianli and Ke, Chang‐Qing},
  title = {An enhanced GRACE-based index for nationwide flood-prone region monitoring in China from 2003 to 2022},
  journal = {Remote Sensing of Environment},
  year = {2026},
  doi = {10.1016/j.rse.2026.115600},
  url = {https://doi.org/10.1016/j.rse.2026.115600}
}