Improving Four-pre ability for flood in alpine regions
Alpine regions face severe flood management challenges due to complex terrain, climate extremes, and sparse data. This commentary critiques limitations in current hydrological practices supporting China’s Four-pre strategy. Key barriers include poor data quality, model limitations, and fragmented systems. Thus, we recommend optimizing observation networks, using physics-informed neural networks, and establishing unified data platforms. This integrated approach will enhance flood modeling reliability in data-scarce regions, providing actionable insights for adaptive flood governance in high-risk mountainous regions.
Authors
- Haowen Li (ORCID: https://orcid.org/0000-0003-2746-4000)
- Zhipan Niu (ORCID: https://orcid.org/0000-0001-8679-382X)
- Zhongshun Gu
- Y.-R. Guo (ORCID: https://orcid.org/0009-0004-1635-779X)
- Li Zhou (ORCID: https://orcid.org/0000-0002-3384-1784)
- Xiaopeng Wang (ORCID: https://orcid.org/0000-0001-7977-1518)
- Biqiong Wu
- Lingling Wu (ORCID: https://orcid.org/0000-0003-2490-6192)
- Chun Zhou
Institutions
- Hong Kong Polytechnic University (HK)
- China Three Gorges University (CN)
- Sichuan University (CN)
- Inner Mongolia Autonomous Region Meteorological Bureau (CN)
- Yangtze River Pharmaceutical Group (China) (CN)
- State Key Laboratory of Hydraulics and Mountain River Engineering
Publication Details
- Journal
- Eco-Environment & Health
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1016/j.eehl.2025.100189
- Primary Topic
- Flood Risk Assessment and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Department of Science and Technology of Sichuan Province