Wang et al. (2026) Flood Susceptibility Mapping and Runoff Modeling in the Upper Baishuijiang River Basin, China

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Identification

• Journal: Remote Sensing
• Year: 2026
• Date: 2026-04-22
• Authors: Hao Wang, Quanfu Niu, Jiaojiao Lei, Weiming Cheng
• DOI: 10.3390/rs18091270

Research Groups

Not specified in the provided text.

Short Summary

This study develops a process-oriented framework to link mountain flood susceptibility patterns with hydrological processes in the Baishuijiang River Basin, China, demonstrating that susceptibility is driven by terrain-climate interactions and runoff dynamics.

Objective

• To establish an explicit linkage between spatial flood susceptibility patterns and underlying hydrological processes, moving beyond conventional purely statistical prediction models.

Study Configuration

• Spatial Scale: Baishuijiang River Basin, Wenxian County, southern Gansu Province, China.
• Temporal Scale: Not explicitly stated (includes historical observed runoff and future climate change projections).

Methodology and Data

• Models used: Particle Swarm Optimization (PSO)-enhanced Maximum Entropy (MaxEnt) model for susceptibility mapping; Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) for hydrological simulation.
• Data sources: Multi-source environmental variables (climatic, terrain, soil, land cover, and vegetation factors) and observed runoff data.

Main Results

• Susceptibility Mapping: The MaxEnt model achieved high accuracy (AUC = 0.912), identifying the precipitation of the driest month (bio14), elevation, and land use as the dominant controlling factors.
• Spatial Distribution: Medium-to-high-susceptibility areas cover approximately 22% of the basin, primarily concentrated in river valleys and flow convergence areas.
• Hydrological Validation: HEC-HMS simulations showed strong agreement with observed runoff (NSE = 0.74–0.85), with runoff dynamics primarily controlled by the Curve Number (CN), recession constant, and ratio to peak.
• Future Projections: Under climate change scenarios, medium-to-high-susceptibility areas are projected to expand by 5.2% ± 0.8%, with increased concentration along river corridors.

Contributions

• The study provides a physically interpretable framework that bridges the gap between statistical susceptibility assessment and process-based hydrological modeling, allowing susceptibility patterns to be explained through physical hydrological responses.

Funding

Not specified in the provided text.

Citation

@article{Wang2026Flood,
  author = {Wang, Hao and Niu, Quanfu and Lei, Jiaojiao and Cheng, Weiming},
  title = {Flood Susceptibility Mapping and Runoff Modeling in the Upper Baishuijiang River Basin, China},
  journal = {Remote Sensing},
  year = {2026},
  doi = {10.3390/rs18091270},
  url = {https://doi.org/10.3390/rs18091270}
}