Flood Susceptibility Mapping and Runoff Modeling in the Upper Baishuijiang River Basin, China.
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| Title: | Flood Susceptibility Mapping and Runoff Modeling in the Upper Baishuijiang River Basin, China. |
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| Authors: | Wang, Hao1,2 (AUTHOR), Niu, Quanfu2,3,4 (AUTHOR) niuqf@lut.edu.cn, Lei, Jiaojiao2,3,5 (AUTHOR), Cheng, Weiming4,6 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1270. 27p. |
| Subjects: | Flood risk, Runoff models, Hydrological research, Maximum entropy method, Climate change, Particle swarm optimization, Mountain watersheds, Watersheds |
| Geographic Terms: | China |
| Abstract: | Highlights: What are the main findings? Medium-to-high flood susceptibility areas remain generally stable but exhibit a moderate expansion (+5.2% ± 0.8%) under future climate scenarios. Runoff dynamics are mainly controlled by watershed properties, particularly infiltration capacity, recession behavior, and CN-related parameters, which regulate peak discharge and hydrological response. What are the implications of the main findings? Flood risk management should focus on areas with increasing susceptibility along river corridors and enhance infiltration and storage capacity to mitigate runoff concentration. Integrating susceptibility mapping with physically based hydrological modeling provides a robust framework for improving flood prediction and climate adaptation strategies in mountainous basins. Mountain flood susceptibility in complex mountainous basins is strongly influenced by terrain–climate interactions; however, the linkage between spatial susceptibility patterns and hydrological processes remains poorly understood. This study proposes a process-oriented framework that explicitly links flood susceptibility patterns with hydrological processes, moving beyond conventional approaches that rely on independent model integration. The Baishuijiang River Basin, located in Wenxian County, southern Gansu Province, China, is selected as a representative mountainous watershed for this analysis. The specific conclusions are as follows: (1) Flood susceptibility was mapped using a Particle Swarm Optimization (PSO)-enhanced Maximum Entropy (MaxEnt) model based on multi-source environmental variables, including climatic, terrain, soil, land cover, and vegetation factors. The model achieved high predictive accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.912), identifying precipitation of the driest month (bio14), elevation, and land use as dominant controlling factors. Medium-to-high-susceptibility areas account for approximately 22% of the basin and are mainly distributed along river valleys and flow convergence areas. These patterns are strongly associated with reduced infiltration capacity under dry antecedent conditions and enhanced flow concentration in steep terrain, and they exhibit clear nonlinear responses and threshold effects. (2) Hydrological simulations using Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) show good agreement with observed runoff (Nash–Sutcliffe Efficiency (NSE) = 0.74–0.85). Sensitivity analysis indicates that runoff dynamics are primarily controlled by the Curve Number (CN), recession constant, and ratio to peak, corresponding to infiltration capacity, recession processes, and peak discharge amplification. The spatial consistency between high-susceptibility areas and areas of strong runoff response demonstrates that susceptibility patterns can be physically explained through hydrological processes, providing a process-based interpretation rather than a purely statistical prediction. (3) Future projections indicate that medium–high-susceptibility areas remain generally stable but show a gradual expansion (+5.2% ± 0.8%) and increasing concentration along river corridors under climate change scenarios. This reflects intensified precipitation variability and enhanced runoff concentration processes, suggesting a climate-driven amplification of flood risk in hydrologically connected areas. Overall, this study goes beyond conventional susceptibility assessment by establishing a physically interpretable framework that provides a consistent linkage between environmental controls, susceptibility patterns, and hydrological responses. The proposed approach is transferable to similar mountainous basins with strong terrain–climate interactions, although uncertainties related to data limitations and single-basin application remain and require further investigation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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