Quantitative assessment method for catastrophic debris flow risks in the Bailong River Basin, China

Rapidly and quantitatively assessing potential catastrophic debris flow risks over a regional scale is crucial for early-stage risk prevention and control. However, significant differences in the developmental conditions of various debris flow catchments pose considerable challenges to regional risk assessment. To address this, this study proposes a method for the rapid quantitative estimation of the population threatened by debris flows in the Bailong River Basin, aiming to identify potential sites of catastrophic debris flows. First, the SCS hydrological model is used to predict the peak discharge and maximum outflow volume of debris flows under a designed rainfall scenario. Including the innovative application of machine learning to construct a sediment supply prediction model. Then, an empirical formula for debris flow deposition fans in the region is applied to estimate the potential hazard area. Finally, this potential hazard area is overlaid with spatially gridded population distribution data to estimate the number of people potentially threatened by each debris flow catchment. Utilizing publicly available fundamental data, this method constructs a comprehensive estimation process for debris flows, covering rainfall-runoff-outflow volume-hazard area-threatened population. It provides a robust and scalable solution for the rapid quantitative assessment of regional catastrophic debris flow risks.