Channel erosion and sediment entrainment amplify rainstorm-triggered debris flows, but their interaction with topographic constraints remains poorly quantified. To reveal the controlling mechanism of erosion amplification on the disaster-forming process of rainstorm-type debris flows, using the 2016 Zhangba Xiaosha River event in Yunnan, China, we reconstructed debris-flow motion, erosion, and deposition with RAMMS::DEBRISFLOW. Five bed-erosion scenarios (0, 0.25, 0.50, 0.75 and 1.0 m) were used to assess changes in deposit thickness, runout, velocity, impact pressure, and volume amplification. The 1.0 m baseline scenario successfully reproduced the observed path, depositional extent, and representative deposit thickness. Increasing the equivalent erosion depth from 0 to 1.0 m increased the total debris-flow volume from 2.0 × 10⁴ to 4.27 × 10⁴ m³, yielding an amplification factor of 2.14; without erosion, maximum runout was 44.2% shorter. Along-channel erosion supply increases the mass, flow depth and momentum of the moving mass, forming an amplification chain of “sediment supply−scale amplification−momentum maintenance−frontal propagation”. Feedback may exist between actual erosion intensity, flow velocity and flow depth, but the equivalent erosion depth in RAMMS scenarios in this study is a predefined parameter. Topography exerted reach-specific controls: steep upstream channels promoted initiation and acceleration, the middle reach dominated erosional bulking, and the wider, gentler downstream reach enhanced dissipation and deposition. Within the erosion scenarios of 0.75–1.0 m set in this study, when the equivalent erosion depth increased further, the newly entrained sediment mainly increased deposit thickness and inundation extent, while the increment of maximum runout distance was limited. The event disaster scale resulted from coupled erosion-induced amplification and longitudinal topographic constraints. Hazard assessments should integrate initial initiation volume, erodible channel sediment, and local reach-scale topography.

Climate-driven extreme rainfall is increasing landslide risk, whereas conventional regional rainfall thresholds inadequately represent spatial variations in slope preconditioning across complex mountainous terrain. This study develops a dynamic early-warning framework that couples slope unit landslide susceptibility with rainfall triggering for Jinyang County, Southwest China. The county was divided into 7249 slope units, and eight conditioning factors were used to train support vector machine (SVM) and extreme gradient boosting (XGBoost) models with Bayesian hyperparameter optimization. Bayesian-optimized XGBoost (BO-XGBoost) achieved the best discrimination, with an area under the curve (AUC) of 0.893. For temporal triggering, 113 historical rainfall-landslide events were analyzed in logarithmic intensity–duration (I–D) space. A power-law fit described the central trend, and the fifth percentile of residuals defined a 5% non-exceedance threshold exceeded by approximately 95% of triggering events. Residual-based thresholds were used to establish four rainfall-trigger levels. A gated risk matrix coupled these levels with susceptibility classes. Validation using 162 online monitoring sites during the 21 August 2023 extreme rainfall event yielded 85.2% accuracy, 83.6% precision, 81.2% recall, 88.2% specificity, and an F1 score of 82.4%, demonstrating practical potential for spatially refined regional landslide early warning.