Debris flows are rapid, destructive landslides that pose significant risks in mountainous regions. This study presents a novel algorithm to simulate debris flow dynamics, focusing on sediment transport from 0-order basins to depositional zones. The algorithm integrates the D8 flow direction method with an adjustable friction coefficient to enhance the accuracy of debris flow trajectory and deposition modeling. Its performance was evaluated on three real-world cases in the Otoishi River basin, affected by rainfall-induced debris flows in July 2017, and the Aso Bridge landslide triggered by the 2016 Kumamoto Earthquake. By utilizing diverse friction coefficients, the study effectively captured variations in debris flow behavior, transitioning from fluid-like to more viscous states. Simulation results demonstrated a precision of 88.9% in predicting debris flow paths and deposition areas, emphasizing the pivotal role of the friction coefficient in regulating mass movement dynamics. Additionally, Monte Carlo (MC) simulations enhanced the identification of critical slip surfaces within 0-order basins, increasing the accuracy of debris flow source detection. This research offers valuable insights into debris flow hazards and risk mitigation strategies. The algorithm’s proven effectiveness in simulating real-world scenarios highlights its potential for integration into disaster risk assessment and prevention frameworks. By providing a reliable tool for hazard identification and prediction, this study supports proactive disaster management and aligns with the goals of sustainable development in regions prone to debris flow disasters.
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