A debris flow descending through an erodible convex colluvial bed,originating from a landslide dam and its upstream deposits,can entrain massive amounts of sediment,dramatically increasing the debris flow volume.Most ...A debris flow descending through an erodible convex colluvial bed,originating from a landslide dam and its upstream deposits,can entrain massive amounts of sediment,dramatically increasing the debris flow volume.Most existing erosion models assume that bed sediments are fully saturated,although this condition is rarely observed in nature.Therefore,a thorough understanding of debris flow overtopping erosion on a convex unsaturated bed is crucial for quantifying disaster risk.In this study,we experimentally investigated the effects of sediment composition,specifically coarse-grain size distribution and fine particle content,on the pore pressure evolution and entrainment of debris flows overriding a convex unsaturated colluvial bed.The average entrainment rate at convex sites for continuously graded bed sediment was higher than its discontinuous counterpart.The measured pore pressures within the unsaturated bed sediments were primarily generated by the passing debris flows.Furthermore,it was found that these pressures decreased as the fine particle content increased and the coarse-grain size of the erodible substrates decreased.When the coarse-grain size of the debris flow was smaller than that of the bed sediment,only a portion of the eroded material was entrained by the moving debris flow.In contrast,when the coarse-grain size of the debris flow was equal to or greater than that of the bed sediment,nearly all of the eroded material was entrained.The findings of this study could contribute to the assessment of hazard amplification and inform the design of mitigation and prevention strategies.展开更多
Stony debris flows,characterized by coarse boulders embedded in a sediment-laden matrix,greatly amplify destructive potential by altering flow dynamics and impact forces.Conventional single-phase particle-fluidmixture...Stony debris flows,characterized by coarse boulders embedded in a sediment-laden matrix,greatly amplify destructive potential by altering flow dynamics and impact forces.Conventional single-phase particle-fluidmixture models often struggle to capture the complexities introduced by coarse boulders and multi-phase interactions,while strong-coupling methods can be computationally prohibitive for practical hazard assessments.In this study,we propose a semi-hybrid,fully resolved coupling numerical framework for modeling boulder-laden debris flows.This framework conceptualizes debris flows as a composite system comprising a continuous viscous fluidphase(including finesediments)and a discrete phase of arbitrarily shaped coarse particles.The continuous phase is treated as a generalized nonlinear Coulomb-viscoplastic fluidusing the smoothed particle hydrodynamics(SPH)method,while coarse particles are modeled via the distributed contact discrete element method(DCDEM).These two phases are coupled through an efficienttwo-way resolved scheme,ensuring accurate simulation of flow-boulder interactions within a unifiedtimeframe.We validate the proposed method against two physical experiments:(1)gravity-driven concrete flows and(2)debris flowinteracting with slit-type barriers.Results confirmthe method's robustness in accurately capturing fluid-solid-structureinteractions and deposition processes.Its capabilities are further showcased through the simulation of a stony debris-flowevent inWenchuan County,China,highlighting its promise for real-world engineering applications and validating the effectiveness of the existing cascade dam system in mitigating debrisflowimpact and energy dissipation.展开更多
Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to ev...Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.展开更多
基金supported by the National Key R&D Program of China(Grant No.2018YFC1505205)the Science and Technology Research Program of the Institute of Mountain Hazards and Environment,Chinese Academy of Sciences(Grant No.IMHE-ZDRW-01)Sichuan Science and Technology Program(Grant No.2024NSFSC0781).
文摘A debris flow descending through an erodible convex colluvial bed,originating from a landslide dam and its upstream deposits,can entrain massive amounts of sediment,dramatically increasing the debris flow volume.Most existing erosion models assume that bed sediments are fully saturated,although this condition is rarely observed in nature.Therefore,a thorough understanding of debris flow overtopping erosion on a convex unsaturated bed is crucial for quantifying disaster risk.In this study,we experimentally investigated the effects of sediment composition,specifically coarse-grain size distribution and fine particle content,on the pore pressure evolution and entrainment of debris flows overriding a convex unsaturated colluvial bed.The average entrainment rate at convex sites for continuously graded bed sediment was higher than its discontinuous counterpart.The measured pore pressures within the unsaturated bed sediments were primarily generated by the passing debris flows.Furthermore,it was found that these pressures decreased as the fine particle content increased and the coarse-grain size of the erodible substrates decreased.When the coarse-grain size of the debris flow was smaller than that of the bed sediment,only a portion of the eroded material was entrained by the moving debris flow.In contrast,when the coarse-grain size of the debris flow was equal to or greater than that of the bed sediment,nearly all of the eroded material was entrained.The findings of this study could contribute to the assessment of hazard amplification and inform the design of mitigation and prevention strategies.
基金supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI(Grant Nos.JP23KK0182,JP23K26356,and JP24K00971).
文摘Stony debris flows,characterized by coarse boulders embedded in a sediment-laden matrix,greatly amplify destructive potential by altering flow dynamics and impact forces.Conventional single-phase particle-fluidmixture models often struggle to capture the complexities introduced by coarse boulders and multi-phase interactions,while strong-coupling methods can be computationally prohibitive for practical hazard assessments.In this study,we propose a semi-hybrid,fully resolved coupling numerical framework for modeling boulder-laden debris flows.This framework conceptualizes debris flows as a composite system comprising a continuous viscous fluidphase(including finesediments)and a discrete phase of arbitrarily shaped coarse particles.The continuous phase is treated as a generalized nonlinear Coulomb-viscoplastic fluidusing the smoothed particle hydrodynamics(SPH)method,while coarse particles are modeled via the distributed contact discrete element method(DCDEM).These two phases are coupled through an efficienttwo-way resolved scheme,ensuring accurate simulation of flow-boulder interactions within a unifiedtimeframe.We validate the proposed method against two physical experiments:(1)gravity-driven concrete flows and(2)debris flowinteracting with slit-type barriers.Results confirmthe method's robustness in accurately capturing fluid-solid-structureinteractions and deposition processes.Its capabilities are further showcased through the simulation of a stony debris-flowevent inWenchuan County,China,highlighting its promise for real-world engineering applications and validating the effectiveness of the existing cascade dam system in mitigating debrisflowimpact and energy dissipation.
基金supported by the National Natural Science Foundation of China(42361144880)the Science and Technology Program of Xizang Autonomous Region,China(XZ202402ZD0001)the Qinghai Province Basic Research Program Project,China(2024-ZJ-904).
文摘Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.
