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
Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to floodi...Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve—receiver operating characteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC>90%)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the period between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72%between 2000 and 2017,while the PG rate increased by 51.68%over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.展开更多
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This work was supported by the National Natural Science Foundation of China(Grant No.41861134008)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)of China(Grant No.2019QZKK0902)+1 种基金the National Key Research and Development Program of China(Project No.2018YFC1505202)the Key R&D Projects of Sichuan Science and Technology(Grant No.18ZDYF0329).
文摘Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve—receiver operating characteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC>90%)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the period between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72%between 2000 and 2017,while the PG rate increased by 51.68%over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.
