During the period from 7.0 to 1.7 Ma B. P., the Gyirong Basin formed in the Late Miocene was filled with more than 300m of fluvio-lacustrine sediments. Since 1.7Ma B. P., the Himalaya has been strongly rising and the ...During the period from 7.0 to 1.7 Ma B. P., the Gyirong Basin formed in the Late Miocene was filled with more than 300m of fluvio-lacustrine sediments. Since 1.7Ma B. P., the Himalaya has been strongly rising and the basin dissected by the river on the southern slope of the Himalaya had finished its sedimentary history. Based on the data on sedimentology, magnetostratigraphy, paleontology and oxygen and carbon isotope, the tectonic and dimatic events since the Late Miocene are reconstructed. It is pointed out that the Himalaya uplift commenced before 7.0Ma B.P.. but the strong uplift took place during 2.0 -1.7 and after 0.8 Ma B.P., the most important dimatic events took place between 5.7 and 2.5 Ma BP., the dimatic cydes in the glacial and interglalcial periods began at 1.7 Ma B.P. and was strengthened after 0.8 Ma B.P.展开更多
A reliable economic risk map is critical for effective debris-flow mitigation.However,the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application.To estimate the econ...A reliable economic risk map is critical for effective debris-flow mitigation.However,the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application.To estimate the economic risks caused by future debris flows,a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map.We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year.The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact,temporal probability,and annual susceptibility.We employed a hybrid machine learning model-certainty factor-genetic algorithm-support vector classification-to calculate susceptibilities.Simultaneously,a Poisson model was applied for temporal probabilities,while the determination of annual probability of spatial impact relied on statistical results.Additionally,four major elements at risk were selected for the generation of an economic loss map:roads,vegetation-covered land,residential buildings,and farmland.The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values.Therefore,we proposed a physical vulnerability matrix for residential buildings,factoring in impact pressure on buildings and their horizontal distance and vertical distance to debrisflow channels.In this context,an ensemble model(XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings.The results show that residential buildings occupy 76.7% of the total economic risk,while roadcovered areas contribute approximately 6.85%.Vegetation-covered land and farmland collectively represent 16.45% of the entire risk.These findings can provide a scientific support for the effective mitigation of future debris flows.展开更多
基金Project supported by the National Climbing Project Foundation of China.
文摘During the period from 7.0 to 1.7 Ma B. P., the Gyirong Basin formed in the Late Miocene was filled with more than 300m of fluvio-lacustrine sediments. Since 1.7Ma B. P., the Himalaya has been strongly rising and the basin dissected by the river on the southern slope of the Himalaya had finished its sedimentary history. Based on the data on sedimentology, magnetostratigraphy, paleontology and oxygen and carbon isotope, the tectonic and dimatic events since the Late Miocene are reconstructed. It is pointed out that the Himalaya uplift commenced before 7.0Ma B.P.. but the strong uplift took place during 2.0 -1.7 and after 0.8 Ma B.P., the most important dimatic events took place between 5.7 and 2.5 Ma BP., the dimatic cydes in the glacial and interglalcial periods began at 1.7 Ma B.P. and was strengthened after 0.8 Ma B.P.
基金supported by the Key Laboratory of Mountain Hazards and Earth Surface Processes,Chinese Academy of Sciencesthe European Union’s Horizon 2020 research and innovation program Marie Skłodowska-Curie Actions Research and Innovation Staff Exchange (RISE)under grant agreement (Grant No.778360)+1 种基金the National Natural Science Foundation of China (Grant No.51978533)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No.XDA20030301).
文摘A reliable economic risk map is critical for effective debris-flow mitigation.However,the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application.To estimate the economic risks caused by future debris flows,a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map.We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year.The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact,temporal probability,and annual susceptibility.We employed a hybrid machine learning model-certainty factor-genetic algorithm-support vector classification-to calculate susceptibilities.Simultaneously,a Poisson model was applied for temporal probabilities,while the determination of annual probability of spatial impact relied on statistical results.Additionally,four major elements at risk were selected for the generation of an economic loss map:roads,vegetation-covered land,residential buildings,and farmland.The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values.Therefore,we proposed a physical vulnerability matrix for residential buildings,factoring in impact pressure on buildings and their horizontal distance and vertical distance to debrisflow channels.In this context,an ensemble model(XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings.The results show that residential buildings occupy 76.7% of the total economic risk,while roadcovered areas contribute approximately 6.85%.Vegetation-covered land and farmland collectively represent 16.45% of the entire risk.These findings can provide a scientific support for the effective mitigation of future debris flows.
