As disasters cripple the world’s prospects for sustainable development, protecting the most vulnerable groups exposed to hazards is one of the main challenges facing humanity. Owing to the systemic nature of risk and...As disasters cripple the world’s prospects for sustainable development, protecting the most vulnerable groups exposed to hazards is one of the main challenges facing humanity. Owing to the systemic nature of risk and the interactions and interdependencies between upland and lowland systems, healthy and productive mountain households and livelihoods are essential to global sustainability. This paper argues that, building on existing international frameworks, and integrated knowledge and praxis, the development of a global policy agenda should be established to build sustainable peace, sustainable security, and development.展开更多
This paper gives an account of the diverse dimensions of research on disaster risk reduction in mountain regions derived from an open call of the Journal of Mountain Science that brought 21 contributions.This special ...This paper gives an account of the diverse dimensions of research on disaster risk reduction in mountain regions derived from an open call of the Journal of Mountain Science that brought 21 contributions.This special issue includes topics as diverse as landslide dynamics and mechanisms,landslide inventories and landslide susceptibility models,insights to landslide hazards and disasters and mitigation measures,disaster response and disaster risk reduction.The overall structure of the paper takes the form of three sections.The first part begins by laying out the significance of disaster risk reduction in mountain areas,whereas the second one looks at the research insights on disaster risk reduction in mountains provided by the contributions comprised in the special volume.The final section identifies areas for further research.展开更多
Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir areas.Landslide forecasting and early warning based on surface displa...Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir areas.Landslide forecasting and early warning based on surface displacements have been widely investigated.However,the lack of direct subsurface real-time observations limits our ability to predict critical hydrometeorological conditions that trigger landslide acceleration.In this paper,we leverage subsurface strain data measured by high-resolution fiber optic sensing nerves that were installed in a giant reservoir landslide in the Three Gorges Reservoir(TGR)region,China,spanning a whole hydrologic year since February 2021.The spatiotemporal strain profile has preliminarily identified the slip zones and potential drivers,indicating that high-intensity short-duration rainstorms controlled the landslide kinematics from an observation perspective.Considering the time lag effect,we reexamined and quantified potential controls of accelerated movements using a data-driven approach,which reveals immediate response of landslide deformation to extreme rainfall with a zero-day shift.To identify critical hydrometeorological rules in accelerated movements,accounting for the dual effect of rainfall and reservoir water level variations,we thus construct a landslide prediction model that relies upon the boosting decision tree(BDT)algorithm using a dataset comprising daily rainfall,rainfall intensity,reservoir water level,water level fluctuations,and slip zone strain time series.The results indicate that landslide acceleration is most likely to occur under the conditions of mid-low water levels(i.e.,<169.700 m)and large-amount and high-intensity rainfalls(i.e.,daily rainfall>57.9 mm and rainfall intensity>24.4 mm/h).Moreover,this prediction model allows us to update hydrometeorological thresholds by incorporating the latest monitoring dataset.Standing on the shoulder of this landslide case,our study informs a practical and reliable pathway for georisk early warning based on subsurface observations,particularly in the context of enhanced extreme weather events.展开更多
文摘As disasters cripple the world’s prospects for sustainable development, protecting the most vulnerable groups exposed to hazards is one of the main challenges facing humanity. Owing to the systemic nature of risk and the interactions and interdependencies between upland and lowland systems, healthy and productive mountain households and livelihoods are essential to global sustainability. This paper argues that, building on existing international frameworks, and integrated knowledge and praxis, the development of a global policy agenda should be established to build sustainable peace, sustainable security, and development.
文摘This paper gives an account of the diverse dimensions of research on disaster risk reduction in mountain regions derived from an open call of the Journal of Mountain Science that brought 21 contributions.This special issue includes topics as diverse as landslide dynamics and mechanisms,landslide inventories and landslide susceptibility models,insights to landslide hazards and disasters and mitigation measures,disaster response and disaster risk reduction.The overall structure of the paper takes the form of three sections.The first part begins by laying out the significance of disaster risk reduction in mountain areas,whereas the second one looks at the research insights on disaster risk reduction in mountains provided by the contributions comprised in the special volume.The final section identifies areas for further research.
基金supported by the National Science Fund for Distinguished Young Scholars(Grant No.42225702)the National Natural Science Foundation of China(Grant No.42077235)+1 种基金the Maria Sklodowska-Curie Action(MSCA)-UPGRADE(mUltiscale IoT equipPed lonG linear infRastructure resilience built and sustAinable DevelopmEnt)project HORIZON-MSCA-2022-SE-01(Grant No.101131146)the China Scholarship Council(CSC)for funding his research period at UNIPD and CNRIRPI。
文摘Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir areas.Landslide forecasting and early warning based on surface displacements have been widely investigated.However,the lack of direct subsurface real-time observations limits our ability to predict critical hydrometeorological conditions that trigger landslide acceleration.In this paper,we leverage subsurface strain data measured by high-resolution fiber optic sensing nerves that were installed in a giant reservoir landslide in the Three Gorges Reservoir(TGR)region,China,spanning a whole hydrologic year since February 2021.The spatiotemporal strain profile has preliminarily identified the slip zones and potential drivers,indicating that high-intensity short-duration rainstorms controlled the landslide kinematics from an observation perspective.Considering the time lag effect,we reexamined and quantified potential controls of accelerated movements using a data-driven approach,which reveals immediate response of landslide deformation to extreme rainfall with a zero-day shift.To identify critical hydrometeorological rules in accelerated movements,accounting for the dual effect of rainfall and reservoir water level variations,we thus construct a landslide prediction model that relies upon the boosting decision tree(BDT)algorithm using a dataset comprising daily rainfall,rainfall intensity,reservoir water level,water level fluctuations,and slip zone strain time series.The results indicate that landslide acceleration is most likely to occur under the conditions of mid-low water levels(i.e.,<169.700 m)and large-amount and high-intensity rainfalls(i.e.,daily rainfall>57.9 mm and rainfall intensity>24.4 mm/h).Moreover,this prediction model allows us to update hydrometeorological thresholds by incorporating the latest monitoring dataset.Standing on the shoulder of this landslide case,our study informs a practical and reliable pathway for georisk early warning based on subsurface observations,particularly in the context of enhanced extreme weather events.
