Towards hydrometeorological thresholds of reservoir-induced landslide from subsurface strain observations  

作　　者：YE Xiao ZHU HongHu WANG Jia ZHENG WanJi ZHANG Wei SCHENATO Luca PASUTO Alessandro CATANI Filippo 

机构地区：[1]School of Earth Sciences and Engineering,Nanjing University,Nanjing,210023,China [2]Department of Geosciences,University of Padova,Padova,35131,Italy [3]School of Geosciences and Info-Physics,Central South University,Changsha,410083,China [4]Department of Information Engineering,University of Padova,Padova,35131,Italy [5]National Research Council-Research Institute for Geo-Hydrological Protection(CNR-IRPI),Padova,35127,Italy

出　　处：《Science China(Technological Sciences)》2024年第6期1907-1922,共16页中国科学（技术科学英文版）

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);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。

摘　　要：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 subsurfac

关 键 词：slow-moving landslide fiber-optic monitoring subsurface strain hydrometeorological threshold extreme weather 

分 类 号：TV697.23[水利工程—水利水电工程] P642.22[天文地球—工程地质学]