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

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

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
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.

slow-moving landslidefiber-optic monitoringsubsurface strainhydrometeorological thresholdextreme weather

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

展开 >

School of Earth Sciences and Engineering,Nanjing University,Nanjing 210023,China

Department of Geosciences,University of Padova,Padova 35131,Italy

School of Geosciences and Info-Physics,Central South University,Changsha 410083,China

Department of Information Engineering,University of Padova,Padova 35131,Italy

National Research Council-Research Institute for Geo-Hydrological Protection(CNR-IRPI),Padova 35127,Italy

展开 >

National Science Fund for Distinguished Young ScholarsNational Natural Science Foundation of ChinaMaria Sk?odowska-Curie Action(MSCA)-UPGRADE(mUltiscale IoT equipPed lonG linear infRastructure resilience built and sustAinaChina Scholarship Council(CSC)at UNIPD and CNR-IRPI

4222570242077235HORIZON-MSCA-2022-SE-01101131146

2024

10.1007/s11431-023-2657-3

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子：1.056
ISSN：1674-7321
年,卷(期)：2024.67(6)
  • 1