Thermo-hydro-poro-mechanical responses of a reservoir-induced landslide tracked by high-resolution fiber optic sensing nerves

Thermo-poro-mechanical responses along sliding zone/surface have been extensively studied.However,it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond the slip surface to progressive failure.Here,we aim to investigate the subsurface multiphysics of reservoir landslides under two extreme hydrologic conditions(i.e.wet and dry),particularly within sliding masses.Based on ultra-weak fiber Bragg grating(UWFBG)technology,we employ specialpurpose fiber optic sensing cables that can be implanted into boreholes as“nerves of the Earth”to collect data on soil temperature,water content,pore water pressure,and strain.The Xinpu landslide in the middle reach of the Three Gorges Reservoir Area in China was selected as a case study to establish a paradigm for in situ thermo-hydro-poro-mechanical monitoring.These UWFBG-based sensing cables were vertically buried in a 31 m-deep borehole at the foot of the landslide,with a resolution of 1 m except for the pressure sensor.We reported field measurements covering the period 2021 and 2022 and produced the spatiotemporal profiles throughout the borehole.Results show that wet years are more likely to motivate landslide motions than dry years.The annual thermally active layer of the landslide has a critical depth of roughly 9 m and might move downward in warmer years.The dynamic groundwater table is located at depths of 9e15 m,where the peaked strain undergoes a periodical response of leap and withdrawal to annual hydrometeorological cycles.These interface behaviors may support the interpretation of the contribution of reservoir regulation to slope stability,allowing us to correlate them to local damage events and potential global destabilization.This paper also offers a natural framework for interpreting thermo-hydro-poro-mechanical signatures from creeping reservoir bank slopes,which may form the basis for a landslide monitoring and early warning system.

Characteristics and triggering mechanism of Xinmo landslide on 24 June 2017 in Sichuan, China

At 5: 39 AM on 24 June 2017, a huge landslide-debris avalanche occurred on Fugui Mountain at Xinmo village, Diexi town, Maoxian county, Sichuan province, China. The debris blocked the Songpinggou River for about 2 km, resulting in a heavy loss of both human lives and properties(10 deaths, 3 injuries, 73 missing, and 103 houses completely destroyed). The objectives of this paper are to understand the overall process and triggering factors of this landslide and to explore the affecting factors for its long term evolution before failure. Post event surveys were carried out the day after the landslide occurrence. Information was gathered from literature and on-site investigation and measurement. Topography, landforms, lithology, geological setting, earthquake history, meteorological and hydrological data of the area were analysed. Aerial photographs and other remote sensing information were used for evaluation and discussion. Eye witnesses also provided a lot of helpful information for us to understand the process of initiation, development and deposition. The depositional characteristics of the moving material as well as the traces of the movement,the structural features of the main scarp and the seismic waves induced by the slide are presented and discussed in detail in this paper. The results show that the mechanism of the landslide is a sudden rupture of the main block caused by the instability of a secondary block at a higher position. After the initiation, the failed rock mass at higher position overloaded the main block at the lower elevation and collapsed in tandem. Fragmentation of the rock mass occurred later, thus forming a debris avalanche with high mobility. This landslide case indicates that such seismic events could influence geological hazards for over 80 years and this study provides reference to the long term susceptibility and risk assessment of secondary geological hazards from earthquake.

An international program on Silk Road Disaster Risk Reduction--a Belt and Road initiative (2016-2020)

Belt and Road Initiative(BRI) is a Chinese national strategy which calls for cooperative economic, political and cultural exchange at the global level along the ancient Silk Road. The overwhelming natural hazards located along the belt and road bring great challenges to the success of BRI. In this framework, a 5-year international program was launched to address issues related to hazards assessment and disaster risk reduction(DRR). The first workshop of this program was held in Beijing with international experts from over 15 countries. Risk conditions on Belt and Road Countries(BRCs) have been shared and science and technology advancements on DRR have been disseminated during the workshop. Under this program, six task forces have been setup to carry out collaborative research works and three prioritized study areas have been established. This workshop announced the launching of this program which involved partners from different countries including Pakistan, Nepal, Russia, Italy, United Kingdom, Sri Lanka and Tajikistan. The program adopted the objectives of Sendai Framework for Disaster Risk Reduction 2015-2030 and United Nation Sustainable Development Goals 2030 and was implemented to assess disaster risk in BRCs and to propose suitable measures for disaster control which can be appropriate both for an individual country and for specific sites. This paper deals with the outcomes of the workshop and points out opportunities for the near future international cooperation on this matter.

Earthquake-triggered landslides affecting a UNESCO Natural Site：the 2017 Jiuzhaigou Earthquake in the World National Park,China

On August 8^(th), 2017, an Ms 7.0 magnitude earthquake occurred in Jiuzhaigou County, northern Sichuan Province, China. The Jiuzhaigou Valley World National Park was the most affected area due to the epicentre being located in the scenic area of the park. Understanding the distribution characteristics of landslides triggered by earthquakes to help protect the natural heritage sites in Jiuzhaigou Valley remains a scientific challenge. In this study, a relatively complete inventory of the coseismic landslides triggered by the earthquake was compiled through the interpretation of high-resolution images combined with a field investigation. The results indicate thatcoseismic landslides not only are concentrated in Rize Gulley, Danzu Gully and Zezhawa Gully in the study area but also occur in the front part of Shuzheng Gully along the road network(from the entrance of Jiuzhaigou Valley to Heye Village). The landslides predominantly occur on the east-and southeastfacing slopes in the study area, which is a result of the integrated action of the valley direction and fault movement direction. The back-slope effect and the slope structure caused the difference in coseismic landslide distribution within the three gullies(Danzu Gully, Rize Gully, and Zezhawa Gully) near the inferred fault. In addition, the topographic position index was used to analyse the impact of microlandforms on earthquake-triggered landslides by considering the effect of the slope angle. The study results reveal a higher concentration of landslides in the slope position class of the middle slope(30°-50°) in Jiuzhaigou Valley. These findings can provide scientific guidance for the protection of natural heritage sites and post-disaster reconstruction in Jiuzhaigou Valley.

滑坡监测经验评述

过去几年里，科学界己越来越重视对自然现象的监测。自然现象监测的目的，是弄清其破坏过程的形成机理、制定适当的预防措施、减缓灾害影响、减少人类生命和财产的损失。为探测不同地质和环境背景下的斜坡稳定条件，意大利自1982年以来开展了一系列岩土工程调查。对众多类型滑坡及其可能的社会经济影响的调查显示，在滑坡研究中需要应用最新技术对滑坡进行连续监测。为此，意大利已在数个滑坡上建立了连接多个传感器的自动监测系统（同时也有助于民防）。在该项研究中，我们不仅发现了一些问题，并且找到一些解决方案。本文结合了3个滑坡的研究，讨论了与监测仪器安装和管理有关的主要问题。

Backward automatic calibration for three-dimensional landslide models

Back-analysis is broadly used for approaching geotechnical problems when monitoring data are available and information about the soils properties is of poor quality.For landslide stability assessment back-analysis calibration is usually carried out by time consuming trial-and-error procedure.This paper presents a new automatic Decision Support System that supports the selection of the soil parameters for three-dimensional models of landslides based on monitoring data.The method considering a pool of possible solutions,generated through permutation of soil parameters,selects the best ten configurations that are more congruent with the measured displacements.This reduces the operator biases while on the other hand allows the operator to control each step of the computation.The final selection of the preferred solution among the ten best-fitting solutions is carried out by an operator.The operator control is necessary as he may include in the final decision process all the qualitative elements that cannot be included in a qualitative analysis but nevertheless characterize a landslide dynamic as a whole epistemological subject,for example on the base of geomorphological evidence.A landslide located in Northeast Italy has been selected as example for showing the system potentiality.The proposed method is straightforward,scalable and robust and could be useful for researchers and practitioners.

Economic Risk Assessment of Future Debris Flows by Machine Learning Method

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.