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"LANDSLIDES"
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Prediction of a multi-hazard chain by an integrated numerical simulation approach: the Baige landslide, Jinsha River, China
Successive major landslides during October and November 2018 in Baige village, eastern Tibet, dammed the Jinsha River on two occasions, and the subsequent dam breaches instigated a multi-hazard chain that flooded many towns downstream. Analysis of high-resolution aerial images and field investigations unveiled three potentially unstable rock mass clusters in the source area of the landslides, suggesting possible future failures with potential for river-damming and flooding. In order to evaluate and understand the disaster chain effect linked to the potentially unstable rock mass, we systematically studied the multi-hazard scenarios through an integrated numerical modelling approach. Our model begins with an evaluation of the probability of landslide failure, including runout and river damming, and then addresses the dam breach and resultant flood—hence simulating and visualising an entire disaster chain. The model parameters were calibrated using empirical data from the two Baige landslides. Then, we predict the future cascading hazards via seven scenarios according to all possible combinations of potential rock mass failure. For each scenario, the landslide runouts, dam-breaching, and flooding are numerically simulated with full consideration of uncertainties among the model input parameters. The maximum dam breach flood extent, depth, velocity, and peak arrival time are predicted at sequential sites downstream. As a first attempt to simulate the full spectrum of a landslide-induced multi-hazard chain, our study provides insights and substantiates the value provided by multi-hazard modelling. The integrated approach described here can be applied to similar landslide-induced chains of hazards in other regions.
Journal Article
Successive landsliding and damming of the Jinsha River in eastern Tibet, China: prime investigation, early warning, and emergency response
Two successive landslides within a month started in October 11, 2018, and dammed twice the Jinsha River at the border between Sichuan Province and Tibet in China. Both events had potential to cause catastrophic flooding that would have disrupted lives of millions and induced significant economic losses. Fortunately, prompt action by local authorities supported by the deployment of a real-time landslide early warning system allowed for quick and safe construction of a spillway to drain the dammed lake. It averted the worst scenario without loss of life and property at least one order of magnitude less to what would have been observed without quick intervention. Particularly, the early warning system was able to predict the second large-scale slope failure 24 h in advance, along with minor rock falls during the spillway construction, avoiding false alerts. This paper presents the main characteristics of both slope collapses and damming processes, and introduces the successful landslide early warning system. Furthermore, we found that the slope endured cumulative creeping displacements of > 40 m in the past decade before the first event. Twenty-five meter displacement occurred in the year immediately before. The deformation was measured by the visual interpretation of multitemporal satellite images, which agrees with the interferometry synthetic aperture radar (InSAR) measurement. If these had been done before the emergency, economic losses could have been reduced further. Therefore, our findings strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods (i.e., multitemporal satellite or UAV images) and in situ monitoring system as a way to reduce risk. It is expected that this success story can be replicated worldwide, contributing to make our society more resilient to landslide events.
Journal Article
Birnam Wood
The Booker-winning author of The Luminaries delivers a gripping thriller of high drama and kaleidoscopic insight into what drives us to survive -- Provided by publisher.
Book
Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China
The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, geological maps, and satellite images. Twelve factors were selected as independent variables using correlation coefficient analysis and the neighborhood rough set (NRS) method. In total, 288 soil landslides were mapped using field surveys, historical records, and satellite images. The landslides were randomly divided into two datasets: 70% of all landslides were selected as the original training dataset and 30% were used for validation. Then, SMOTE was employed to generate datasets with sizes ranging from two to thirty times that of the training dataset to establish and compare the four machine learning methods for landslide susceptibility mapping. In addition, we used slope units to subdivide the terrain to determine the landslide susceptibility. Finally, the landslide susceptibility maps were validated using statistical indexes and the area under the curve (AUC). The results indicated that the performances of the four machine learning methods showed different levels of improvement as the sample sizes increased. The RF model exhibited a more substantial improvement (AUC improved by 24.12%) than did the ANN (18.94%), SVM (17.77%), and LR (3.00%) models. Furthermore, the ANN model achieved the highest predictive ability (AUC = 0.98), followed by the RF (AUC = 0.96), SVM (AUC = 0.94), and LR (AUC = 0.79) models. This approach significantly improves the performance of machine learning techniques for landslide susceptibility mapping, thereby providing a better tool for reducing the impacts of landslide disasters.
Journal Article
Seismic landslide risk assessment based on landslide density optimized Newmark model: new insights from the Xianshuihe fault zone in the Eastern Tibetan Plateau, China
The potential hazard of seismic landslides is notably high within active fault zones, currently, the commonly used Newmark model for seismic landslide risk assessment often predicts cumulative displacement that are lower than the actual displacement, In order to enhance the earthquake landslide risk assessment accuracy, a new LS-D-Newmark (Landslide density Newmark) model, which considers the attenuation of geotechnical mechanical parameters in areas with different historical landslide densities, is proposed to evaluate the potential seismic landslide hazard. The Xianshuihe fault zone in the eastern Tibetan Plateau was selected as an example, a historical landslide database was established based on fault activity, field investigation, multi-source remote sensing and InSAR monitoring. The landslide hazards in the Xianshuihe fault zone are distributed linearly along the fault zone and are more concentrated at the intersection of the faults. The results of potential seismic landslide risk assessment based on LS-D-Newmark model show that its prediction accuracy (
AUC
value) increased from 0.78 to 0.84, a 7.69% improvement compared to the traditional Newmark model. Using the spatial characteristics of landslides triggered by the 2022 Luding Ms 6.8 earthquake for verification, and it was found that 75.87% of the landslides were located in the extremely high risk areas and high risk areas predicted by the LS-D-Newmark model, which is consistent with the actual distribution of landslides. The proposed LS-D-Newmark model effectively resolves the issue of underestimating displacement predictions, enhancing the accuracy of potential seismic landslide risk assessments, and provides an important reference for major project planning and construction as well as disaster prevention and mitigation in the region.
Journal Article
Governing affect : neoliberalism and disaster reconstruction
\"Roberto E. Barriospresents an ethnographic study of the aftermaths of four natural disasters: southern Honduras after Hurricane Mitch; New Orleans following Hurricane Katrina; Chiapas, Mexico, after the Grijalva River landslide; and southern Illinois following the Mississippi River flood. Focusing on the role of affect, Barrios examines the ways in which people who live through disasters use emotions as a means of assessing the relevance of governmentally sanctioned recovery plans, judging the effectiveness of such programs, and reflecting on the risk of living in areas that have been deemed prone to disaster. Emotions such as terror, disgust, or sentimental attachment to place all shape the meanings we assign to disasters as well as our political responses to them. The ethnographic cases in Governing Affect highlight how reconstruction programs, government agencies, and recovery experts often view postdisaster contexts as opportune moments to transform disaster-affected communities through principles and practices of modernist and neoliberal development. Governing Affect brings policy and politics into dialogue with human emotion to provide researchers and practitioners with an analytical toolkit for apprehending and addressing issues of difference, voice, and inequity in the aftermath of catastrophes.\"-- Provided by publisher.
Book
Source characteristics and dynamics of the October 2018 Baige landslide revealed by broadband seismograms
The catastrophic Baige landslide occurred at 22:05:36 (Beijing time, UTC+8) on the 10th of October 2018 in Tibet, China. The large and rapid landslide generated strong long-period seismic signals that were recorded by broadband seismic stations. In this study, we inverted the long-period seismic data from five broadband seismic stations at distances of 108 to 255 km from the event to obtain a time series of forces that the landslide exerted on the Earth. Then, we calculated the spectrogram of the vertical component of the seismic signal generated by the event, recorded at the closest broadband seismic station (GZI). This spectrogram, combined with the force-time function and the field investigation, described the dynamic process of the landslide that could be divided into three intervals. For the first interval, assuming that the rockslide can be regarded as a sliding block, we estimated the average slope-parallel acceleration of the sliding materials at 1.08 m/s2, and the average friction coefficient at the base of the rockslide at 0.47. In the second and third intervals, the rockslide had converted to granular debris. The direction and timing of the force correspond to the different points in the sliding path of the granular debris. We used this correlation to estimate the speeds of the granular debris. This study demonstrates that the seismic signal generated by the landslide provides an effective method for estimating the dynamic properties of the landslide.
Journal Article