Catastrophic Landslides Research Articles

Catastrophic landslide in Wayanad district of Kerala, India on July 30, 2024: A complex interplay between geology, geomorphology, and climate

Catastrophic landslide in Wayanad district of Kerala, India on July 30, 2024: A complex interplay between geology, geomorphology, and climate

The triggering factors on the 3rd December 2023 catastrophic landslide in the Hanang area, northern Tanzania

The triggering factors on the 3rd December 2023 catastrophic landslide in the Hanang area, northern Tanzania

Displacement residuals reveal landslide regime shifts

AbstractDespite significant progress in the development of advanced technologies for detecting and monitoring unstable slopes, accurately predicting catastrophic landslides remains a challenge. To tackle this challenge, our research integrates advanced prediction models and granular systems theory to provide insights into regime shifts within slow-moving deep-seated landslide dynamics. Our approach is designed to discern exceptional departures from historical landslide dynamics. The approach leverages the “group dynamics,” crucial for identifying precursory failure indicators, according to the generic dynamics of the precursory failure regime in granular systems. We select three different monitored slow-moving landslides as test cases. We employ an error correction cointegration vector autoregression model together with an exogenous regressor to encode historical spatiotemporal landslide dynamics and predict displacement at multiple locations by considering the historical landslide motion and relationship with external triggers. Displacement residuals are obtained by computing the difference between predicted and measured displacement for a given historical calibration time window. Threshold values for the displacement residuals are determined by analyzing the historical distribution of these residuals. Lastly, persistence in time of the threshold exceedance and the number of monitoring points that exceed the threshold at the same time are considered to encode the group dynamics. This approach offers several advantages, including the effective identification of critical regime shifts, adaptability, and transferability, and it introduces regime shift information into local landslide early warning systems. This approach can enhance confidence in the resultant alert, particularly when integrated with conventional alert systems, thereby improving the reliability of landslide warning systems.

Automatic recognition of active landslides by surface deformation and deep learning

Catastrophic landslides are generally evolved from potential active landslides, and early identification of active landslides over an extensive region is vital to effective prevention and control of disastrous landslides in urban areas. Interferometric Synthetic Aperture Radar (InSAR) has immense potential in mapping active landslides. However, artificial interpretation of InSAR measurements and manual recognition of active landslides are very laborious and time-consuming, with a relatively high missing and false alarms. That hinders the application of InSAR technique and the identification of active landslides in wide areas. Automatic recognition of active landslides has always been a great challenge and has been relatively rarely investigated by previous studies. This work establishes comprehensive identification indices of geoenvironmental, disaster-triggering, and surface deformation features. Moreover, it suggests a novel deep learning algorithm of SDeepFM to conduct automatic identification of active landslides across a vast and landslide-serious area of Hualong County. Some new viewpoints are suggested as follows. (1) The identification indices consist of disaster-controlling, disaster-inducing, and active deformation characteristics and are constructed in terms of the cause characteristics of active landslides. Thus, it can effectively decrease the false alarms of active landslide identification. (2) The proposed SDeepFM algorithm features a spatial-perception ability and can adequately extract and fuse the low-level and high-level semantic features. It outperforms the classification and regression tree (CART), multi-layer perceptron (MLP), convolutional neural network (CNN), and deep neural network (DNN) algorithms. The test accuracy attains 0.91, 99.73%, 90.21%, 0.92, 0.96, and 0.91 in F1-score, Accuracy, Precision, Recall, AUC, and Kappa, respectively. (3) A total of 164 active landslides are exactly recognized, and 39 active landslides are newly identified in this work. (4) In Hualong County, the characteristics of slope deformation, spatial context, lithology, tectonic movement, human activity, and topography play important roles in active landslide identification. River distribution and rainfall also contribute to active landslide recognition.

Utilizing crowdsourced data for timely investigation of catastrophic landslide accidents: a case study of the coal mine collapse in inner Mongolia, China

Utilizing crowdsourced data for timely investigation of catastrophic landslide accidents: a case study of the coal mine collapse in inner Mongolia, China

Forecasting Inundation of Catastrophic Landslides From Precursory Creep

AbstractForecasting landslide inundation upon catastrophic failure is crucial for reducing casualties, yet it remains a long‐standing challenge owing to the complex nature of landslides. Recent global studies indicate that catastrophic hillslope failures are commonly preceded by a period of precursory creep, motivating a novel scheme to foresee their hazard. Here, we showcase an approach to hindcast landslide inundation by linking satellite‐captured precursory displacements to modeling of consequent granular‐fluid flows. We present its application to the 2021 Chunchi, Ecuador landslide, which failed catastrophically and evolved into a mobile debris flow after four months of precursory creep, destroying 68 homes along its lengthy flow path. Underpinned by uncertainty quantification and in situ validations, we highlight the feasibility and potential of forecasting landslide inundation damage using observable precursors. This forecast approach is broadly applicable for flow hazards initiated from geomaterial failures.

Landslide Deformation Prediction and Automatic Warning by Coupling Machine Learning and Physical Models

AbstractAutomatic deformation forecast and warning of a catastrophic landslide can effectively avoid significant casualties and economic losses. However, currently it has not come to a comprehensive forecast framework covering all the deformation stages of a landslide. Moreover, landslide deformation prediction possesses high error and false alarm rates. This work suggests a novel integrated framework of landslide deformation forecast and warning by coupling machine learning and physical models. The framework can relatively accurately predict all the deformation stages from creeping deformation to critical sliding and features 4 advantages. (a) The forecast indices are established by combining deformation and disaster‐triggering characteristics to improve the prediction accuracy. (b) It leverages the advantage of C5.0 decision tree algorithm in knowledge interpretability to automatically extract deformation forecast criteria. (c) It capitalizes on the precision superiority of a graph convolutional network in time‐series data learning to predict the four deformation stages from creeping deformation to rapidly accelerated deformation. (d) It utilizes the physical and mechanical bases of Morgenstern‐Price method to forecast the critical sliding stage. Zhujiatang Landslide is a large‐scale deep‐seated soil landslide with significant deformation. It is selected as a case study because it has endangered 1,131 persons and may cause a direct financial loss of 100 million RMB. The validating and predicting Accuracy values attain 97.39% and 95.72%, respectively, and the Kappa values reach 0.91 and 0.93, respectively. The landslide will run out when it suffers from a rainstorm with cumulative rainfall of 79.57 mm and an earthquake with a horizontal coefficient of 0.04.

Geodetic Evidence for Cascading Landslide Motion Triggered by Extreme Rain Events at Joshimath, NW Himalaya

AbstractSlope instability due to tectonic, hydrological and anthropogenic activities cause severe landslides in Himalaya. Joshimath, a densely populated Himalayan town witnessed a catastrophic landslide event during December 2022 and January 2023 causing damages to ∼700 buildings. We use Interferometric synthetic aperture radar, Global Positioning System and rainfall measurements to probe the kinematics of the Joshimath landslide. We separate the seasonal and episodic deformation components using singular spectrum analysis. While the low amplitude annual landslide motions are modulated by seasonal precipitation, acceleration phases are triggered by extreme rain events. Our analysis revealed episodes of cascading motions triggered by extreme rain events resulting an overall increase in landslide velocity from −22 mm/yr during 2004–2010 to −325 mm/yr during 2022–2023. We estimate the landslide depth (∼30 m) and hydraulic diffusivity (∼3 × 10−5 m2/s) using a 1‐D pore‐water pressure diffusion model. Our study reveals the importance of systematic monitoring of ground deformation and weather parameters for landslide hazard mitigation.

Energy evolution of gravitational-granular flows

The catastrophe of natural disasters such as landslides, is a prevalent phenomenon in natural terrain conditions in high mountainous areas; however, the mobility of such landslides has not yet well understood due to the discrete nature of material and the coming to play of water. In this paper, we numerically study the mobility of an unsaturated gravitational-granular flow, occurring on a slope-break system that contains two regions: inclined-upstream and horizontal-downstream areas, by using three-dimensional discrete element simulations. A sliding volume composed of spherical grains collapses on the first region, then plunges and deposits on the second one. The upstream-plunging length and the cohesive stress exerted on grains affect differently on the energy evolution not only in the whole process but also in different regions and directions depending on the inclination angle. These findings provide a deep understanding of the mechanism and mobility of landslides, leading to good predictions about the potential impacts of the catastrophic landslides on buildings and human lives.

Dams overtopping scenarios from catastrophic landslides in mountains’ headwaters Case study in Kobe City, Japan

As a Mw 8.0 Nankai Trough Earthquake is predicted with an 80% probability of occurrence within the next 30 years, the efficiency of check dams in the mountains above Kobe City is a crucial question when considering co-seismic landslide disaster risk management. In the present contribution the author aimed to define which subsection of the Sumiyoshigawa watershed may be more prone to generate impacts in downstream Kobe City from catastrophic landslides in the headwaters. For this purpose, the present state of the check dams network was analysed from the 2018 LiDAR (Light Detection and Ranging) data, considering the progressive infilling of the structures. As a result, the capacity of the dams in 2018 dropped by about 1/10 of the original designed capacity, arguably because of the heavy rainfall events in 2014 and 2018. Despite this evolution, however, landslides > 20,000 m3 starting from only one tributary can fill all the dams and flow downstream. From this data, it is possible to prioritize dam curation programs, especially because population ageing and shrinking is reducing the manpower and the funds available to maintain the check dams.

Collective urban green revitalisation: Crime control an sustainable behaviours in lower-income neighbourhoods

The growing need to increase green spaces in highly urbanised cities has become more prominent recently. Special attention is paid to involving communities in the design process to enhance societal changes for a meaningful appreciation of the natural environment. Urban green collective initiatives in lower-income neighbourhoods have the potential to promote meaningful relatedness to the natural environment, agency, and stewardship. These initiatives contribute to new spatial collective norms that help decrease crime and violence. This study analyses a unique communal green space in Medellin, built and maintained by residents of the Villatina neighbourhood after a catastrophic landslide. After the disaster, residents exhibited self-organisation and collective efficacy to protect the space from being used by criminal actors. In 2010, the garden underwent renovations to be part of the ecosystem services network of the Circumvent Garden project. Although participatory strategies were implemented, the new social dynamics hindered collective actions in the space. This study contends that urban green collective initiatives in lower-income neighbourhoods introduce a unique form of communal action in urban green spaces, resulting in long-term societal changes. By integrating the collective placemaking strategies and key spatial elements in participatory design protocols, balanced social-environmental dynamics may be introduced to foster sustainable consciousness and minimise criminal behaviour.

The analysis of seismic induced progressive instability and failure mechanisms: A case study

The 2008 Wenchuan Ms8.0 earthquake triggered a catastrophic landslide, i.e., the Daguangbao (DGB) landslide (1.2 ✕ 109 m3). Its shear failure occurred within a deep-seated (average depth of 400 m) saturated carbonate bedding fault in the Paleozoic Formation. The frequent seismic events in the tectonic belt make the landslide initiation mechanism more complex. Shake table tests were conducted on the DGB landslide to understand the coupling effect of multiple earthquakes, geology and hydrology. The results showed that the multiple earthquakes weakened the friction coefficient as the Boltzmann function on the sliding surface of the landslide. The friction law of the basal surface related to seismic energy was established and incorporated into the Newmark analysis. And the seismic energy consumed in the steep scarp surface release of landslide during an earthquake is also considered. It found that the traditional Newmark model overestimates the time for earthquake-induced landslides due to ignoring the steep scarp surface release stage. It proposed that evaluating pre-earthquake induced slope damage, and critical seismic energy for triggering co-seismic landslides are important for predicting post-seismic landslides.

Investigation of the mass movement and thermal pressurization effect of rapid and long-runout landslides in Shuicheng, Guizhou, China

The speed, distance and energy evolution of rapid and long-runout landslides are crucial indicators for evaluating the characteristics of these catastrophic landslides. The Shuicheng rapid and long-runout landslide was chosen as an example for further study, and the evolutionary process of landslide motion was simulated by PFC. The kinematic characteristics and dynamic mechanism were experimentally studied with the above three indicators, and the results indicated that the landslide lasted approximately 60 s and could be divided into four stages: uniform deformation, erosion-disintegration, block-spilt and convergence-accumulation. The speed of the landslide peaked at 36.1 m/s at 28 s. Controlled by the terrain, particles collided violently, and there was strong momentum transfer, which greatly promoted the long-runout movement of the landslide, with a maximum movement distance of 1303 m. During movement, gravitational potential energy was mainly converted into dissipated energy of friction and collision, accounting for 51.0 % and 39.6 %, respectively, of the total gravitational potential energy released during the landslide. After the landslide initiated, the apparent frictional coefficient reached a peak of 0.49 in 6 s. Then, the apparent frictional coefficient gradually decreased and stabilized at approximately 0.32. The factor leading to the decrease in the apparent frictional coefficient was the rapid increase in the dissipated energy of friction. In addition, it was found through quantitative calculation that the dissipated energy of friction generated in the propagation can cause the basal facies to rise by 65.08 °C. The results from the numerical simulations are in agreement with the findings from the field investigation, affirming the thermal pressurization impact associated with rapid and long-runout landslides. These simulations yield insights that are scientific merit for understanding the kinematics and dynamics underlying these types of landslides.

Frictional Control on Accelerating Creep During the Slow‐To‐Fast Transition of Rainfall‐Induced Catastrophic Landslides

AbstractSlow moving landslides regulated by precipitation/snowmelt induced subsurface pore‐pressure transients can sometimes accelerate to catastrophic failure causing loss of infrastructure and lives. Yet, unified theories of the transition of slow landslides into ultimately catastrophic ones in response to pore‐pressure changes remain relatively unexplored. Here, we use a simple gravity‐driven block‐slider model governed by laboratory‐derived rate‐and‐state friction (RSF) equations with velocity‐weakening parameters to analyze the mechanical progression of initially creeping landslides toward runaway acceleration. The rigid‐block approximation allows for exact or semi‐analytical estimates of the timescales over which such, potentially unstable, creeping landslides can be expected to transition to runaway acceleration in response to idealized pore‐pressure perturbation histories. We demonstrate that the duration of creep preceding catastrophic failure is critically sensitive to the RSF parameters, pore‐pressure variation amplitude and frequency, and background shear‐load and pore‐pressure levels through a set of non‐dimensional numbers. Our model predicts that slow landslides within velocity‐weakening clay‐rich soils can potentially creep for years to decades before transitioning to runaway failure when regulated by typical seasonal pore‐pressure transients. Remarkably, for much larger and rapid pore‐pressure changes, the same landslides can evolve to runaway failure over days to few tens of minutes. Being dependent purely on soil parameters that can be inferred from routine laboratory experiments, our model provides a theoretical framework that might be practically useful to understand the non‐linear and hysteretic response of landslide motion to pore‐pressure transients.

Irshalwadi landslide in Western Ghats of India: Observations from precursory slope movement, rainfall and soil moisture

The Western Ghats of India, a UNESCO world heritage site, is gradually becoming a hotspot for catastrophic landslides. On July 19, 2023, the entire Irshalwadi village in the Raigad district of Maharashtra, India, was obliterated due to a single catastrophic landslide. Of the 228 residents of the village, the landslide killed 27 people and 57 are still reported to be missing. The landslide occurred on a slope which exhibited no visual precedence of disturbance or creep. However, analysis of potential precursory movements prior to slope failure using the Persistent Scatterer Interferometry (PSI) technique reveals active movement near the base of the slope (in order of ∼12 ​mm/y). Sentinel-2 satellite imagery acquired post-event characterises the landslide as a bifurcated debris flow possibly triggered by heavy rainfall in the region. Peak cumulative rainfall, estimated by the Indian Meteorological Department (IMD), was observed in this region on 17, 18 and July 19, 2023 (∼500) mm and was the highest recorded rainfall in the region during the given period. This caused significant water percolation into the porous basaltic soil, leading to increased soil moisture, as supported by the Soil Moisture Active Passive (SMAP) data. The resultant increase in pore pressure caused the slope material to fail and eventually trigger the landslide.

List of 85 typical catastrophic landslides from March 2004 to February 2024

List of 85 typical catastrophic landslides from March 2004 to February 2024

Подверженность населенных пунктов Республики Дагестан обвально-осыпным процессам

The object of the study is the landslide activity in the Republic of Dagestan. The paper considers the results of systematization and analysis of various sources containing information on the activity and danger of landslide processes in the settlements of the republic for 2004–2022. The conditions, causes, activity and danger of landslide manifestations on their territory are characterized. The role of natural and man-made factors in the development of this process and its obvious timing to the winter-spring-summer period of the year (the time of snowmelt and abnormal precipitation) is determined. 46 settlements were identified (in 21 administrative districts of the republic), where significant landslides occurred over the period under consideration. Most of these rural settlements are located in the low and middle mountainous parts of the republic. The largest number of affected settlements (from the studied processes) are located in the Tsumadinsky and Akhvakhsky administrative districts of the republic. It is noted that in the period from 2004 to 2022, the greatest landslide activity was observed in 2014, and a very weak degree was observed in 2010, 2013 and 2021. In 2006, 2017 and 2019, landslides in the settlements of the republic were not recorded. The most catastrophic landslide processes over the studied period of time were observed in the villages of Hebda and Golotl in the Shamil district. The article provides information on the scale of the collapse deformations and destruction of residential infrastructure on the territory of settlements. The analysis of the socio-economic consequences of the landslide allows us to speak about a fairly significant degree of their danger to the life of the population in the mountainous part of the republic.

Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique

In recent years, several catastrophic landslide events have been observed throughout the globe, threatening to lives and infrastructures. To minimize the impact of landslides, the need of landslide susceptibility map is important. The study aims to extract high-quality non-landslide samples and improve the accuracy of landslide susceptibility modelling (LSM) outcomes by applying a coupled method of ensemble learning and Machine Learning (ML). The Zigui-Badong section of the Three Gorges Reservoir area (TGRA) in China was considered in the present study. Twelve influencing factors were selected as inputs for LSM, and the relationship between each causal factor and landslide spatial development was quantitatively analyzed. A total of 179 landslides have been used in the present study. About 70% of the landslide pixels were randomly considered for training, and the remaining 30% were used for validation. Logistic Regression (LR) model was applied to produce an initial susceptibility map, and the non-landslide samples were selected within the classified low-susceptibility zone. Subsequently, two ML classifiers – the Classification and Regression Tree (CART), and the Multi-Layer Perceptron (MLP), and four coupling models – the CART-Bagging, CART-Boosting, MLP-Bagging, and MLP-Boosting, were utilized for LSM. Finally, the receiver operating characteristics (ROC) curve and statistical analysis were applied for accuracy assessment. The results show that altitude and distance to rivers were the main causal factors of landslides in the study area. The LR-MLP-Boosting performed the best with an accuracy of 0.986 followed by the LR-CART-Bagging, LR-CART-Boosting, and LR-MLP-Bagging. Accuracy comparisons demonstrate that ensemble learning algorithm can notably enhance the LSM performance of ML classifiers, and the Boosting algorithm marginally outperforms the Bagging algorithm. Moreover, the LR model can effectively constrain the selection range of non-landslide samples. The non-landslide sampling method constrained by LR yields higher quality samples compared to raditional random sampling method with no constraints, which develops a more excellent LSM.

Rapid cholera outbreak control following catastrophic landslides and floods: A case study of Bududa district, Uganda.

In June 2019, landslides and floods in Bududa district, eastern Uganda, claimed lives and led to a cholera outbreak. The affected communities had inadequate access to clean water and sanitation. To share the experience of controlling a cholera outbreak in Bududa district, after landslides and floods. A descriptive cross-sectional study was carried out in which outbreak investigation reports, weekly epidemiological data and disaster response reports were reviewed. On 4 - 5th June 2019, heavy rainfall resulted in four landslides which caused six fatalities, 27 injuries, floods and displaced 480 persons. Two weeks later, a cholera outbreak was confirmed in Bududa district. The Ministry of Health (MoH) rapidly deployed oral cholera vaccine (OCV) from local reserves and mass vaccinated 93% of the target population in 22 affected parishes. The outbreak was controlled in 10 weeks with 67 cholera cases and 1 death reported. However, WaSH conditions remained poor, with only, 24.2 % (879/3,628) of the households with washable latrines, 26.8% (1,023/3,818) had hand-washing facilities with soap and 33.6% (1617/4807) used unsafe water. The OCV stockpile by the MoH helped Uganda to control cholera promptly in Bududa district. High-risk countries should keep OCV reserves for emergencies.

The Detection and Control Factor Analysis of Active Landslides in Guizhou Province, China, Using Sentinel-1 SAR Imagery

Catastrophic landslides occur frequently in Guizhou Province, China, and the landslides in this area have special geomorphological, geological, and anthropogenic features. In order to detect and explore the distribution pattern and control factors of active landslides in Guizhou, firstly, a total of 693 active landslides throughout Guizhou Province were mapped based on the deformation rate, which was obtained by spatiotemporal filtering and Intermittent Small Baseline Subset (ISBAS) Interferometric Synthetic Aperture Radar (InSAR) techniques. Then, the relationships between the detected landslides and elevation, aspect, slope gradient, and stratigraphic lithology were analysed. Moreover, it was found that the landslides were mainly concentrated in three stratigraphic combinations, that is T1f~P2l−d, T1f~T1yn, and T2g~T1yn. Thereafter, the correlation coefficients between the landslide density and elevation and distance to the stratigraphic boundary were 0.54 and −0.19, indicating that the distribution of landslides was significantly controlled by the elevation and the boundary of specific stratigraphic combinations. Finally, we chose a typical landslide to explore how landslide development was controlled by the combined effects of elevation and stratigraphy by using ascending and descending InSAR results. We revealed that landslides occurred primarily in areas with a steep slope and a stratigraphy characterized by mudstone and sandstone.