Catastrophic Landslide Events Research Articles

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.

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.

Analyzing the 27 July 2021 rainfall-induced catastrophic landslide event in the Kutupalong Rohingya Camp in Cox’s Bazar, Bangladesh

This article critically investigates a catastrophic rainfall-induced landslide event that occurred on 27 July 2021 in the Kutupalong Rohingya Camp (KRC) in Cox’s Bazar, Bangladesh, from geological and geomorphological perspectives. Large-scale anthropogenic interventions mainly caused the disastrous landslide event in the KRC in addition to intense rainfall. Before the landslide occurrence, about 300 mm of cumulative rainfall was recorded in the previous seven days and 120 mm of rainfall during the landslide event. A preliminary investigation was conducted to understand the extent, causative factors, and landslide characteristics. The landslide is of mud-flow type, but on the nearby slope, slumping was also visible. The landslide length was about 33 m, width 31 m, and area 612 m2. The approximate volume of slope materials displaced during the landslide event was about 2450 m3. The displaced slope materials mainly were silt and sand. The landslide event caused five fatalities and damaged nearly 5000 shelters in the KRC area. The devastation from such a small landslide event was attributed to dense households on the slope’s hilltop, slope, and toe. The camp areas and host communities are subjected to frequent and fatal landslides in the years to come due to intense human interventions and climatic conditions. The modifications of the slopes have been reducing the cohesion and the shear strength of the slope materials. Therefore, it is recommended to undertake proper mitigation and preparedness measures, including developing and implementing a landslide early warning system to address the emerging humanitarian crisis in the KRC and its surroundings.

A visualized hybrid intelligent model to delineate Swedish fine-grained soil layers using clay sensitivity

In the current paper, a hybrid model was developed to generate 3D delineated soil horizons using clay sensitivity (St) with 1 m depth intervals in a landslide prone area in the southwest of Sweden. A hybridizing process was carried out using generalized feed forward neural network (GFFN) incorporated with genetic algorithm (GA). The model was conducted by means of seven variables consisting of the geographical coordinates and piezocone penetration test data (CPTu). The output of model (St) as a description of the effect of soil disturbance on shear strength plays a significant role in landslides in Sweden and thus can be applied for site-specific evaluation. Therefore, the use of St-based models to delineate soil layers can be a cost-effective solution to improve geo-engineering design practices and assist in the reduction of related environmental risks, such as catastrophic landslide events or excavation failures. Evaluated model performance based on different applied soil classifications showed 4.38% improvement in the predictability level of GFFN-GA compared to optimum GFFN. Accordingly, delineated soil layers were evaluated using different criteria including previous landslides as well as supplementary geophysical and geotechnical investigations. The results show that the adopted hybrid GFFN-GA is an efficient tool that can potentially be applied to delineate soil horizons for the prediction of future events.

Preliminary analysis of a catastrophic landslide event on 6 August 2020 at Pettimudi, Kerala State, India

The Western Ghats, the most prominent orographic feature of peninsular India, occupying about 47% of Kerala state, is vulnerable to various types of landslides. The occurrence of cataclysmic landslides along the windward slope of Western Ghats during torrential rainfall is attributed to the unique physiography and climate. A disastrous landslide occurred in the Pettimudi village of Idukki district, Kerala State at 2245 h (UTC+5.30) on 6 August 2020 due to heavy downpour. A preliminary analysis was carried out to understand the characteristics of the event and to investigate the slope failure mechanisms. The landslide was initiated on the steep slopes of Shola forest and was slid through the tea plantations. The dimensions of the landslide scar were measured as 1200m in length, 30-120m width, 3-7m depth and 125m height. An approximate area of 70125 m2 was estimated to be affected by the landslide. The heavy downpour which is thought to trigger the Pettimudi landslide was measured as 61.6 cm/day, the highest ever recorded in the area.The disaster caused 66 fatalities of residents, among which four are still missing, making it the most catastrophic landslide event reported in the state.

Life and death of slow-moving landslides

In the most destructive and catastrophic landslide events, rocks, soil and fluids can travel at speeds approaching several tens of metres per second. However, many landslides, commonly referred to as slow-moving landslides, creep at rates ranging from millimetres to several metres per year and can persist for years to decades. Although slow-moving landslides rarely claim lives, they can cause major damage to infrastructure and sometimes fail catastrophically, transitioning into fast-moving landslides that can result in thousands of casualties. In addition, slow-moving landslides are highly erosive features that control the landscape morphology in many mountainous regions (such as the California Coast Ranges or the Apennines). The persistent and long-term motion of slow-moving landslides provides an exceptional opportunity to investigate landslide processes and mechanisms. In this Review, we examine the environmental conditions (such as geology, climate and tectonics) of slow-moving-landslide-prone regions, analyse the forcings (for example, precipitation and groundwater, earthquakes, river erosion, anthropogenic forcings and external material supply) that drive their motion and investigate the subsequent implications of the different forcings on landslide dynamics. We then discuss circumstances in which slow-moving landslides can accelerate rapidly, move large distances or even fail catastrophically. Finally, we provide new perspectives and challenges for future landslide research. Some landslides creep at low velocities of a few millimetres to metres per year, forced by external factors such as rainfall, snowmelt, earthquakes or anthropogenic forcings. This Review discusses the recent advances in understanding slow-moving landslides and examines circumstances where slow-moving landslides can accelerate and fail catastrophically.

MULTI-TEMPORAL HIGH-RESOLUTION LANDSLIDE MONITORING BASED ON UAS PHOTOGRAMMETRY AND UAS LIDAR GEOINFORMATION

Abstract. Due to the high seismicity and high annual rainfall, numerous landslides occurred and caused severe impacts in Taiwan. Typhoon Morakot in 2009 brought extreme and long-time rainfall, and caused severe disasters. After 2009, numerous large scale deep-seated landslides may still creeping, however not necessary easily to inspect the activity. In recent years, the remote sensing technology improves rapidly, providing a wide range of image, essential and precious geoinformation. Accordingly, the Small unmanned aircraft system (sUAS) has been widely used in landslide monitoring and geomorphic change detection. This study used UAS to continuously monitor a landslide area in Baolai Village in southern Taiwan, which had a catastrophic landslide event triggered by heavy rainfall caused by Typhoon Morakot in 2009. In order to accesses the potential hazards, this study integrates UAS, field geomatic survey, terrestrial laser scanner (ground LiDAR), and UAS LiDAR for sequential data acquisition since 2015. Based on the methods we are able to construct multi-temporal and high resolution DTMs, so as to access the activity and to monitoring the creeping landslides. The data set are qualified from 21 ground control points (GCPs) and 11 check points (CPs) based on real-time kinematic-global positioning system (RTK-GPS) and VBS RTK-GPS (e-GNSS). More than 10 UAS flight missions for the study areas dated since 2015, for an area large than 5–40 Km2 with 8–12 cm spatial resolution (GSD). Then, the datasets was compared with the airborne LiDAR data, to evaluate the quality and the interpretability of the dataset. Since early 2018, we integrate UAS LiDAR technology to scanning the sliding area. The density of the point cloud data sets are higher than 250 and 100 points/m2 for the total and ground point, respectively. The spatial distributions of geomorphologic changes were quantified firstly with the GCPS and CPs. The potential disaster was evaluated at different times, and the result reveals that most active regions were on the eastern side of the landslide. Significant changes in elevation were detected before the middle of 2017, however reactivated again since middle of 2018. The results of this study provide not only geoinfomatic datasets of hazardous area, but also for essential geomorphologic information/methods for other study, and for hazard mitigation and planning, as well.

Benefit-Cost Analysis of Acquisition of Properties Subject to One-time-loss (Landslides)

For a special class of hazards such as severe landslides, the expected level of damage is usually limited to two possibilities; zero damage or total loss (“one-time-loss”). A new approach is developed for Benefit cost Analysis of acquisition of properties subjec0 to non-recurring total loss. The equivalent annual probability of landslide is estimated for a given probability of a catastrophic landslide event within the next few years from geotechnical studies. The theoretical approach and assumptions are laid out and the proposed solution is explained through an example problem. Simplified solutions in tabular, graphical and equation formats are included.

Spatial and temporal analysis of a global landslide catalog

Landslide inventories are critical to support investigations of where and when landslides have happened and may occur in the future; however, there is surprisingly little information on the historical occurrence of landslides at the global scale. This paper presents a new publicly available global landslide catalog (GLC), which is based on media reports, online databases, and other sources. This database is currently available at http://ojo-streamer.herokuapp.com/. The 5741 points in the GLC provide a foundation for evaluating spatial and temporal trends in landslide activity from 2007 to 2013. Globally, landslides were reported most frequently from July to September. Most events occurred in Asia, North America and Southeast Asia. In contrast, fewer than 5% of the fatalities were reported in North America, suggesting a significant amount of under-reporting in other regions as well as potential discrepancies between developing and developed regions. Reported landslide events were also compared to satellite-based precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) to evaluate the co-occurrence of extreme precipitation and landslide activity. Of the 3550 points considered in a subset of the GLC, approximately 60% of the reported landslides have daily precipitation exceeding the 95th percentile of precipitation calculated over a 14-year TRMM record for the same location. This study also investigated how the recurrence interval of extreme precipitation corresponded to some of the most catastrophic landslide events. In spite of several reporting and cataloging biases, spatial and temporal analysis of the GLC suggests that it is a valuable database for characterizing global patterns of landslide occurrence and evaluating relationships with extreme precipitation at regional and global scales.

Terrestrial Laser Scanning 3D as a useful method for monitoring of urban areas endangered by landslides

Landslides are a serious problem in Poland in recent years. Starting from catastrophic landslide events in 1990s, systematically is increased risk of landslides hazard on urbanized areas. However, many of Polish landslides are slow-moving, so potential terrain instability is often difficult to observe or it is easy to neglect (Mrozek et al.). In the analysis of landslides (predicting, monitoring and alerting for early warning, monitoring for change detection, etc.) a variety of geomatic techniques can be used. One of the most innovative is Terrestrial Laser scanner 3D. Terrestrial Laser Scanning, usually adopted in industry, could be successfully applied for deformation monitoring of the landslide. This technique give a great contribution to the knowledge of both the surface shape and the kinematics of landslides by providing data which are used from geologists, geomorphologists and geotechnics for interpreting the phenomenon. Such a methodology offers the advantages typical of noncontact techniques, and moreover permits to collect in short time dense 3D point clouds over the surface of interest, to record a perspective image (intensity data and sometimes RGB data), it doesn’t requires necessarily deployment of reflectors, and it permits immediately and easily to take measurements between points (Bitelli et al. 2004). The acquired points clouds data have to be connected together in order to reconstruct the continuous surface of the landslide, so they are automatically aligned during the survey and merged by the software, which permits to create polygonal surfaces and to map the surface with textures using digital photos obtained during the scanning by a digital semi-metric camera placed over the laser instrument (Barbarella and Fiani 2012). The Terrestrial Laser Scanner allow us to survey in a very fast, efficient and precise way. It could be very useful because of their ability to describe in details the geometry of objects. In the analysis of the temporal evolution of landslides and of related hydrogeological hazards, terrestrial laser scanning (TLS) seems to be a very suitable technique for morphological description and displacement analysis. Monitoring in general can be regarded as the regular observation and recording of activities taking place in a certain structure. It is a process of gathering information on all aspects of the object (Pilecka and Bazarnik 2015). Deformation monitoring and gathering measured values is a major component for further calculation of soil and rock stability, deformation analysis, prediction and alert.

Paleomagnetic constraints on fault motion in the Hilina Fault System, south flank of Kilauea Volcano, Hawaii

Movement of the south flank of Kilauea Volcano in Hawaii has been associated with catastrophic landslide events. The surface expression of this former movement is the Hilina Fault System with fault scarps as high as 500 m. Paleomagnetic directions for lava flows exposed in the Hilina Fault scarps at Puu Kapukapu and Keana Bihopa on the Hilina Pali are used to determine the average rate of movement along faults (slip surfaces) separating the two sections. This paper reports results from two independent paleomagnetic studies within the Hilina Pali area. Twenty-one paleomagnetic sites (143 cores) were sampled by the Michigan Technological University group from lava flows between the Mo’o Ash and Middle Pohakaa Ash at Keana Bihopa in the footwall block of the 500-m-high Hilina Pali fault scarp. Thirty paleomagnetic sites (152 cores) were collected by the California Institute of Technology group from lava flows between the Mo’o Ash and Middle Pohakaa Ash in the 300-m-high Puu Kapukapu section (the hanging-wall block). A comparison of site-mean directions show that lava flows in the lower part of the Puu Kapukapu section have been tilted more than lava flows in the upper part with respect to the Keana Bihopa section. The systematic steepening of remanent directions downsection at Puu Kapukapu indicates that slippage of this block occurs along listric normal faults. The average amount of backward tilt of the Puu Kapukapu block, based on a comparison of mean directions from the two sections, is 7.8°±7.7°. Using slope stability methods, the average rate of movement of the Puu Kapukapu block since deposition of the Middle Pohakaa Ash is 1.7–2.4 cm/yr, and the average displacement (subsidence) is 680–740 m. Assuming that the average displacement resulted from a series of earthquakes producing subsidence equal to that observed in the 1975 Kalapana earthquake (3 m of subsidence along a 40-km segment of coastline on the south flank of Kilauea Volcano), one Kalapana-size earthquake occurring every 200 years would account for this displacement. Lastly, overall mean directions for the two sections indicate that Puu Kapukapu has rotated counterclockwise with respect to the Hilina Pali by 14.8°±8.5° about a nearby vertical axis. This also suggests that slippage between the two blocks occurs along listric normal faults.