Landslide Development Research Articles

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.

Экзогенные процессы формирования современной геоэкологичеcкой ситуации в Белгородской области

According to the “Fundamentals of state policy in the field of environmental development of the Russian Federation for the period up to 2030”, it is the anthropogenic factor that has a greater impact on the environmental situation in our country. In such conditions, the risk of dangerous geological processes increases significantly. The authors found that exogenous geological processes in combination with anthropogenic factors make a significant negative contribution to the ecological situation in the region. The object of the study is exogenous geological processes occurring on the territory of the Belgorod region. The subject of the study is exogenous geological processes, anthropogenic processes, and their contribution to the current state of the ecological situation in the Belgorod region. The purpose and objectives of the study are as follows: to study the characteristics and patterns of development of exogenous geological processes in the Belgorod region; methods and technologies for the rapid detection and prediction of the occurrence of exogenous geological processes, as well as preventive measures to reduce them in the region are developed. The authors have come to the following conclusions: On the territory of the region, the development of erosive, landslide, karst, Aeolian and suffusion processes has a significant impact on the environmental situation, the manifestation of which is primarily due to the geological structure of its parts, physical and geographical conditions, the deve­lopment and condition of the mining industry and agriculture. The dominant exogenous geological process that makes the greatest contribution to the formation of the ecological situation of the Belgorod region is linear erosion. The degree of damage to the territory of the region by erosion processes is quite high. Intensive erosion dismemberment is one of the main indicators of the unfavorable ecological and geomorphological condition of the territory. The influence of the anthropogenic factor on exogenous processes is expressed, first of all, in environmental pollution, landscape transformation and changes in the hydrological regime. The possibility of neutralizing exogenous geological processes in order to ensure the safety of the population of the Belgorod region should be justified by knowledge of the engineering and geomorphological conditions of the territory. To do this, it is necessary to create a strict modern system for monitoring exogenous geological processes, which will reduce the risks associated with them, more accurately predict their occurrence and obtain more reliable data. The solution of this problem is possible within the framework of the regional monitoring center for exogenous geological processes.

Geospatial conditions of landslide formation on the territory of Sakhalin Island

The paper considers the conditions for the formation of landslide-prone areas of Sakhalin Island, the intensity of development and the main characteristics of landslides of various types common on the territory of Sakhalin Island. Sakhalin Island, determined by the geospatial location of the territory. It is due to the geospatial location of the territory as a result of the tectonic and geological history of its development that the present relief, in addition to extensive sedimentation basins, is represented by steeply sloping erosion-denudation and abrasion forms of macro and mesorelief with high potential energy, overlain by layered significantly dislocated and structurally disturbed terrigenous strata represented by relatively weak rocks, the accumulation of which occurred as a result of cyclic sedimentation. The tectonic and accompanying seismic activity, as well as the geologic history of the terrane formation, have led to the formation of complex engineering, geological, and hydrogeological conditions of the territory. Furthermore, the marginal position of the region determines its monsoon climate with high cyclonic activity, leading to a general high moisture content of the territory and activation of exogenous processes. Thus, due to the geospatial position of Sakhalin in the transition zone, a whole complex of natural factors of landslide formation was formed in the territory, including global factors (tectonic, climatic) and their subordinate determinants, as well as secondary (trigger) factors (geomorphologic, geological, hydrogeological, seismic), contributing to the widespread development of modern active landslides of different scales, characteristics, as well as types of landslide process and mechanism of mountain displacement.

Mapping of landslide susceptibility using analytical hierarchy process on the example of the right bank of the Kaniv Reservoir

The study of landslide-prone areas requires the analysis of a large amount of objective data, products of remote sensing data processing (spatial images, digital terrain elevation data and analytical data on calculated indices), analytical maps based on field measurements. All these data make it possible to objectively and more accurately characterize the studied territory. It is important, based on the results of the assessment, to get a conclusion about favorable, unfavorable and dangerous areas. The result is a landslide susceptibility map. In this study, we carried out the procedure of creating a landslide susceptibility map at the regional level for the Rzhyshchiv united territorial community of the Kyiv region. Nine factors that influence the development of landslides or become indicators of landslide processes are selected. A description of the algorithm for creating a landslide susceptibility map using the method of hierarchical analysis is provided. The obtained result gives an idea of the different propensity of the areas of the studied territory to the development of landslide processes, it means the areas of the greatest threats, which contributes to the rational adoption of management decisions.

Deformation Pattern and Failure Mechanism of Railway Embankment Caused by Lake Water Fluctuation Using Earth Observation and On-Site Monitoring Techniques

The prediction of railway embankment failure is still a global challenge for the railway industry due to the complexity of embankment failure mechanisms. In this work, the pre-failure deformation and the settlement from abnormal deformation to the final failure were investigated based on earth observation and on-site monitoring with a focus on the deformation stage and failure mechanism of railway embankments. Some new viewpoints are suggested: (1) the differential settlement of ~19 mm revealed via InSAR at the failure region of the embankment may have been caused by internal erosion after rapid drawdown. The cumulative settlement was found to increase with the decline of the lake water level. (2) The railway embankment experienced three phases of primary, secondary, and accelerated creep phases, similar to the evolution of most landslide or dam failures. However, the train loading and seepage force may have aggravated the secondary consolidation, promoting the embankment to enter the accelerated creep phase quickly. The deformation pattern was presented as an exponential curve trend. (3) The formation mechanism of embankment collapse can be summarized as “seepage failure-creep-shear slip-collapse” failure under repeated train loading and rapid drawdown. This work provides some clues for early warnings and for the development of maintenance plans.

A Landslide Displacement Prediction Model Based on the ICEEMDAN Method and the TCN–BiLSTM Combined Neural Network

Influenced by autochthonous geological conditions and external environmental changes, the evolution of landslides is mostly nonlinear. This article proposes a combined neural network prediction model that combines a temporal convolutional neural network (TCN) and a bidirectional long short-term memory neural network (BiLSTM) to address the shortcomings of traditional recurrent neural networks in predicting displacement-fluctuation-type landslides. Based on the idea of time series decomposition, the improved complete ensemble empirical mode decomposition with an adaptive noise method (ICEEMDAN) was used to decompose displacement time series data into trend and fluctuation terms. Trend displacement is mainly influenced by the internal geological conditions of a landslide, and polynomial fitting is used to determine the future trend displacement; The displacement of the fluctuation term is mainly influenced by the external environment of landslides. This article selects three types of landslide-influencing factors: rainfall, groundwater level elevation, and the historical displacement of landslides. It uses a combination of gray correlation (GRG) and mutual information (MIC) correlation modules for feature screening. Then, TCN is used to extract landslide characteristic factors, and BiLSTM captures the relationship between features and displacement to achieve the prediction of wave term displacement. Finally, the trend term and fluctuation term displacement prediction values are reconstructed to obtain the total displacement prediction value. The results indicate that the ICEEMDAN–TCN–BiLSTM model proposed in this article can accurately predict landslide displacement and has high engineering application value, which is helpful for planning and constructing landslide disaster prevention projects.

Experiences and practices from application of rockfall remedial works at MKD national road network

Rockfalls, landslides and floods continue to have a growing share in the overall damages and losses, influencing considerably the national budget. Recent experiences and practices related to application of stabilisation measures and remediation of rockfall zones along MKD national road network are presented in the article. Analyses are based on case histories where the prognosis of geotechnical conditions is a key element in definition of remedial measures for rock slopes. Some issues related with Consequence Class, as suggested in Eurocode 7 202x, and the concepts of Acceptable Level of Risk are presented. Proposals in definition of Acceptable Level of Risk, through combining Probability of Failure, Reliability Index and Consequence Classes, are given in the article. These findings can help in development and/or improvement of rockfall, landslide and flood risk management strategies.

Assessment of landslide hazard risks on the example of the land cadastre of Kosiv district

The purpose of the research presented in this article is to analyse landslide hazards by assessing the impact of the natural component of factors on land use within the Kosiv district of the Ivano-Frankivsk region. The increasing number of negative consequences of landslide processes in the study area, and especially in the Carpathian region of Ukraine, requires the creation of new approaches to land resource assessment and civil engineering to reduce the negative impact of landslides on the environment and human activities. Methods. Landslide processes were studied using geophysical methods, GPS and geostatistical methods of data analysis. Based on the created mapping layer of the land cadastre and the landslide hazard map for Kosiv district, a vector analysis was performed to assess the risk in each area according to its cadastral number. Results. The environmental and geological risk of the natural component of landslide hazard factors was calculated using geoinformatics and geostatistical analysis tools with the use of QGIS. An important result of the research is the creation of landslide risk maps of land plots based on data from the State Land Cadastre using the Kadastr.Live Toolbar plugin. Scientific novelty. For the first time an analysis of the natural component of landslide hazard risks for the territory of Kosiv district was carried out as a basis for the assessment of such risks for individual cadastral zones. The regulatory and monetary assessments were clarified based on individual territories, for conducting economic activities that require hydrogeological research or changing the intended use of land.. Practical significance. By applying the methodology of dividing landslide risk assessment factors into natural and anthropogenic components, it becomes possible to identify areas within the study area with the highest probability of landslide development and activation. This method helps to study the characteristics of natural factors and is useful in assessing the risk of landslides..

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.

Integrated Dynamic Model for Numerical Modeling of Complex Landslides: From Progressive Sliding to Rapid Avalanche

Landslides are one of the most common catastrophic mass flows in mountainous areas. The occurrence of fragmentation leads to the evolution of the integrity and stiffness of the sliding mass. The changes in internal composition caused by basal erosion and entrainment make the dynamic evolution of landslides more complex. To consider these complex processes, physics-based dynamic models are often used to analyze the dynamic characteristics of landslides. However, the proprietary assumptions of dynamic models often limit their application to complex events. A single dynamic model is often not competent for the analysis of landslides with evolving dynamic characteristics. In this study, two dynamic models are effectively integrated according to the evolving characteristics of the landslide. The common effects of basal erosion and entrainment are also considered. The maximum sliding velocity, accumulation range, and erosion depth characteristics of this integrated dynamic model are more consistent with the field than those of the single dynamic model. Under the terrain conditions of this study, within a few seconds of the triggering stage, if the occurrence of disintegration is advanced by 2 s, the maximum impact area will increase by about 3.1% to 4.1%, and the maximum kinetic energy will increase by more than 20%. Simulation results indicate that the changes in the integrity of the landslide body significantly affect the evolution of subsequent landslide dynamic characteristics.

Data-Driven Landslide Spatial Prediction and Deformation Monitoring: A Case Study of Shiyan City, China

Landslide susceptibility mapping (LSM) is significant for landslide risk assessment. However, there remains no consensus on which method is optimal for LSM. This study implements a dynamic approach to landslide hazard mapping by integrating spatio-temporal probability analysis with time-varying ground deformation velocity derived from the MT-InSAR (Multi-Temporal InSAR) method. Reliable landslide susceptibility maps (LSMs) can inform landslide risk managers and government officials. First, sixteen factors were selected to construct a causal factor system for LSM. Next, Pearson correlation analysis, multicollinearity analysis, information gain ratio, and GeoDetector methods were applied to remove the least important factors of STI, plan curvature, TRI, and slope length. Subsequently, information quantity (IQ), logistic regression (LR), frequency ratio (FR), artificial neural network (ANN), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) methods were performed to construct the LSM. The results showed that the distance to a river, slope angle, distance from structure, and engineering geological rock group were the main factors controlling landslide development. A comprehensive set of statistical indicators was employed to evaluate these methods’ effectiveness; sensitivity, F1-measure, and AUC (area under the curve) were calculated and subsequently compared to assess the performance of the methods. Machine learning methods’ training and prediction accuracy were higher than those of statistical methods. The AUC values of the IQ, FR, LR, BP-ANN, RBF-ANN, RF, SVM, and CNN methods were 0.810, 0.854, 0.828, 0.895, 0.916, 0.932, 0.948, and 0.957, respectively. Although the performance order varied for other statistical indicators, overall, the CNN method was the best, while the BP-ANN and RBF-ANN method was the worst among the five examined machine methods. Hence, adopting the CNN approach in this study can enhance LSM accuracy, catering to the needs of planners and government agencies responsible for managing landslide-prone areas and preventing landslide-induced disasters.

Kinematic Evolution of a Large Paraglacial Landslide in the Barry Arm Fjord of Alaska

AbstractOur warming climate is adversely affecting cryospheric landscapes via glacial retreat, permafrost degradation, and associated slope destabilization. In Prince William Sound, Alaska, the rapid retreat of Barry Glacier has destabilized the slopes flanking the glacier, resulting in numerous landslides. The largest of these landslides (∼500 Mm3 in volume) is more than 2 km wide and has the potential to generate a tsunami that could affect nearby recreationists, marine traffic, infrastructure, natural and cultural resources, and the community of Whittier, located 60 km from the landslide. Here, we combine landslide structural and kinematic element mapping with data acquired from bi‐yearly airborne lidar, multi‐week satellite‐based synthetic aperture radar (SAR), sub‐hourly ground‐based SAR, and seismic monitoring from 2020 to 2022 to characterize this landslide and examine its evolution. While some methods serve as a snapshot in time that is a culmination of events, others emphasize the ever‐evolving nature of the landslide and associated hazards. Four major kinematic elements define the overall structure of the landslide, which vary in deformation type and rate, from creep (5 mm per day over several months) to episodic movement (2 m in 30 days) and landslide‐wide to localized events. In some areas of the landslide, short‐term deformation deviates from structures formed by cumulative movement, implying structural and kinematic evolution associated with glacier retreat. These insights are important for assessing landslide hazards and hazard evolution for large, slow‐moving bedrock landslides in actively deglaciating environments.

Improving the Understanding of Landslide Development in Alpine Forest Regions Using the InSAR Technique: A Case Study in Xiaojin County China

Employing a small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and hotspot analysis, this study identified 81 potential landslides in a 768.7 km2 area of Xiaojin county, eastern Tibetan Plateau. Subsequent time-series deformation analysis revealed that these potential landslides are in the secondary creep stage. The newly identified landslides were compared to a landslide inventory (LI), established through field surveying, in terms of causative factors, including altitude, slope, relief amplitude, distance to river, distance to road, and slope curvature. From the comparison, the InSAR technique showed the following advantages: (1) it identified 25 potential landslides at high altitudes (>3415 m) in addition to the low-altitude landslides identified through the field survey. (2) It obtained approximately 37.5% and 70% increases in the number of potential landslides in the slope angle ranges of 20°–30° and 30°–40°, respectively. (3) It revealed significant increases in potential landslides in every relief amplitude bin, especially in the range from 58 m to 92 m. (4) It can highlight key geological factors controlling landslides, i.e., the stratigraphic occurrence and key joints as the InSAR technique is a powerful tool for identifying landslides in all dip directions. (5) It reveals the dominant failure modes, such as sliding along the soil–rock interface and/or interfaces formed by complicated combinations of discontinuities. This work presents the significant potential of InSAR techniques in gaining deeper knowledge on landslide development in alpine forest regions.

Destabilization Mechanism of Rainfall-Induced Loess Landslides in the Kara Haisu Gully, Xinyuan County, Ili River Valley, China: Physical Simulation

The destabilization mechanism of rainfall-induced loess landslides generally occurring in the Ili River Valley of China remains inadequately understood. This research investigates the variations accounting for the pore water pressure and vertical stresses in the landslide body during rainfall infiltration in Karahaisu landslide, Xinyuan County, Ili River Valley, China, through physical modeling tests together with the scanning electron microscope tests. The findings indicate that rainfall significantly increases the degree of water saturation within the slope, pore water pressure and vertical stresses. This increase is more pronounced in the later stages of rainfall, followed by a sharp decrease in both pore water pressure and vertical stresses post-landslide occurrence. The results also show the dynamic fluctuations in the size and structural complexity of soil particles and pores during the rainfall infiltration. Furthermore, the soil particles undergo fragmentation as a consequence of water infiltration, leading to soil body subsidence. This tensile fracturing, attributed to differential subsidence of the slope body, constitutes the fundamental cause of accelerated deformation and damage to the slopes. The evolution of continuous rainfall-induced landslides can be categorized into three stages: crack formation caused by compression and subsidence, uniform deformation by localized slip, and eventual damage by accelerated deformation.

Effects of Irrigation Projects on the Classification of Yellow River Terrace Landslides and their Failure Modes: A Case Study of Heitai Terrace

The study of the classification and failure modes of Yellow River terrace landslides under the influence of irrigation projects is of key importance to alleviate the paradox between the rapid evolution of terrace landscapes caused by landslides and the survival of local residents. However, such studies remain controversial, despite it being widely recognized that a rise in groundwater level caused by irrigation is a key factor associated with landslide failure modes. In this paper, we take the Heitai terrace as a case study. Using aerial images and field investigations, we classify landslides in the Heitai loess layer into type A landslides (not related to groundwater) and type B1 and B2 landslides (related to groundwater). We analyze the failure modes and disaster-causing characteristics of each type of landslide, and our results indicate that the attenuation in soil strength is a key factor common to both type A and type B landslides, based on which type A landslides with small volume and short sliding distance are able to block the previous spring discharge, causing a rise in localized groundwater, which further contributes to type B landslides; the location of previous type B1 landslides with a large volume and long sliding distance and type A landslides may be more susceptible to type B2 landslides with a small volume and short sliding distance, where there are low confining pressures during the lower soil shear process. Therefore, we believe that the inevitable interaction effects between the failure modes of landslides during landslide evolution, which govern the geomorphological evolution of the Heitai terrace, are unavoidable. Combining these data with numerical analyses, we further demonstrate that a rise in groundwater level and discontinuous attenuation of soil strength caused by changes in soil properties during irrigation together control terrace landslides and their failure modes. From the results of interferometric synthetic aperture radar time-series monitoring of Yellow River terrace activity with and without irrigation projects, and electrical resistivity tomography groundwater detection, we conclude that in the future, Heitai terrace will continue to experience a high intensity of landslide activity, and conditions for the most catastrophic type of landslide (type B1) will remain, including the high localized groundwater caused by previous landslides, and the discontinuous attenuation of soil strength caused by the deterioration in soil properties. In this context, we believe that slope-cutting engineering will be one of the most economical means to achieve future landslide-type transformation on the Heitai terrace; this will mitigate the process of geomorphological evolution and improve the human living environment.

Updated Global Navigation Satellite System Observations and Attention-Based Convolutional Neural Network–Long Short-Term Memory Network Deep Learning Algorithms to Predict Landslide Spatiotemporal Displacement

Landslide displacement prediction has garnered significant recognition as a pivotal component in realizing successful early warnings and implementing effective control measures. This task remains challenging as landslide deformation involves not only temporal dependency within time series data but also spatial dependence across various regions within landslides. The present study proposes a landslide spatiotemporal displacement forecasting model by introducing attention-based deep learning algorithms based on spatiotemporal analysis. The Maximal Information Coefficient (MIC) approach is employed to quantify the spatial and temporal correlations within the daily data of Global Navigation Satellite System (GNSS) observations. Based on the quantitative spatiotemporal analysis, the proposed prediction model combines a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture spatial and temporal dependencies individually. Spatial–temporal attention mechanisms are implemented to optimize the model. Additionally, we develop a single-point prediction model using LSTM and a multiple-point prediction model using the CNN-LSTM without an attention mechanism to compare the forecasting capabilities of the attention-based CNN-LSTM model. The Outang landslide in the Three Gorges Reservoir Area (TGRA), characterized by a large and active landslide equipped with an advanced monitoring system, is taken as a studied case. The temporal MIC results shed light on the response times of monitored daily displacement to external factors, showing a lagging duration of between 10 and 50 days. The spatial MIC results indicate mutual influence among different locations within the landslide, particularly in the case of nearby sites experiencing significant deformation. The attention-based CNN-LSTM model demonstrates an impressive predictive performance across six monitoring stations within the Outang landslide area. Notably, it achieves a remarkable maximum coefficient of determination (R2) value of 0.9989, accompanied by minimum values for root mean squared error (RMSE), absolute mean error (MAE), and mean absolute percentage error (MAPE), specifically, 1.18 mm, 0.99 mm, and 0.33%, respectively. The proposed model excels in predicting displacements at all six monitoring points, whereas other models demonstrate strong performance at specific individual stations but lack consistent performance across all stations. This study, involving quantitative deformation characteristics analysis and spatiotemporal displacement prediction, holds promising potential for a more profound understanding of landslide evolution and a significant contribution to reducing landslide risk.

Formation and Hazard Analysis of Landslide Damming Based on Multi-Source Remote Sensing Data

Remote sensing plays an increasingly important role in the investigation of natural hazards, not only by obtaining specific data related to hazards, but also by realizing targeted research by combining with other data and/or technologies. Small-scale landslide hazard chain events occur frequently in mountainous areas with fragile geological environments and have strong destructive effects, yet have been somewhat understudied. This paper analyzes the Zhoujiaba (ZJB) landslide hazard chain that occurred in Longnan City on 18 August 2020. On the basis of the comprehensive application of multi-source remote sensing data, combined with time-series InSAR technology, electrical resistivity tomography (ERT), and numerical simulations, we studied the formation mechanism, damming characteristics, and potential outburst scenarios of this event. Our research suggests that geological structure and strong natural weathering are the preconditions for landslide development, which is eventually induced by extreme rainfall. Specific topographic conditions determine the rapid sliding and accumulation of landslide materials, and ultimately result in river damming. Our simulation results showed that a flood, rather than a debris flow, will be the result of dam outburst. When the simulated upstream inflow is 1.5 times that when the landslide occurred, 68% of the downstream village area will be flooded. The artificial spillway can effectively reduce the scale of the potential outburst flood, but there remains a risk of dam failure owing to the shallow depth. Our study of the hazard chain of a small-scale landslide using a combination of methods will provide a valuable reference for the analysis and treatment of similar hazard chains.

The Mechanical Characterization of Pyroclastic Deposits for Landslide Early Warning Systems

Broad mountainous areas in the western Campania (southern Italy), where young pyroclastic deposits extensively outcrop, frequently experience rainfall-induced slope movements of different degrees of mobility, causing heavy damage and fatalities. Such landslides cannot be easily mitigated, and the implementation of physically based early warning systems is still not able to predict the post-failure evolution of slope movements and the exposed areas at risk. This paper is devoted to overcoming this limit. To this end, the mechanical characterization of pyroclastic soil, carried out through an extensive laboratory testing program, is presented and compared with those of two other ashy soils of different depositional mechanisms. The results show that the depositional mode influences soil properties; to begin with, it affects the unsaturated shear strength, whose intercept of cohesion is up to 5 kPa higher in ashes of flow deposition than in airfall ash deposits. The saturated undrained soil response allowed for the identification of different levels of susceptibility to the liquefaction of pyroclastic deposits, which is one of the main factors governing the post-failure evolution of landslides. Gathering all the acquired information, including saturated and unsaturated soil shear strength, permeability function, and water retention curves, into a soil database, it was possible to present all data under a unitary framework. Finally, the implementation of the proposed flowchart for a simplified assessment of post-failure evolution to be employed in regional early warning systems can enhance our knowledge of the areas at risk.

Detailed Inventory and Spatial Distribution Analysis of Rainfall-Induced Landslides in Jiexi County, Guangdong Province, China in August 2018

In recent years, with the intensification of climate change, the occurrence of heavy rain events has become more frequent. Landslides triggered by heavy rainfall have become one of the common geological disasters around the world. This study selects an extreme rainfall event in August 2018 in Jiexi County, Guangdong province, as the research object. Based on high-resolution remote sensing images before and after the event, visual interpretation is conducted to obtain a detailed distribution map of rainfall-induced landslides. The results show that a total of 1844 rainfall-induced landslides were triggered within Jiexi County during this rainfall event. In terms of triggered scale, the total area of the landslides is 3.3884 million m2, with the largest individual landslide covering an area of 22,300 m2 and the smallest one covering an area of 417.78 m2. The landslides are concentrated in the northeastern, central, and southwestern parts of the study area, consistent with the distribution trend of rainfall intensity. To investigate further the influence of the regional environment on landslide distribution, this study selects eight influencing factors, including elevation, slope aspect, slope angle, topographic wetness index (TWI), topographic relief, lithology, distance to river, and accumulated rainfall. The landslide number density (LND) and landslide area percentage (LAP) are used as evaluation indicators. Based on statistical analysis using a data analysis platform, the relationship between landslide distribution and influencing factors triggered by this event is revealed. The results of this study will contribute to understanding the development law of regional rainfall-induced landslides and provide assistance for disaster prevention and mitigation in the area. The research results show that the elevation range of 100–150 m is the high-risk zone for landslides. In addition, this study has verified previous findings that slopes in the southeast direction are more prone to landslides. The steeper the slope, the more significant its influence on landslide development. When the topographic wetness index (TWI) is less than 4, landslides tend to have a high-density distribution. Greater variation in terrain relief is more likely to trigger landslides. The instability of lithology in Mesozoic strata is the main cause of landslides. The farther away from the water system, the fewer landslides occur. An increase in cumulative rainfall leads to an increase in both the number and area of landslides.

Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China

Abstract. To enhance the timeliness and accuracy of spatial prediction of coseismic landslides, we propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software named Mat.LShazard V1.0. Based on this software, we evaluate the applicability of this improved spatial prediction strategy in six earthquake events that have occurred near the Sichuan–Yunnan region, including the Wenchuan, Ludian, Lushan, Jiuzhaigou, Minxian, and Yushu earthquakes. The results indicate that in the first stage (immediately after the quake event), except for the 2013 Minxian earthquake, the area under the curve (AUC) values of the modeling performance are above 0.8. Among them, the AUC value of the Wenchuan earthquake is the highest, reaching 0.947. The prediction results in the first stage can meet the requirements of emergency rescue by immediately obtaining the overall predicted information of the possible coseismic landslide locations in the quake-affected area. In the second and third stages, with the improvement of landslide data quality, the prediction ability of the model based on the entire landslide database is gradually improved. Based on the entire landslide database, the AUC value of the six events exceeds 0.9, indicating a very high prediction accuracy. For the second and third stages, the predicted landslide area (Ap) is relatively consistent with the observed landslide area (Ao). However, based on the incomplete landslide data in the meizoseismal area, Ap is much smaller than Ao. When the prediction model based on complete landslide data is built, Ap is nearly identical to Ao. This study provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages of emergency rescue, temporary resettlement, and late reconstruction after a major earthquake.