Landslides最新文献

On March 15, 2024, a wildfire occurred in Yajiang County, Sichuan Province, China, burned an area of 278.8 km² and influenced 250 catchments in the burned area. On April 23 and May 10–11, 2024, two rainfall events struck the burned area, inducing 8 and 54 post-fire debris flows, respectively. This news investigates the characteristics of the burned area, involving the burn severity mapping, and geometry analysis on the burned catchments. Then, the characteristics of the debris flows following the fire are unravelled based on preliminary field investigations. It is expected that the debris flows after the wildfire, particularly in the first rainy season, with the high susceptibility and low rainfall threshold, will attract researchers’ interest in monitoring and mitigating such unique debris flow disasters.

The Three Gorges Reservoir Area (TGRA) is highly susceptible to the reactivation of ancient landslides and the emergence of new ones due to the fragile geological conditions and the regulation of water levels. This study integrated multi-source data from space-air-ground-based platforms to investigate the dynamics and stability of the Daping landslide group on the bank of TGRA. Monitoring active landslide deformation is essential to ensure the safety of coastal residents and waterways. The results demonstrate that combining Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) and Distributed Scatterer InSAR (DS-InSAR) methods effectively increases measurement point density and enhances the visibility of displacement. The reliability of using InSAR monitoring results for landslide analysis has been validated through the consistent displacement trends between the InSAR and Global Positioning System (GPS) points with a high correlation (R² = 0.968). Spatial-temporal variations in the displacement of the Daping landslide group were observed during the monitoring period, attributable to fluctuating reservoir water levels and rainfall events. Additionally, Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) from Unmanned Aerial Vehicle (UAV) was employed to reveal the actual micro-geomorphological and topographical features related to landslide movement, such as the bank slump at the toe of the landslide. This bank slump itself is a consequence of water-level fluctuations following the impoundment of TGRA. Numeric simulation was conducted to quantitatively assess the influence of bank slump on the slope’s stability, the displacement results of which were consistent with those of GPS and InSAR. The evolution characteristics of the Daping landslide group were summarized as a sequence of events: bending-tensile cracking-sliding failure along weak surfaces and predicted future development. The findings of this study hold significant importance for effective monitoring of slope bodies in reservoir areas, providing valuable insights into potential landslide risks and contributing to overall safety measures.

 Volcanic debris avalanches are significant landslide events that shape volcanic landscapes globally. This study focuses on creating a comprehensive database of volcanic debris avalanches in Northwest Argentina through remote sensing analysis, leveraging the region’s well-preserved deposits in arid conditions. The database includes morphometric parameters extracted from 12-m spatial resolution TanDEM-X digital elevation models and literature, providing insights into the occurrence and characteristics of these potentially catastrophic events. The methodology involved compiling bibliographic and cartographic data, manual digitization of collapse scars and deposits, and computation of morphometric parameters in a GIS, integrating structural lineaments and hydrothermal alteration zones. The database, which comprises 19 records, features detailed data on scars and deposits, morphometric characteristics, and additional layers for regional lineaments and hydrothermal alteration zones. Statistical analyses reveal correlations between various morphometric parameters, with most avalanche directions aligning perpendicularly to regional tectonic trends and hydrothermal alteration zones identified as significant factors in volcanic instability. The majority of collapses originate from composite volcanoes, with larger collapses linked to dacitic compositions. Collapses have ages between the Upper Miocene and Pliocene. We deem that the database, accessible via the IBIGEO website, will be a valuable tool for researchers and national authorities for geological risk assessment, enhancing the understanding of the spatial and temporal distribution of volcanic debris avalanches in the Central Volcanic Zone of the Andes. Continuous updates and fieldwork are essential to validate and expand the database, addressing gaps and confirming remote observations, thereby contributing to global knowledge on volcanic sector collapses and associated risks.

The canyon section from Longyangxia to Liujiaxia in the upper reaches of the Yellow River, which is characterized by a significant number and large scale of landslides, is a typical area prone to landslides. Investigating the characteristics and causes of giant landslides in this region holds great significance for understanding and mitigating the risks associated with such disasters. This paper explored the development characteristics and induced causes of these giant landslides via field surveys, remote sensing interpretations, unmanned aerial vehicle (UAV) surveys, geological dating methods, and numerical simulation methods. There were 22 giant landslides in the study area. Their sliding face is deeply buried in sandy mudstone or sandy stone that is landslide strata and shows large differences in elevation of landslide front and back edges. Most of these landslides mainly occurred tens of thousands of years ago that is indicated by geological dating results. The causes behind these giant landslides have been discussed from various perspectives. Multiple factors, such as tectonic activity, rock properties (“genes”), river erosion dynamics, climate change influences, and ancient earthquakes, are believed to have contributed to the development of giant landslides in this region. Regional tectonic activities exert tectonic forces leading to structural plane formation within rock masses while providing initial conditions for deformation and failure processes associated with giant landslide occurrences. Since the Pleistocene, Yellow River erosion has created favorable spatial environments conducive to giant landslide development by altering slope stress conditions; furthermore, climate change weakens rock mass strength. Finally, earthquakes generate substantial energy serving as catalysts for triggering massive landslide events by inducing rock mass failures.

On the night of October 3, 2023, the moraine-dammed South Lhonak Lake in Sikkim suddenly discharged a substantial volume of water, resulting in a glacial lake outburst flood (GLOF) that caused 178 fatalities and the destruction of three downstream hydropower projects, making it one of the most devastating GLOF events in the Himalayas. Leveraging satellite imagery and numerical modeling, this study analyzed the long-term evolution and outburst mechanisms of South Lhonak Lake, as well as the propagation of the GLOF, to provide valuable insight for regional GLOF risk assessment and management. The South Lhonak GLOF was triggered by the collapse of a massive lateral moraine, with an estimated collapse material volume of 16.75 × 10⁶ m³. Following the outburst flood, the lake area decreased by 15.38%, from 1.69 ± 0.03 to 1.46 ± 0.03 km². The impact of the GLOF extended to 169 km downstream, corresponding to a total inundation area of 32.04 ± 1.91 km². A multi-phase r.avaflow model was employed to simulate the mass flow and cascading process. An impulse wave displacement speed of approximately 26 m·s⁻¹ was observed after the collapse of the lateral moraine, and the overtopping height on the moraine dam reached 16.11 m. The hydrograph at the dam breach site revealed that approximately 38.49 × 10⁶ m³ of water was released during the drainage process and that the peak discharge of 3.02 × 10⁵ m³·s⁻¹ occurred 140 s after the dam breach. Upon entering the downstream channel, the peak discharge was attenuated, and its duration was prolonged because of the influence of the valley terrain. Given the current challenges in accurately identifying potential avalanche zones, we identified a discrepancy between GLOF triggers and modeling scenarios. A novel framework for GLOF hazard assessment involves driving collapses of varying magnitudes to strike the lake and subsequently simulating GLOF initiation and downstream propagation for the most plausible outburst scenario. This conceptual approach can be used to design artificial drainage and determine dam immobilization engineering criteria and to evaluate the resistance performance of remedial measures under specific striking probabilities.

Large landslides often cause catastrophic life losses and infrastructure damage. Identification of the driving forces of large ancient landslides is of utmost importance for the understanding of geohazard assessment and regional geomorphologic evolution and for the understanding of regional paleoclimate and paleoseismology. Through field geological survey, multi-temporal satellite image interpretation, sedimentological observation, and static and dynamic numerical simulation, the paper studied the geo-environments and deposit succession of the Hongshiyan paleolandslide (HSYPL), over against the Hongshiyan landslide (HSYL) triggered by the 2014 Ludian M_S (surface wave magnitude) 6.5 earthquake. The study reveals that (1) the HSYPL and HSYL are symmetrically distributed on the opposite banks of the Niulan River and on the opposite wings of a vertical anticline plunging west. Both landslides involved an anti-dip slope structure of upper hard rock while lower soft rock. (2) Two phases of deposit succession in the paleolandslide accumulations were recognized from their surficial appearances, planar distribution, spatial superimposition relationship, permeability test, and borehole survey. (3) The deposit did not result from one single paleolandslide event but two long-interval individual events, i.e., penultimate landslide (PL) and last landslide (LL), whose source volumes were estimated to be ~ 11.8 Mm³ and ~ 113.5 Mm³, respectively. (4) These two landslides kept stable under static conditions but failed when the SN component acceleration reached 1.4 and 1.2 times the value of the 2014 Ludian M_S 6.5 earthquake. The ground motions basically correspond to the earthquake magnitudes that are back-analyzed by their volumes. (5) Both the penultimate landslide and last landslide were seismically triggered with high probability. The former was more likely due to the seismic activity of the Zhaotong-Ludian fault than the Baogunao-Xiaohe fault, while the latter might be induced by either fault which was active since the Holocene. Our findings present new insights into the regional seismological history and considerations on the risk reduction of the new hydro-project constructed from the Hongshiyan co-seismic landslide dam.

Glacial stability on the Tibetan Plateau has declined sharply in the context of global warming. Previously, continental glaciers on the northwestern Tibetan Plateau were considered stable and had little susceptibility to ice collapse. However, in recent years, numerous continental glacier ice collapses have resulted in significant economic losses, casualties, and ecological environmental damage. This study focused on eight watersheds in the Ngari Prefecture of the northwest Tibetan Autonomous Region, specifically in the upper reaches of Zecuo Lake. Continental glacier ice-avalanche disaster factors were examined based on topography, climate, and geological structure. This study proposes a continental glacier ice-avalanche susceptibility assessment method that considers both internal and external factors by combining the analytic hierarchy process (AHP) and cloud theory. A comprehensive evaluation and analysis revealed that the eight watersheds in the upstream study area of Zecuo Lake were greatly affected by climate change and geological structure, and the susceptibility assessment ratings for all glaciers were high. Therefore, it is essential to strengthen the monitoring and early warning systems in areas of human activity. The findings of this study provide a scientific basis for predicting, preventing, and mitigating continental glacier avalanche disasters on the northwestern Tibetan Plateau. Additionally, the proposed method can be applied to areas with similar geological conditions.

Based on a minimum amount of rainfall that when reached or exceeded can trigger landslides, rainfall thresholds are used to predict potential landslide occurrence and are essential parts of many landslide early warning systems. Despite the extensive literature on the definition and use of rainfall thresholds, little attention has been given to examining and comparing the mathematical methods that can be used to define thresholds as lower bounds of clouds of empirical rainfall conditions known to have triggered landslides. When multiple thresholds are available, it is unclear how to combine them. Here, we address both issues. We test and compare four mathematical methods to define event cumulated rainfall—rainfall duration, ED thresholds using 2259 measurements of rainfall duration (D, in hours) and cumulated rainfall (E, in mm) that resulted in mostly shallow landslides in Italy between January 2002 and December 2012. The methods cover a broad spectrum of data driven approaches, including a frequentist least square method, a frequentist quantile regression method, a Bayesian quantile regression method, and a machine-learning symbolic regression method. We apply and compare the methods for three non-exceedance probability levels, p = 0.01, 0.05, 0.10, and we propose a voting strategy to combine the predictions into a single, dichotomous—i.e. ‘sharp’—non-probabilistic landslide prediction that we apply to the available dataset of rainfall measurements.

The interplay between climate change–induced extreme rainfall and slope failure mechanisms presents a significant challenge. To address this, a new temporal modeling of landslides that integrates dynamic rainfall patterns with slope failure mechanisms is proposed. The approach features three steps: (1) analysis of the critical continuous rainfall (CCR) level using a physics-based model with Monte Carlo simulation; (2) calculation of the cumulative distribution function of the generalized extreme value distribution; and (3) estimation of the temporal probability map. Then, combined with the landslide spatial probability obtained from one-dimensional convolution neural network (1D-CNN), the landslide hazard probability was estimated for future periods of 5, 10, 20, and 50 years. The CCR and spatial probability maps were validated using the 2018 landslide event in Hiroshima Prefecture, Japan. The CCR map achieves an area under the receiver operating curve (AUC) of 74.8%. Cohesion and friction angle are the most sensitive in the hybrid model. The proportions of temporal probabilities > 0.5 yielded by the non-stationary model (10, 19, 28, and 38%) were greater than those of the stationary model (6, 10, 16, and 24%) for periods of 5, 10, 20, and 50 years, respectively. The 1D-CNN model (AUC = 84.1%) outperformed logistic regression (AUC = 80.1%) and naïve Bayes (AUC = 80.1%) models. The landslide hazard probability obtained from the non-stationary model is more susceptible than that of the stationary model. These results indicate that the proposed approach is a valuable tool for future landslide risk assessment and may be applicable even in areas without a landslide inventory.

Rainfall-triggered landslides are most deadly in developing countries, and future urban sprawl and climate change could intensify existing risks. In these regions, enhancing efforts in urban landslide risk mitigation and climate change adaptation is crucial. Current landslide probability assessment methodologies struggle to support effective mitigation because they fail to represent local anthropogenic factors (e.g. informal housing) across space and time scales. To meet this challenge, we demonstrated in previous work that hillslope-scale mechanistic models representing such localised changes can be used to create synthetic libraries of urban landslides that account for both data and future scenario uncertainty. Here, we show how these libraries can become an explorative tool for researchers and stakeholders, allowing them to investigate slope stability variations across spatial scales and landscapes. Results highlight, for example, how the main slope instability drivers change according to the location (e.g., upper vs lower catchment), the landcover (e.g. forest vs urban) and the spatial scale analysed (e.g. at hillslope scale slope stability was mostly controlled by water table height, whereas at regional scale by slope geometry). Ultimately, we demonstrate that stochastic analyses can lead to a greater understanding of the system interactions and they can support the identification of mitigation strategies that perform well across spatial scales and uncertain scenarios. These strategies should be prioritised even if future conditions are unknown. This reasoning is shown on a data-scarce region with expanding informal housing. However, the same methodology can be applied to any urban context and with any mechanistic-based model.