On December 18, 2023, an Ms 6.2 earthquake struck Jishishan, Bao’an, Dongxiang, and Salar Nationality Autonomous County, Linxia Hui Autonomous Prefecture, Gansu Province, China. This moderate-to-strong earthquake triggered more severe co-seismic landslides than expected, including the catastrophic Zhongchuan Town mudflow, which resulted in the loss of > 20 lives. By conducting comprehensive field surveys immediately after the earthquake, we present a detailed field panorama of the earthquake-triggered landslides and provide a field-based understanding of delineating the spatial distribution patterns and failure mechanisms of the landslides. The majority of the co-seismic landslides are small-to-medium scale shallow ones and concentrated in the loess area in Zhongchuan, Guanting towns of Qinghai Province and Dahejia, Shiyuan, Liugou towns in Gansu Province. Loess falls are the most common co-seismic landslide type and are usually distributed on steep cut-slopes of roads and loess terraces where human activities are intense. The second-most common loess slides tend to occur on valley slopes covered by thick loose loess. Landslides in the bedrock area are less frequent, and the majority are complex geo-disasters involving rock falls and debris avalanches that originated in the upper part of the slopes and tend to cluster at the northern end of Jishishan Mountain. Generally, the distribution and occurrence mechanism of the co-seismic landslides are closely related to lithology, seismic response, terrain, hydrogeology, and human activities in the areas where they originated. Measures need to be taken on the irrigation method and the discontinuities and suspended loose debris to avoid further post-seismic failures.

Reactivation of old landslides is increasingly reported worldwide, yet important uncertainties remain regarding how multi-stage reactivation evolves over decadal timescales and how the dominance of different triggers shifts across time scales, especially where extreme weather events, earthquakes, and human disturbance co-occur. Here we quantify 2014-2024 kinematics of two old landslides in the piedmont of the Qilian Mountains, China, using Interferometric Synthetic Aperture Radar (InSAR), and link deformation to hydro-ecological conditions, geomorphology, and anthropogenic disturbance. The landslides exhibit pronounced spatiotemporal deformation over the past decade, with cumulative displacement reaching -141 mm and peak velocities up to 52 mm/year. Notable spatial variability in deformation was observed, which strongly correlates with the distribution of springs, pastoral living sites, NDVI, and slope within the old sliding body. Deformation rates tend to be higher in gently sloping areas with a high density of pastoral settlements, indicating the potential destabilizing effect of human activities on slope stability. Analysis of reactivation mechanism indicates a strong positive correlation with increased temperature over the long term, while heavy rainfall, high temperature, and earthquakes (M > 6) act as short-term accelerators. InSAR time series further reveal three distinct acceleration modes, highlighting that reactivation is not a single process but a sequence of stage-dependent responses to compound forcings. Moreover, we analyzed the impact of old landslide reactivation and revealed significant expansion of boundaries, gullies, and channels, and a decline in NDVI between 2014 and 2024. It also induced secondary landslides and debris flows, increasing the risk of cascading geological hazards. Our results provide a decadal, process-based template to diagnose time-scale-dependent controls and acceleration modes of old landslide reactivation, with implications for hazard assessment in cryosphere-seismic transition zones.

The Donghekou landslide (2.6×107 m3), situated at the terminus of the Beichuan-Yingxiu Fault, was triggered by the 2008 Mw 7.9 Wenchuan Earthquake, causing heavy losses and attracting extensive academic attention. Because of steep alpine valley terrain and vigorous tectonic movement, disputes remain over its fault-controlled formation mechanism, runout path and trajectory deflection caused by terrain collision. In the current study, a discrete element model coupled with spherical particles and rigid blocks was established to simulate the dynamic processes and hypermobility mechanisms of the Donghekou landslide. Its composite genesis of debris flow and rock avalanche, as well as the interaction between landslide motion and fault deformation are systematically analyzed. The results indicate that the entire disaster evolution lasts approximately 200 s, and the rock avalanche lasts 150 s with a peak velocity of 68.5 m/s. High-velocity mass ejection generates long-runout debris flow. Unlike two-dimensional simulation outcomes, actual movement reveals obvious trajectory deflection after topographic collision. The accompanying lateral material entrainment and basal weathered layer erosion significantly further expand landslide volume and momentum, with a maximum runout distance of 2384 m. Landslide migration is jointly dominated by steep topographic gradients and sliding surface morphology. Southwestern debris flow failure is dominated by fault terminal rupture effect and steep terrain, triggering rock extrusion, fragmentation and rapid particle ejection. By contrast, northeast rock avalanche is governed mainly by shear failure along structural planes. They constitute a complete evolutionary chain including rock fragmentation, ejection, collision, erosion, river damming and barrier lake formation. This study indicates that the dynamic mechanism of large composite seismic landslides depends not only on material properties and structural features, but also on the combined effects of regional landform conditions, fault deformation characteristics and sliding surface geometries.

Landslide-damming events, occurred frequently in Southeastern margin of the Tibetan Plateau, often can be served as valuable geological records for reconstructing tectonic activities and understanding river evolution processes. The Ninglang paleo-landslide, situated on a branch fault of the Chenghai Fault Zone in Ninglang County of China, exemplifies such phenomena. This study combined field investigations, UAV photogrammetry and dating methods to analyze the characteristics, formation mechanism and evolutionary process of this landslide damming event. The results show that the Ninglang paleo-landslide with an estimated volume of 3.7 × 10⁷ m3 was triggered by seismic activity. The landslide formed one about 70 m high dam to create a dammed lake with a covered area of approximately 2.84 × 10⁶ m2, significantly changing the local geomorphology. According to the dating results, the dammed lake could be formed from at least 33.1 ka BP and breached at about 0.05 ka BP, which indicated that the dam kept over 33 ka. The failure mechanism is attributed to a branch of the Chenghai Fault, which crossed the landslide area. Fault movement created a weak structural zone within the dam body, which became a preferential flow path for water. This concentrated erosion initiated piping, a process that was accelerated by the warm and humid conditions of the Holocene Climate Optimum, ultimately leading to the dam's breach. The findings in this study provide valuable insights for similar landslides and contribute to a more comprehensive understanding of landslide-induced landscape evolution of river catchments.

The equivalent shear strength (ESS) of a rock mass with multiple nonpersistent joints is a critical input parameter for stability analysis using the equivalent approach. However, accurately evaluating ESS remains a significant challenge in rock mechanics due to the inherent complexity of joints. To gain deeper insight into the ESS, this paper conducts systematic laboratory direct shear tests on rock-like samples, accounting for key geometric parameters such as persistence, inclination angle, and number of nonpersistent joints. By integrating physical observations with microelement mechanical analysis, the mechanism of mechanical degradation of rock bridges under compressive-shear loading is revealed. Furthermore, an improved Jennings criterion for ESS evaluation is derived, incorporating both joint geometric parameters and the mechanical degradation of rock bridges. Validation against experimental data demonstrates that the improved criterion achieves significantly higher accuracy than the original model. These results provide an in-depth understanding of the variables influencing ESS from multiple perspectives. Highlights

Landslide susceptibility assessment is essential for hazard prevention and risk management in mountainous regions. Conventional susceptibility models mainly rely on static conditioning factors and often underuse dynamic information such as surface deformation. In this study, surface deformation refers to measurable ground motion detected by InSAR, represented by line-of-sight (LOS) cumulative displacement and deformation velocity, and treated as dynamic evidence of slope activity. To integrate such information into susceptibility mapping, we propose a deformation-informed posterior probability updating framework at the slope-unit scale. First, a Random Forest model is used to estimate prior susceptibility probability from conventional static factors. Then, three robust InSAR-derived indicators are constructed for each slope unit: deformation intensity (VQ95), activity (Aact), and data density (density), representing deformation magnitude, activity level, and observation reliability, respectively. These indicators are introduced in logit space to update the prior probability and generate a deformation-updated susceptibility probability. The framework is applied to the Motuo area, China. Compared with the prior model, the updated model improves AUC from 0.9241 to 0.9809, PR-AUC from 0.9218 to 0.9759, and Recall from 0.8115 to 0.9791, while reducing LogLoss from 0.3664 to 0.2594. In addition, the coverage of known landslides within the high and very high susceptibility zones increases from 86.9% to 91.6%. These results show that deformation-informed probability updating can improve the identification of high-risk slopes in actively deforming mountainous areas and provide an interpretable strategy for integrating dynamic deformation information into landslide susceptibility assessment.

Abstract Background Landslides are among the most widespread and destructive geological hazards in China, driven by complex mechanisms involving multiple interacting factors. Their impacts are particularly severe when intense rainfall and strong ground motion occur in combination. At the national scale, existing landslide hazard assessments often rely on empirical or machine learning approaches. While effective for regional-scale applications, these models are constrained by incomplete inventories and often lack sufficient physical interpretability, which restricts their use for physically interpretable, scenario-based slope-stability assessment. Results To address these limitations, we propose a scenario-based physically informed framework for national-scale landslide slope-stability assessment in China. This framework integrates the TRIGRS and Newmark models to simulate slope stability under 15 rainfall intensity–duration scenarios and 10 seismic ground motion levels. Failure-probability estimation is supported by incorporating the Rosenblueth point estimate method and empirical failure probability curves to translate model outputs into probabilistic metrics. Furthermore, a sequential rainfall-conditioned seismic scenario involving extreme rainfall followed by earthquake shaking is introduced to evaluate nonlinear amplification effects from multi-source interactions. The results indicate that comparatively high rainfall-induced failure probabilities occur in the southwestern mountainous regions and the Loess Plateau. In contrast, areas with comparatively high earthquake-induced failure probability expand significantly with increasing PGA, with comparatively unstable areas primarily located along the western Sichuan area, northern Shaanxi, and eastern Tibet, where tectonic activity is intense. Under the sequential rainfall–earthquake scenarios, the predicted unstable area is substantially larger than that under the corresponding single-trigger scenarios, indicating pronounced within-model scenario amplification. Conclusion These findings enhance the understanding of spatial distribution patterns of landslides under multi-source triggering conditions and provide a useful reference for national-scale scenario-based landslide assessment and hazard-informed planning.

Human engineering activity, such as cross-regional transportation construction, often disturbs the geological environment and triggers landslides. This study investigated a landslide induced by tunnel excavation in the northeastern region of the Qinghai-Tibet Plateau, exploring how a seemingly low-risk local small-scale landslide can trigger an engineering disaster. Based on field geological and geomorphological surveys, unmanned aerial vehicle (UAV) remote sensing photography, and SBAS-InSAR data analysis (time-series monitoring from 2021 to 2023), the spatiotemporal evolution patterns and causative mechanisms of landslide deformation were systematically elucidated. The results indicate the following: (1) The landslide evolved from initial multiple small local slides, gradually expanding and connecting to form a larger and deep-seated landslide. (2) SBAS-InSAR analysis revealed that the landslide deformation rate ranged from -38.13 to 12.01 mm/a, with a maximum cumulative deformation of 121.91 mm. Substantial deformation was concentrated in April-June 2021, June-August 2022, and April-July 2023. Spatially, the deformation intensity exhibited a pattern of middle section > front > rear, with greater deformation closer to the tunnel construction point. (3) The landslide deformation is primarily related to tunnel construction disturbance. The topography, geological structure, and frozen ground thawing exerted certain influences. The deformation mechanism is summarized as follows: Slope toe excavation initially triggers local sliding, leading to tension cracking at the rear edge. Subsequently, tunnel construction further promotes landslide expansion, resulting in the formation of a deep-seated landslide. This study showed that the landslide resulted from the combined effects of engineering activity and natural conditions. The results reveal that, under disturbances from inappropriate engineering activities, local small landslides may develop into major disasters. Therefore, the construction plan for the tunnel must be revised to mitigate such risks.

Landslide activity in the Qilian Mountains is increasing due to permafrost thawing driven by global warming, where also developed numerous reverse strike-slip active faults with frequent and high-intensity earthquakes. While many studies have examined the influence of climate change on landslide activity in permafrost regions, the role of earthquakes in modulating landslide behaviour in such environments remains less well quantified. In this study, Interferometric Synthetic Aperture Radar (InSAR) was employed to investigate whether the 2022 Menyuan earthquake induced post-seismic acceleration of landslides in permafrost areas. The results show that the earthquakes exerted a pronounced impact on landslide deformation, producing an instantaneous subsidence of approximately 24.83 mm, which is substantially larger than the maximum annual displacement observed before the event. Moreover, post-seismic landslide velocities increased by up to 21 mm/year, and the seasonal deformation amplitude after the earthquake was approximately twice that observed during the pre-seismic period. To further elucidate the mechanisms underlying these responses, we quantified the dominant controlling factors of landslide deformation using a Geographic Detector approach. The results indicate that landslides located closer to active faults exhibit larger displacement rates, although no clear hanging-wall or footwall effect was observed. Landslides at higher elevations, particularly above 3,450 m, are more susceptible to deformation. Importantly, we find that landslides under colder ground temperature conditions are more strongly affected by seismic shaking and tend to exhibit larger post-seismic deformation, with a 0.2 degrees C decrease in ground temperature corresponding to an increase of approximately 5 mm/year in displacement rate. These findings provide new quantitative insights into the role of earthquakes in controlling landslide activity in permafrost regions and highlight the importance of permafrost thermal conditions in modulating post-seismic landslide behaviour.

The landslide disaster database is a prerequisite for regional landslide disaster research, and summarizing and analyzing the distribution pattern and influencing factors of landslide disasters is of great significance for carrying out the susceptibility and hazard assessment. The study area is a typical southwest mountainous area, and geological disasters such as landslides are very serious. A total of 3573 landslides were identified after a combination of image interpretation and field investigation in an area of 8.4 km2.This paper conducted a spatial analysis to reveal the distribution laws of landslides and analyzed the relationship between landslide and 13 influencing factors such as elevation, slope, slope aspect, topographic relief, soil, land use, lithology, annual average rainfall, ground peak ground acceleration (PGA). It can be concluded that the landslide showed the characteristics of non-uniformity and zonal distribution. A statistics analysis indicates that landslides are significantly correlated with elevation, slope gradient, slope direction, distance from faults, lithology, rivers, highways and so on. Therefore, when constructing engineering in alpine and canyon areas, it is essential to avoid the areas with steep slopes, large height difference, active faults, and the area being distributed by soft or broken hard rock masses to reduce disaster risks.

The Yalong River Basin (YRB), situated on the eastern margin of the Tibetan Plateau, is characterized by high vertical erosion rates, rugged terrain, narrow gorges, and steep hill slopes, which host many large landslides. Analyze the distribution characteristics of landslides in this region is significant for understanding geomorphic evolution and assessing potential landslide risks. In the current study, a landslide database for the Yalong River was initially established based on remote sensing interpretation and field investigation. A total of 729 landslides were mapped, including 191 medium-scale and 538 large-scale landslides. According to the updated Varnes classification, 538 large landslides (73.8 %) were recognized and divided into four major types: slides (344 cases), flows (109 cases), topples (56 cases), and slope deformations (29 cases). The total area of these landslides was 886.2 km2, and they were predominantly concentrated in the alpine canyon areas along the middle segment of the Yalong River. Based on integrated kernel density estimation and spatial autocorrelation, three landslide cluster zones (A, B, and C) were identified, with zone B showing the highest concentration of large landslides. Statistical results indicate that large landslides are concentrated in areas with elevations of 1500-3500 m, slopes of 20 degrees-40 degrees, topographic reliefs of 400-600 m, and dominant aspects being E, SE and SW. Active faults in the YRB play a crucial role in the formation and spatial distribution of landslides, showing a notable correlation with fault geometry, arrangement, and movement types.

Coseismic landslides pose significant threats to seismically active mountainous regions, where the interplay of topographic, geological, and seismic factors jointly controls slope failures. Conventional susceptibility models often fail to capture nonlinear feature interactions while maintaining physical interpretability. To address this issue, we introduced an interpretable deep learning framework—Superposable Neural Network (SNN)— to seismic landslide susceptibility modeling for the first time. This innovative modeling framework employs additive, independent sub-networks to represent both individual predictor influences (Level-1 features) and their pairwise interactions (Level-2 features) with transparent interpretability. Such a configuration was used to quantify the contribution of each factor and their combinations, and to evaluate the improvements in predictive performance when accounting for feature interactions. The results indicate that peak ground acceleration (PGA), slope angle, and distance to the seismogenic fault (dis2fault) are the most influential Level-1 predictors. Furthermore, composite interactions such as “slope × PGA” and “rainfall × lithology” have substantially improved the model’s interpretation capability. The SNN effectively captures the spatial heterogeneity of landslide distribution and delineates high-susceptibility zones where ground motion amplification exacerbates slope instability. Compared with conventional models, the proposed approach delivers superior predictive performance and enhanced interpretation capability. This study validates the SNN as a robust and effectively explainable tool for seismic landslide hazard assessment in complex tectonic settings.

The position of landslides on a slope plays a crucial role in determining landslide susceptibility and the likelihood of landslide debris interacting with the fluvial system. Most studies primarily focus on shallow landslides in the bedrock weathering zone or large-scale bedrock landslides, but the relevant work about the location and connectivity to channels of loess landslides is limited despite their potential to provide insights into slope stability and material transport in loess regions. In this study, we explored differences in landslide location and connectivity to channels between 2013 Mw5.9 Minxian earthquake-induced (EQ) landslides and 2013 Tianshui rainfall-induced (RF) landslides in the Loess Plateau area, China. The result shows that more than 37% of EQ landslides occur in the vicinity of ridges and similar to 30% are concentrated near river channels. Landslide locations of the Minxian earthquake not only occur in ridge crest areas but also exhibit clustering near the channels. We attribute the former cluster to seismic shaking along the ridge crest, and the latter cluster to dynamic changes in pore pressure within saturated lower hillslopes due to nearly a month of rainfall prior to the Minxian earthquake. Compared to EQ landslides, RF landslides are more evenly distributed across slopes. However, due to heavy rainfall and river erosion, landslides are more concentrated in the middle and lower slope areas, especially near the river channels. Moreover, the connectivity of landslides to channels indicates that RF landslides exhibit stronger connectivity with river channels compared to EQ landslides, which may be related to the concentration of EQ landslides near ridge areas. Furthermore, due to the smaller scale of EQ landslides compared to RF landslides, larger landslides are more likely to be located closer to river channels. This may contribute to the lower observed connectivity index between EQ landslides and river channels.

The northern part of the Xiaojiang fault zone was chosen as the research location, and a support vector machine (GEO-SSA-SVM) model optimized by GEO and the Sparrow search algorithm (SSA) was created with the slope unit as the evaluation unit. Seven hundred eighty-four landslide points’ worth of data were gathered via field research and the analysis of remote sensing images. Compounded with the GEO thresholds q(> 0.0179) and p(< 0.1225), eleven important factors were chosen as the landslide susceptibility evaluation criteria. The SSA approach increases the model’s capacity for generalization and prediction accuracy by fine-tuning the parameters of the SVM model. The findings demonstrate that GEO filtering can greatly increase the SVM model’s prediction accuracy. When landslide susceptibility is predicted, the GEO-SSA-SVM model clearly outperforms other conventional models, with an accuracy of 85.80 and an AUC value of 0.915. It is necessary to conduct landslide susceptibility assessment in order to prevent and reduce disasters. This model offers a fresh viewpoint and approach to assessing the susceptibility of landslides.

Catastrophic landslides often occur along the southeastern margin of the Tibetan Plateau because of strong earthquake/faults and heavy rains. In this study, 26 large-scale landslides were collected from the middle segment of the Yalong River to analyze landslide features and possible formation mechanism. The investigation results revealed that landslides featuring a linear distribution along the riverbanks can be classified into three failure types: tensile cracking-sliding, sliding-bending (crushing and buckling), and toppling. Among them, the Xiamajidian landslide at the junction area between the river and the Qianbo fault is being dangerous with obvious deformation, including different subzones and different failure types. The landslide body is delineated into three distinct zones (A, B, and C) based on different deformation features and material compositions. Among them, the Zone A with the largest deformation is dangerous, the front of which is obviously moving toward the river channel. The deformation monitoring data indicated that the 2008 Ms 8.0 Wenchuan earthquake caused only slight disturbances to the Xiamajidian landslide body, but the subsequent 2008 Ms 6.1 Huili earthquake caused the deformation to increase quickly. The distinct-element method is then used to determine the importance of strong earthquakes and heavy rainfall during landslide failure. The results suggest that the landslide may have been broken to form a large landslide event, and finally to form a large landslide dam to block the Yalong River. The results presented in this paper are helpful for disaster prevention and risk evaluation.

The detailed seepage process and rainfall infiltration temporal variation cannot be observed visually due to the complexity and non-visibility of the internal structure of landslide body, thus making it difficult to identify the specific process of rainfall in the whole process of landslide catastrophic evolution, which leads to the incomplete understanding of the formation mechanism of rainfall-induced landslide. Therefore, the new technological methods for directly observing and monitoring the infiltration and seepage process of rainfall-induced landslide is of great significance for deep understanding the formation mechanism of landslide induced by raining. Transparent soil test technology enables non-invasive, continuous, non-destructive and visual measurement or monitoring inside soil or rock mass bodies. In this paper, the current status of the development of transparent soil testing technology and its application in different fields especially in the application of slope engineering and practice in the visual simulation of seepage processes are summarized from the aspects of transparent soil material properties, geotechnical engineering properties, experimental equipment and image processing analysis technology, and the feasibility of applying it to the visual observation of seepage in landslide is discussed. On this basis, the transparent soil material is screened and the preparation method is optimized. The transparent soil is used to replace the traditional geotechnical model material to establish a landslide transparent soil physical model suitable for seepage process observation. The physical simulation test of landslide seepage under rainfall conditions is carried out to obtain information on the whole process of rainfall infiltration-induced landslide disaster, and to determine the process and characteristics of the seepage process of the groundwater. The model test is an effective application of transparent soil test technology in the field of landslide seepage visual observation, expanding a new way of landslide disaster visual simulation, and further determining the complex impact of groundwater seepage on landslide. It is conducive to reveal the evolution law of rainfall-induced landslide, explore the sliding mechanism of landslide, and provide a scientific and technological support for rainfall landslide disaster prevention and disaster planning.

The Zhaotong area in Yunnan Province stands out as one of the most susceptible areas to landslide disasters. The landslide susceptibility of the Zhaotong area can be attributed to its steep terrain, fractured rock formations and strong rainfall, compounded by its frequent seismic activity. This study utilized landslide data provided by the Zhaotong City Natural Resources and Planning Bureau and visually interpreted from high-resolution satellite images of Google Earth to establish the landslide database of the Zhaotong area, including 161 landslides and 3646 potential geological disasters. The distribution characteristics and possible influencing factors of landslides within the Zhaotong area were analyzed using the aforementioned data. The results show that the spatial distribution of landslides and potential geological disasters is roughly consistent; the most concentrated landslides occurred at the junction of Yiliang County, Zhaotong City, and Daguan County, indicating the necessity to enhance surveillance of these landslide-prone areas. The relationship of landslide locations and different influencing factors suggests that elevation, slope angle, and distance to rivers are closely related to landslide occurrence. Landslides are more likely to occur in areas with lower elevations with slope angles ranging from 10° to 40° and near river channels.

The Huayingshan coalfield is one of the most important coal districts in Southwest China. Coal mining may have an impact on the hydrochemical characteristics and regional evolution of karst groundwater. This study aims to analyze the hydrogeochemical characteristics, identify the evolution processes, and influencing factors that govern the hydrochemistry in multilayer karst aquifers in the coalfield. Statistical methods and conventional techniques were utilized to gain a thorough understanding of the origin and hydrogeochemical evolution of karst groundwater. The results revealed that the groundwater was fresh water and natural to mildly alkaline. It suggested that the relative abundance of main ions was proposed to be Ca2+ ≫ Mg2+ > K+ + Na+ for cations and HCO_3^ - ≫ SO_4^2 - > Cl– > NO_3^ - for anions. A Piper diagram of the investigated water samples demonstrated that most groundwater was of the HCO3-Ca type. The results showed that dissolution of carbonate, gypsum, halite, and silicate minerals highly influenced the formation of HCO_3^ - , SO_4^2 - , Ca2+, Mg2+, and Na+. Cation exchange and/or absorption was another important regulatory process. NO_3^ - concentrations were excessively high, proving that karst water was affected by agricultural activities in certain aquifers. Moreover, S2– concentrations were high in the borehole and mine tunnel samples, suggesting great acidification potential. Coal mining carries a risk of deteriorating the local water environment. This exposes sulfide minerals to oxygen and water, increases SO_4^2 - concentration, and reduces groundwater pH. Scientific research must focus on specific recharge area locations, runoff and drainage pathways, and hydrochemical evolution processes of karst water, and the contact of sulfide with water and oxygen must be controlled to protect groundwater quality and reduce pollution. The results suggest it may be helpful for investigation and treatment of water environment pollution, aid the protection of karst groundwater in the Huayingshan coalfield, and serve as a model for other comparable studies.

On December 18, 2023, at 23:59, a magnitude MS 6.2 earthquake struck Jishishan County, Gansu Province. The earthquake triggered a severe liquefaction landslide-mudflow-blockage disaster chain event near Zhongchuan Town in the neighboring Haidong City, Qinghai Province (referred to as the “disaster chain event”). This event caused extensive damage to infrastructure, including residential buildings, power transmission towers, and roads, leading to significant loss of life and property. Based on extensive geological surveys conducted at the disaster site, this study employs a combination of methods, including satellite remote sensing image interpretation and three-dimensional real-world models from unmanned aerial vehicles (UAVs). The primary objective is to elucidate the fundamental developmental characteristics and evolution process of the disaster chain event. Moreover, this study preliminarily explores the formation mechanism of this disaster by integrating the topography, lithology, hydrogeological conditions, and seismic triggering factors. The results suggest that the disaster chain event was controlled by unfavorable local hydrogeological conditions and was triggered by the intense shaking of the Jishishan earthquake, resulting in the occurrence of loess liquefaction landslides. The mobilized mudflow material flowed remotely along the low-friction frozen channel. During the movement stage along the channel, the landslide-mudflow chain transitioned into a blockage failure event due to the obstruction of the downstream earth dam, thereby exacerbating the destructive capability and disaster scope of this event. Further in-depth analysis of the formation conditions and mechanisms of the disaster chain event carries significant implications for guiding disaster reduction and prevention in potential hazard points with similar geological conditions in the northwest Loess Plateau region of China.

The megaflooding caused by outbursts from Late Quaternary glacially-dammed lakes in the Yarlung Tsangpo Gorge (YTG) potentially shaped the fluvial landscape and controlled the geodynamic evolution of the Namche Barwa Syntaxis in the eastern Himalaya. However, the sedimentary evidence for such flooding in the narrow reaches of the Yarlung Tsangpo River (YTR) valley has been lacking. In this study we conducted geomorpho-logical and sedimentological analyses of the Motuo stretch of the YTR downstream of the Yarlung Tsangpo Gorge and combined these with dating results to identify multistage high magnitude outburst paleofloods. One meg-aflood event was found to have occurred at -5 ka, with a minimum discharge of -1.1-4.4x10(6) m(3)/s. Our results suggest that this event exceeded 1000 km in range, and was consistent with the age of flood deposits down-stream, where the YTR becomes the Siang-Brahmaputra River. Evidence of older and younger flood deposits was also obtained. The erosion caused by this megaflood was stronger in the steep river channel that flows through the uplift center of the Namche Barwa Massif. This may have been principally a response to the differential uplift caused by the main boundary faults. The megaflood event also gave rise to the headward erosion of the YTR's northern tributaries. The megaflood sediment zone is concentrated in the middle and lower reaches of the YTR where the valley widens. Here, the gradient of the river channel is shallower, potentially reflecting weaker tectonic activity accompanied by slower fluvial erosion. Furthermore, multistage catastrophic floods from the Yarlung Tsangpo Gorge may have significantly impacted any downstream prehistoric human settlements.