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Slow-moving landslide-prone areas (SLAs) are unstable objects on the terrestrial surface that can collapse rapidly when provoked by earthquakes, leading to infrastructure damage. It is critical to identify SLAs prior to earthquake events and assess their hazard-causing probabilities when triggered. An assessment approach of earthquake-triggered geohazards is proposed in this paper by combining interferometric synthetic aperture radar (InSAR) derived SLAs and geological and geomorphological factors. Taking the Ms6.8 Luding earthquake, which occurred in the Sichuan Province of southwestern China on September 5, 2022, as an example, 1320 scenes of Sentinel-1 SAR data in western Sichuan were processed using the small baseline subset (SBAS) InSAR technique before the earthquake. After the earthquake, hazard probability assessment was performed in real-time by filtering the SLAs using a spatial analysis technique with geological and geomorphological factors, e.g., real-time peak ground acceleration (PGA), slope, distance to fault (DTF), and distance to the river (DTR) data. The results show that 11 SLAs were classified into high-risk zones. As verified by the Luding co-seismic landslide dataset from visual interpretation of optical remote sensing images, 142 coseismic landslides were triggered by the earthquake in these high-risk regions. In these areas, an ancient landslide, with high pre-earthquake displacement rates (−50 mm/year) on the scarp was reactivated under the Luding earthquake forces. This method can provide a scientific tool for disaster mitigation and rapid response emergency management. [ABSTRACT FROM AUTHOR]

On April 20, 2013, a Mw 6.6 earthquake struck Lushan County of Sichuan Province, China, about 80 km southwest of the epicenter of the 12 May 2008 Wenchuan Mw 7.9 event. It triggered a large number of landslides in a broad area, causing causality and injuries and damages to roads, drains, dwellings, and infrastructures. Field investigations of coseismic landslides provide a basis for better understanding and illustrating the spatial distributions and hazards related to the Lushan earthquake-triggered landslides. Therefore, this paper presents results of field investigations of the Lushan earthquake-triggered landslides, which can be classified into two main categories of disrupted landslides and coherent landslides, and constitutes seven types. The disrupted landslides include five types: rock falls, rock slides, rock avalanches, soil falls, and soil slides; and the coherent landslides have two types: soil slumps and slow earth flows. All of the seven types of landslides can be observed in many field photos, aerial photographs, and/or satellite images. Among them, seven typical large-scale landslides, distributed in a northeast trending line consistent with the Shuangshi-Dachuan fault, which is presumably related with the earthquake, are described in detail. We prepared an original, emergency-based coseismic landslide inventory map based on visual interpretation of available post-earthquake aerial photographs and pre-earthquake satellite images, combined with results from emergency-based field investigations. The original inventory map registered 3884 landslides. Later, 11,761 more landslides were identified by much more careful and time-consuming visual interpretation based on the same aerial photographs and satellite images. Subsequently, all the 15,645 coseismic landslides were correlated with six common landslide impact factors, including elevation, slope angle, slope aspect, topographic position, stratum/lithology, and peak ground accumulation (PGA). These observational and compiled coseismic landslide data allow us to make subsequent compiling of a detailed and completed landslide inventory by visual interpretation of remote sensing images as well as further studies of landslides induced by the Lushan earthquake. [ABSTRACT FROM AUTHOR]

On September 5, 2022, an Ms6.8 earthquake struck Luding County, Sichuan Province, China. Through creating a coseismic landslide prediction model, we obtained the spatial distribution of the triggered geological hazards immediately after the earthquake. Through collecting all available multi-source optical remote sensing images of the earthquake-affected area via UAV and satellite platforms, the exact information of coseismic landslide was achieved by pattern recognition and visual inspection. According to the current results, the Luding earthquake triggered 5336 landslides with a total area of 28.53km2. The spatial distribution of the coseismic landslides is correlated statistically to various seismic, terrain, and geological factors, to evaluate their susceptibility at regional scale and to identify the most typical characteristics of these failures. The results reveal that the coseismic landslides mainly occurred on the sides of the Xianshuihe fault (within 1.2 km) and Dadu River (within 0.5 km) in striped patterns. They are concentrated in the regions with an elevation range of 1000–1800 m, a slope range of 25–55°, and lithologies of acid plutonic rocks, mixed sedimentary rocks, and siliciclastic sedimentary rocks. Besides, the coseismic landslides of the Luding earthquake are smaller in size and shallower than those triggered by the 2008 Wenchuan earthquake and the 2017 Jiuzhaigou earthquake. Rapidly achieving the spatial locations and distribution patterns of the coseismic landslides enables to provide effective support and guidance to emergency rescue, risk mitigation, and reconstruction planning. [ABSTRACT FROM AUTHOR]

In tectonically active steep mountain belts, such as those in west Sichuan in China, frequent landslide disasters can occur during the rainy season. High-locality landslides often initiate with the failure of fractured rock masses or regolith near the ridge of a high and steep slope. Often, these events occur suddenly in the sense that people downslope are caught by surprise. High-locality landslides can be destructive owing to the large momentum and entrainment potential of the moving mass. Increasing attention has been given to these events since the 2008 Wenchuan earthquake. Indeed, slope ridges are particularly susceptible of topographic amplification of the seismic shaking, resulting in mechanical weakening that predisposes them to enhanced weathering and rain-triggered failures years or decades after the mainshock. Risk reduction in such cases must begin with a systematic surveying of the ridges to identify criticalities. Then, quantitative (numerical) approaches must be used to evaluate volumes and describe the movement. Risk assessments can then be produced, and mitigations suggested. Here, we describe the Tonghua landslide in Li County, which is currently in a creeping stage but threatens critical infrastructures and dwellings. We monitored the site for 3 years and identified additional potentially unstable areas before performing physically based modeling and hazard zonation. Our results could be useful in decision-making for risk reduction. [ABSTRACT FROM AUTHOR]

On 25 August 1933, a 7.5-magnitude earthquake struck the eastern margin of the Tibetan Plateau in Sichuan, China. The Diexi earthquake is among the largest known geohazard events worldwide and is frequently cited by those studying the effects of large earthquakes. And yet, the attention focused on this event has failed to deliver a clear picture of landslides and their geomorphic impacts—key attributes of the Diexi earthquake and its aftermath remain obscure and debated. By integrating present-day LiDAR topographic data with existing records (including studies published in Chinese), and a series of unique archival photographs (from 1910, 1920, and 1934), we present the first inventory of coseismic landslides from the epicentral region of this catastrophic 1933 event. We find that the earthquake-triggered landslides were mainly of shallow to deep rock/debris fall/avalanche type, containing mass detached from steep slopes at the top of the mountain ridge source. We reinterpreted three major landslide dams on the Minjiang River based on their geomorphology and sedimentology, and we reconstructed the processes of impoundment and the maximum area of the dammed lake that breached 45 days after the earthquake. Since 1933, we estimate that 43.15–47.68 million m3 of post-earthquake sediment has been delivered to valley floors from the erosion of the three landslide dams, with a sediment yield of up to 131.71 t km−2 year−1. Retrospective studies of the effects of historical earthquakes are challenging, but long-term observations like these are the cornerstone of emerging knowledge of earthquake-induced landsliding and related landscape response. [ABSTRACT FROM AUTHOR]

Enhanced debris flow activity observed after the 2008 Wenchuan earthquake, Sichuan Province, SW China, is still intense more than a decade since the earthquake. A heavy rainstorm on 20 August 2019 caused catastrophic debris flows in the epicentral area of the earthquake. By remote sensing imagery interpretation and field investigation, 13 debris flows were identified. An empirical rainfall threshold I-D model (intensity–duration) of post-seismic debris flows is established. Changing rainfall thresholds, characteristics, and initiation mechanisms of the 2019 debris flows were analyzed, which are different from the events that occurred in previous years. We find that the 2019 debris flows were mainly initiated by concentrated runoff erosion of sediments stored in tributary channels of catchments, with little contribution from the coseismic landslides and new hillslope failures. Differently, the major sediment supply of debris flows in previous years was mainly from the reactivation of coseismic landslide deposits. By analyzing the triggering rainfall of all recorded debris flows after the Wenchuan earthquake, we find that the rainfall threshold in 2019 is about 1/2–4/5 of the pre-earthquake value, and the I-D model we built can predict the 2019 debris flows successfully. Considering that there are still a large number of coseismic landslide deposits in the mountain catchments and the rainfall thresholds are slowly recovering, the post-seismic debris flows may last for a long period. [ABSTRACT FROM AUTHOR]

This work aims to characterize the earthquake-triggered landslides (EQTLs) during the 2017 Jiuzhaigou earthquake and to describe the geological and geomorphological control of landslide hazards. The Jiuzhaigou-EQTLs were studied via field-based investigation and remote sensing techniques during few weeks immediately after the mainshock occurred. The Jiuzhaigou earthquake produced an 11.4-km2 landslide area with 5633 landslides, dominated by shallow disrupted landslides, rock falls, and rockslides/rock avalanche. Statistical analyses show that the Jiuzhaigou-EQTLs had low mobility with a modal apparent friction angle of 40–44°. The Jiuzhaigou-EQTLs mostly occurred at elevations of 2700–3500 m and below 900 m relative relief to the main riverbed, where small-scale landslides were situated at the lower to middle parts of hillslope and large-scale landslides were situated close to the ridge crests. Jiuzhaigou-EQTLs clustered on river valley-side at the upstream of Baihe River (Jiuzhai Paradise and Ganhaizi village), Rize gully, and Danzhu gully on the tectonically active region at the southwest side of seismogenic fault F2. Landslides are prone to failure on the carbonate rocks with anaclinal hillslopes, following the cataclinal and cata-plagioclinal hillslopes. Most Jiuzhaigou-EQTLs preferentially occur on the below low-level break line (L2) in V-shaped valley-side and between first-level (L2) and high-level break line (L1) in U-shaped valley-side formed from long-term paleo-glacier and river incision. We found that landslide frequency is determined by a combination of the earthquake source with ground deformation, slope distribution, and rock strength due to weathering and dissolution along the fractures. Ground surface deformation, rock type (strength), geological structures, and valley-side slope breaks dictated the Jiuzhaigou-EQTL distribution pattern and hazards. This study elucidates the roles that local conditions play in influencing the occurrence, distribution, and frequency of slope failures and provides a better understanding of EQTL phenomenon during the Jiuzhaigou earthquake. [ABSTRACT FROM AUTHOR]