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Background: Earthquakes and landslides pose significant threats to human safety and property, necessitating early warning systems. However, the high construction costs of earthquake early warning systems present a challenge. Purpose: Landslide warnings are more prevalent, so linking them to earthquake warnings could address cost concerns. Hence, it is crucial to validate the feasibility of utilizing GNSS landslide monitoring as assistance for earthquake early warning systems. Methods: This paper analyzes acceleration anomaly data from 31 GNSS landslide monitoring points near the epicenter of the May 2, 2023, MW = 5.2 Baoshan earthquake in Yunnan. The response time was determined as the time difference between an earthquake's occurrence and GNSS's acceleration anomalies. This calculation helps measure the time delay and sensitivity between these two events. Data were obtained from the geological disaster monitoring and early warning management system. Results: GNSS landslide monitoring showed high sensitivity to nearby earthquakes. The fastest response time among the 31 data points was 8 seconds, while the slowest was 56 seconds, all falling within the one-minute mark. A linear correlation was found between acceleration anomaly response time and distance from the epicenter, indicating the feasibility of GNSS landslide monitoring-assisted earthquake monitoring. Conclusion: A proposal is made for a GNSS landslide monitoring cluster to establish a multi-dimensional landslideearthquake disaster warning system. This approach offers new methods for combining earthquake and landslide early warning systems, leveraging existing infrastructure for cost-effectiveness and enhancing disaster preparedness. [ABSTRACT FROM AUTHOR]

The present study investigates the susceptibility of the Kailash Sacred Landscape in Uttarakhand's Pithoragarh district, focusing on regional-level disaster risk assessment in the Gori Ganga watershed as a case study and aims to prepare foundation maps for recurrent disasters like landslides, floods and forest fires. The techniques of remote sensing & geographic information systems, frequency ratio and correlation analytics were employed to perceive hazard-prone areas in the valley, using a variety of geographical, environmental and anthropogenic predictors. The accuracy of the maps generated by the modelling procedure was assessed using validation data and a receiver operating characteristics curve (ROC) with the area under the curve (AUC). The analysis revealed 83.5 km2 was extremely susceptible to flood incidents, 133.1 km2 was susceptible to landslides and 147 km2 was severely vulnerable to wildfires. The determined area under the curve value for flood susceptibility was 88.6%, followed by landslides at 89.5% and wildfires at 90.9%, showing that the model performed exceptionally well. The findings reveal that precipitation, lithology and elevation were the most significant variables in inducing floods. Precipitation, profile curvature, elevation and distance to thrust are the primary determinants of landslides, while closeness to the road and settlements, as well as vegetation indices, were significant predictors of fire events. The Lower Kumaon Himalayan valley region was recognized as highly vulnerable to all three major catastrophes. This study identifies potentially hazardous regions for adaptive sustainable conservation activities, involving local communities in decision-making to mitigate impacts and aid in preventing and managing impending landscape-level threats. [ABSTRACT FROM AUTHOR]

On June 24, 2017, a catastrophic landslide destroyed the village of Xinmo (Maoxian County, Sichuan, China). A 2.87 × 106 m3 rock mass in source area collapsed and entrained the surface soil layer along the run-out path. This disaster took eighty-three people's lives and destroyed more than 103 houses. It is worth noting that rock fragmentation and grinding could expand the spreading area of danger zone in a landslide event. The Xinmo landslide provided a rare opportunity to infer the dynamic fragmentation and grinding of rock masses from the particle size and shape distribution in the entrainment and deposition area. A field investigation combined with an automatic image analysis system was conducted to study the characteristics of particle size and shape distribution along the debris channel. The image analysis of these field data showed that the median size (D50) of particles ranged from 0.41 to 27.71 m in the landslide area. Particle fractal dimension (D) obtained from the Number-size distribution ranged from 1.77 to 2.97 over the entire study area. Moreover, the evolution of D50 and D along the run-out path confirmed that the degree of cumulative rock fragmentation increased as the travel distance increased. Additionally, the particle roundness (R) ranged from 0.51 to 0.88 along the run-out path, which peaked twice during the motion of granular flow, once was in the entrainment area, and another was in the end of the deposition area. Rock scraping occurred in the entrainment area could increase the degree of rock grinding, and reshape coarse stones into smooth particles of large R values (larger roundness of particles could lead to longer spreading distance in a landslide event, due to the lower internal friction among particles). Based on analysis above, the rock scraping phenomena occurred between the source materials and entrainment materials were confirmed to influence the translation and spread of granular flows in landslides. [ABSTRACT FROM AUTHOR]