Search

Due to their practicability, simple and data-driven empirical models have been extensively developed and applied in practical engineering to predict the run-out distance of landslides. However, the definition of the most appropriate empirical model for specific landslide data is rarely discussed. Moreover, the empirical model is subjected to the high variability of landslide data, which should be quantified into the model to provide more reliable predictions. As such, we propose in this paper, a simple, practical, and probabilistic run-out distance prediction method based on an empirical model and Bayesian method. This method was implemented with a regional landslide inventory compiled from 34 loess landslides in Heifangtai Terrace, Gansu Province, China. In this method, we performed a Bayesian model selection to determine the most appropriate empirical model for the compiled database among the possible candidate models adapted from previous literature. Considering the high variability of data, unknown parameters of the empirical model are regarded as random variables, and their posterior distributions are obtained by Bayesian updating with the compiled database. Then, we developed the probabilistic run-out prediction model to evaluate the run-out distance exceedance probability of landslides based on the most appropriate model and its associated posterior random variable information. We utilized data from two recent landslides that occurred in Heifangtai Terrace to validate the performance of the proposed model. In addition, we produced a run-out distance exceedance probability curve using the proposed method for a potential landslide in Heifangtai Terrace, in which the sliding volume interval is estimated using the sloping local base level (SLBL) method. In general, this study presents a practical method for landslide run-out distance analyses within a probabilistic framework, aiming to provide support for risk-based decisions. [ABSTRACT FROM AUTHOR]

Due to the huge losses caused by landslides, landslide deformation monitoring has become essential. The change of the tilting angle of the slope surface can reflect the instability process of the landslide. However, there are few studies on the relationship between the instability process of landslides and the tilting angle of the slope surface on the time scale. Therefore, there are even fewer cases in using the inclination angle of the landslide as an early warning method in practice. On August 24, 2018, a wireless tilt sensor network was deployed in the Heifangtai area of Gansu Province in China, to monitor the inclination state of loess landslides to study the relationship between the tilting angle of the slope surface and the landslide instability process. At 4 o'clock on October 5, 2019, the wireless tilt sensor network mentioned above detected a new loess fall landslide. Through the analysis of the monitoring data from the tilting sensor nodes named T3 and T4, we found that the tilting angle of the landslide surface in the pre-sliding stage has significant change compared with the stable stage, which directly verifies that the inclination data can be used for landslide warning. At the same time, we inputted the monitored inclination data into the inclination warning model proposed by Xie et al. (Landslides 17:301–312, 2020) for verification and found that the trend of inclination changes matches the model well. However, the forecast time error is large. Therefore, this paper optimizes the inclination early warning model proposed by Xie. Using the optimized early warning model, the predicted landslide occurrence time based on the data of the T3 node was 0.517 days, which was about 8 h later than the actual landslide that occurred on October 4, 2019, which is an improvement of forecasting. This case serves as an on-site direct evidence to further verify the feasibility of using the landslide body inclination state as a landslide monitoring and early warning parameter which can be promoted and used in practice. [ABSTRACT FROM AUTHOR]

Loess landslides pose significant physical and environmental threats to communities of the Heifangtai Terrace in Gansu Province, China. Estimating potentially affected areas using dynamic numerical simulations provides a foundation for risk assessment and management. However, current studies often use rheological input parameters derived from the back analysis of an individual historical landslide, which requires that the potential landslide is quite similar to the historical landslide to guarantee the accuracy of predictions. We collected data from 20 loess flowslides that occurred from 2015 to 2019 in the Heifangtai Terrace. A friction model embedded in MassFlow was used to simulate run-out behavior. Accumulation depths at multiple control points were used as observation information. A multi-objective optimization back analysis was used to calibrate optimal rheological parameters for each flowslide, yielding a region-specific database of 20 datasets for statistical analyses. Finally, two flowslides that occurred in 2019 were employed as potential landslides to conduct probabilistic run-out predictions based on the database. These predictions agreed well with the observed accumulation areas in both cases, thus validating our method. This database will be useful for future landslide run-out predictions in this area. [ABSTRACT FROM AUTHOR]

On October 5, 2021, a small-scale catastrophic loess landslide occurred in Niangniangba town, Tianshui City, Gansu Province in China. The landslide with a volume of approximately 15,360 m3 buried 2 houses and damaged another 2 houses. On the basis of field investigation, unmanned aerial vehicle photography, and numerical simulation, the characteristics and dynamic processes of the Zhongzhai landslide are analyzed, and the potential destructive zone of the unstable loess mass is preliminarily assessed. The Zhongzhai landslide is a typical rainfall–reactivated flow-type loess landslide, and its failure mode is creep-tensile type. The upper sliding mass is the typical retrogressive failure, and the lower sliding mass shows thrust load caused failure. The runout simulation results show that the entire sliding process of Zhongzhai landslide lasted for approximately 35 s, with a maximum velocity of 12 m/s. The maximum travel distance of the sliding mass is 130 m. In addition, the presence of masonry buildings has a great influence on the sliding distance. Simulation results of potential destructive zone of the unstable loess mass show that the travel distance of the unstable loess mass is approximately 250–295 m, the thickness of the deposited mass is approximately 2.7–3.3 m in the potential destructive zone, and the maximum velocity is 28 m/s. This work contributes to improving the understanding of evolution law, dynamic processes, and risk prevention of this kind of landslide in loess plateau areas. [ABSTRACT FROM AUTHOR]

On January 27, 2021, a small-scale loess landslide occurred in Dangchuan Village, Yongjing County, Gansu Province, China. Timely monitoring and warning helped to prevent any casualties. The sliding mass of the Dangchuan landslide fell in two stages with an interval of 24 s and lasted for a total of 31 s. Eventually, the Dangchuan landslide had a descent height of approximately 120 m and a sliding portion stretching approximately 350 m. The landslide volume was approximately 3 × 104 m3. Based on the investigation of different stages of the Dangchuan landslide and the characteristics of the deposition area, the induction of this landslide was attributed to the effect of frozen stagnant water. Following field investigation of the landslide source and crack areas, a potential sliding mass model with a volume of 5 × 104 m3 was obtained via calculation. To predict the disaster scope of potential landslides, we first used DAN3D software to invert this Dangchuan landslide. Then, based on the numerical model parameters obtained from the inversion, the sliding accumulation process of the potential slide mass was simulated. The results showed that the maximum disaster scope of the potential slide mass would be larger than that of the Dangchuan landslide. The new deposit would cover an area of approximately 3.5 × 104 m2, with a thickness in the range of 0–6 m. The new deposition area would expand by approximately 50 m in the long-axis direction compared to the Dangchuan landslide, impacting the houses and roads. The DAN3D prediction results of the disaster scope will help residents in the potential danger zone evacuate in advance, reducing unnecessary damage. [ABSTRACT FROM AUTHOR]

Loess landslides are a severe engineering geological problem on the Loess Plateau of China. This study relies on interferometric synthetic aperture radar (InSAR) and unmanned aerial vehicle (UAV) photogrammetry techniques to characterise a large-scale loess landslide deformation process in Northwest China. First, a total of 85 Sentinel-1 SAR images acquired from March 2015 to March 2019 were used to detect the spatial displacement and characterise the pattern of deformation from presliding to postsliding. Second, three UAV flight surveys were conducted to reconstruct landslide morphology, identify scarps, cracks and fissures; and generate elevation differences. By comparing these two technologies, we found that InSAR has more advantages in the retrieval of creeping deformation, while UAV data is valuable for detecting large sudden slides. Dynamic deformation zonation maps were generated based on comprehensive analysis, and the deformation pattern of the large loess landslide was deduced. The Hongheyan landslide was induced by heavy rainfall and then gradually became suspended owing to the topography effect. Recently, the western part of the slope showed a clear accelerating trend. This evolution provides comprehensive kinematic information for local governments to make further intensive observations or take effective precautions. [ABSTRACT FROM AUTHOR]