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Accurate dynamic modeling of landslides could help understand the movement mechanisms and guide disaster mitigation and prevention. Discontinuous deformation analysis (DDA) is an effective approach for investigating landslides. However, DDA fails to accurately capture the degradation in shear strength of rock joints commonly observed in high-speed landslides. In this study, DDA is modified by incorporating simplified joint shear strength degradation. Based on the modified DDA, the kinematics of the Baige landslide that occurred along the Jinsha River in China on 10 October 2018 are reproduced. The violent starting velocity of the landslide is considered explicitly. Three cases with different violent starting velocities are investigated to show their effect on the landslide movement process. Subsequently, the landslide movement process and the final accumulation characteristics are analyzed from multiple perspectives. The results show that the violent starting velocity affects the landslide motion characteristics, which is found to be about 4 m/s in the Baige landslide. The movement process of the Baige landslide involves four stages: initiation, high-speed sliding, impact-climbing, low-speed motion and accumulation. The accumulation states of sliding masses in different zones are different, which essentially corresponds to reality. The research results suggest that the modified DDA is applicable to similar high-level rock landslides. [ABSTRACT FROM AUTHOR]

The reactivation of coüuviai Landslides in reservoir banks poses a serious threat to the safety of hydropower projects and nearby towns. This study aims to research the morphological evolution of this type of landslides under the action of reservoir water and the impact of morphological evolution on landslide stability. The study focused on the Yulinerzu landslide, a large reactivated colluvial landslide in the Xiluodu Reservoir, China. Field surveys were conducted to analyze the geological structure of the landslide. In situ monitoring and surveys were used to obtain the deformation characteristics and morphological evolution of the landslide. A combined seepage-slope stability analysis was conducted to reveal the deformation mechanism. The results show that the reactivation of the Yulinerzu landslide is dominated by reservoir water fluctuations rather than rainfall. The underlying geological condition of the colluvial landslide is its hydrogeological structure, which causes the landslide to deform in a step-like manner during reservoir operation. With the accumulation of displacement and morphology evolution, the landslide displayed self-stabilizing characteristics. Therefore, in the stability analysis and risk assessment of large deformation landslides, it is essential to take into account not only the hydraulic effects of reservoir fluctuation but also the evolution of landslide morphology. [ABSTRACT FROM AUTHOR]

Outburst floods triggered by breaching of landslide dams may cause severe loss of life and property downstream. Accurate identification and assessment of such floods, especially when leading to secondary impacts, are critical. In 2018, the Baige landslide in the Tibetan Plateau twice blocked the Jinsha River, eventually resulting in a severe outburst flood. The Baige landslide remains active, and it is possible that a breach happens again. Based on numerical simulation using a hydrodynamic model, remote sensing, and field investigation, we reproduce the outburst flood process and assess the hazard associated with future floods. The results show that the hydrodynamic model could accurately simulate the outburst flood process, with overall accuracy and Kappa accuracy for the flood extent of 0.956 and 0.911. Three future dam break scenarios were considered with landslide dams of heights 30 m, 35 m, and 51 m. The potential storage capacity and length of upstream flow back up in the upstream valley for these heights were 142 × 106m3/32 km, 182 × 106m3/40 km, and 331 × 106m3/50 km. Failure of these three dams leads to maximum inundation extents of 0.18 km2, 0.34 km2, and 0.43 km2, which is significant out-of-bank flow and serious infrastructure impacts. These results demonstrate the seriousness of secondary hazards associated with this region. [ABSTRACT FROM AUTHOR]

Slope units (SUs) are sub-watersheds bounded by ridge and valley lines. A slope unit reflects the physical relationship between landslides and geomorphological features and is especially useful for landslide sensitivity modeling. There have been significant algorithmic advances in the automatic delineation of SUs. But the intrinsic difficulties of determining input parameters and correcting for unreasonable SUs have hindered their wide application. An improved method of the evaluation and local multi-scale optimization for the automatic extraction of SUs is proposed. The Sus' groups more consistent with the topographic features were achieved through a stepwise approach from a global optimum to a local refining. First, the preliminary subdivisions of multiple SUs were obtained based on the r.slopeunit software. The optimal subdivision scale was obtained by a collaborative evaluation approach capable of simultaneously measuring objective minimum discrepancies and seeking a global optimum. Second, under the selected optimal scale, unreasonable SUs such as over-subdivided slope units (OSSUs) and under-subdivided slope units (USSUs) were further distinguished. The local average similarity (LS) metric for each SU was designed based on calculating the SU's area, common boundary and neighborhood variability. The inflection points of the cumulative frequency curve of LS were calculated as the distinguishing intervals for those unrealistic SUs by maximum interclass variance threshold. Third, a new effective optimization mechanism containing the re-subdivision of USSUs and merging of OSSUs was put into effect. We thus obtained SUs composed of terrain subdivisions with multiple scales, which is currently one of the few available methods for non-single scales. The statistical distributions of density, size and shapes demonstrate the excellent performance of the refined SUs in capturing the variability of complex terrains. Benefiting from the sufficient integrating approach of diverse features for each object, it is a significant advantage that the processing object can be transferred from general entirety to each precise individual. [ABSTRACT FROM AUTHOR]

On Oct. 10 and Nov. 3, 2018, two landslides, i.e., the "10.10" and "11.3" rockslides, occurred successively at Baige Village, Jiangda County, Tibetan Autonomous Region, P. R. China. The two landslides are located in the upper reaches of the Jinsha River and both dammed the river. Immediately since the slides, the authors have been working on the slides and help disaster reduction. Based on the data collected by April 2020, this paper is aimed at clarifying the geological condition of the slides and at explaining why the slides occurred and what the whole sliding process was. Conclusions are summarized as follows. First, the two landslides occurred in the suture belt of the Jinsha River and the rocks are composed of tectonic mélange slices of mainly gneiss intermingled with carboniferous slate and marble and with intruded serpentine and granite porphyry. The gneiss generally bears a schistosity plane with an averaged attitude of N47° W/47°, dipping into the slope. Secondly, long-term geomorphological evolution of the bank slope due to river incision contributed to the progressive slope deformation for the development of the "10.10" rockslide. No preferential joints exist in the slope, but alteration and weathering played important roles in its occurrences. Rainfall and earthquakes may also accelerate its deformation. Thirdly, the "10.10" rockslide is of high-speed wedge-like slope failure with a high-position and a high-shear outlet. Its sliding and deposition process demonstrate special features as initial speed, collision between debris, surging waterjet, and second slipping. Fourthly, the whole process of the "10.10" rockslide can be divided into 6 steps, i.e., startup of the major sliding and sliding resistance zones, sliding initiation of the trailing zone, formation of debris-eroded zones, collision of debris and triggering waterjet and mist, secondary slip of the landslide dam, and surface flush in the deposition area. The estimated speed may reach as high as 67 m/s. Fifthly, the "11.3" rockslide follows a different mode, i.e., wedge cleaving effect. And finally, the cracked zones still have the risk to constitute a potential landslide and to dam the river again. [ABSTRACT FROM AUTHOR]

Outburst floods resulting from giant landslide dams can cause devastating damage to hundreds or thousands of kilometres of a river. Accurate and timely delineation of flood inundated areas is essential for disaster assessment and mitigation. There have been significant advances in flood mapping using remote sensing images in recent years, but little attention has been devoted to outburst flood mapping. The short-duration nature of these events and observation constraints from cloud cover have significantly challenged outburst flood mapping. This study used the outburst flood of the Baige landslide dam on the Jinsha River on 3 November 2018 as an example to propose a new flood mapping method that combines optical images from Sentinel-2, synthetic aperture radar (SAR) images from Sentinel-1 and a Digital Elevation Model (DEM). First, in the cloud-free region, a comparison of four spectral indexes calculated from time series of Sentinel-2 images indicated that the normalized difference vegetation index (NDVI) with the threshold of 0.15 provided the best separation flooded area. Subsequently, in the cloud-covered region, an analysis of dual-polarization RGB false color composites images and backscattering coefficient differences of Sentinel-1 SAR data were found an apparent response to ground roughness's changes caused by the flood. We carried out the flood range prediction model based on the random forest algorithm. Training samples consisted of 13 feature vectors obtained from the Hue-Saturation-Value color space, backscattering coefficient differences/ratio, DEM data, and a label set from the flood range prepared from Sentinel-2 images. Finally, a field investigation and confusion matrix tested the prediction accuracy of the end-of-flood map. The overall accuracy and Kappa coefficient were 92.3%, 0.89 respectively. The full extent of the outburst floods was successfully obtained within five days of its occurrence. The multi-source data merging framework and the massive sample preparation method with SAR images proposed in this paper, provide a practical demonstration for similar machine learning applications using remote sensing. [ABSTRACT FROM AUTHOR]