On 10th Oct. and 3rd Nov. 2018, two successive landslides occurred in the Jinsha River catchment at Baige Village, Tibet Autonomous Region, China. The landslides blocked the major river and formed the barrier lake, which finally caused the huge flood disaster loss. The hillslope at Baige landslide site has been still deforming after the 2018 slidings, which is likely to fail and block the Jinsha River again in the future. Therefore the investigation of 2018 flood disaster at the Baige landslide is of a great significance to provide a classic case for flood assessment and early warning for the future disaster. The detailed survey revealed that the outstanding inundations induced bank collapse disasters upstream the Baige landslide dams, and the field investigations and hydrological simulation suggested that the downstream of the Baige landslide were seriously flooded due to the two periods of the outburst floods. On these bases, the early warning process of potential outburst floods at the Baige landslide was advised, which contains four stages: Outburst Flood Simulating Stage, Outburst Flood Forecasting Stage, Emergency Plan and Emergency Evacuation Stage. The study offers a conceptual model for the mitigation of landslides and flood disasters in the high-relief mountainous region in Tibet.

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.

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.

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.

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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 they leading to secondary impacts, is 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 30m, 35m, and 51m. The potential storage capacity / back-flow reaches 142x106m3/32km, 182x106m3/40km, and 331x106m3/50km in the valley upstream. Failure of these three dams leads to maximum inundation in the downstream of study reach of 1.674 km2, 1.833 km2, and 1.927 km2. These results demonstrate the serious secondary hazard associated with this region.

<p>Giant rock avalanche is extremely rare worldwide, while giant rock avalanche developed in suture zone has presented unique development characteristics. The suture zone is a product of plate moving and strong tectonic activity, where the appearance of a giant avalanche not only plays a barometer role for the regional disaster development environment but an indicator role for the complicated geological environment. In 2019-2021, the author has found a giant paleo-rock avalanche (name Basu avalanche) in the Bangonghu-Nujiang suture zone of the Tibetan Plateau. Some infrequent characteristics such as huge volume, development in nappe structure, and hyper-mobility (debris impact height > 600m) appeared for this giant rock avalanche. In this paper, based on the detailed investigation, <sup>36</sup>Cl dating, and reconstructing the pre-avalanche terrain methods, the development, failure, and hyper-mobility of this giant rock avalanche have been analyzed. The result shows that: (1) The volume of the Basu avalanche is about 3.5×10<sup>9</sup>m<sup>3</sup>, the residue is about 1.4×10<sup>9</sup>m<sup>3</sup><sup> </sup>now. The avalanche occurred at 205.70±7.71ka B.P.(ka: millennium), subsequent the accumulation body occurred two times secondary landslides (name Duolasi landslide) at 17.57±0.72ka B.P. and 7.01±0.32ka B.P., respectively; (2) The nappe structure, formed from the uplift and orogeny process of the suture zone, controls the development and volume size of the Basu avalanche, while the strong earthquake is the biggest likely to trigger the avalanche finally failure because of the dense active faults distribution; (3) Because of the rich Ultrabasic clasts derived from the F2 fault and fine particles produced by cataclastic rock mass, the Basu avalanche formed the slide belt that thickness from centimeters to meters during the motion. The lubrication effect of the slide belt has dominated the avalanche debris's high-speed motion and hyper-mobility, the mechanism is that: due to the huge avalanche volume and induced the high pressure and closed slide belt environment, the slide belt fine-grain formed the lubrication layer with certain water involved, and the friction force sharply decreased; (4) Because of the Basu rock avalanche and the debris flow successive blocked the Leng River, the Leng River valley has experienced diversions process and the river valley from the ’S’ shape to approximate straight-line shape.</p>