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The Bailongjiang River Basin is a high-risk area for debris flow in China. On 17 August 2020, a debris flow occurred in the Shuimo catchment, Wen County, which blocked the Baishui River, forming a barrier lake and causing significant casualties and property damage. In this study, remote sensing, InSAR, field surveys, and unmanned aerial vehicle (UAV) techniques were used to analyze the causal characteristics, material source characteristics, dynamic processes, and disaster characteristics after the debris flow. The results showed that the Shuimo catchment belongs to low-frequency debris flows, with a recurrence cycle of more than 100 years and concealed features. High vegetation coverage (72%) and a long main channel (11.49 km) increase the rainfall-triggering conditions for debris flow occurrence, making it more hidden and less noticed. The Shuimo catchment has a large drainage area of 31.26 km2, 15 tributaries, significant elevation differences of 2017 m, and favorable hydraulic conditions for debris flow. The main sources of debris flow material supply are channel erosion and slope erosion, which account for 84.4% of the total material. The collapse of landslides blocking both sides of the main channel resulted in an amplification of the debris flow scale, leading to the blockage of the Baishui River. The scale of the accumulation fan is 28 × 104 m3, and the barrier lake area is 37.4 × 104 m2. The formation mechanism can be summarized as follows: rainfall triggering → shallow landslides → slope debris flow → channel erosion → landslide damming → dam failure and increased discharge → deposition and river blockage. The results of this study provide references for remote sensing emergency investigation and analysis of similar low-frequency and concealed debris flows, as well as a scientific basis for local disaster prevention and reduction. [ABSTRACT FROM AUTHOR]

The Bailong River Basin is one of the most developed regions for debris flow disasters worldwide, often causing severe secondary disasters by blocking rivers. Therefore, the early identification of potential debris flow disasters that may block the river in this region is of great significance for disaster risk prevention and reduction. However, it is quite challenging to identify potential debris flow disasters that may block rivers at a regional scale, as conducting numerical simulations for each debris flow catchment would require significant time and financial resources. The purpose of this article is to use public resource data and machine learning methods to establish a relationship model between debris flow-induced river blockage and key influencing factors, thereby economically predicting potential areas at risk for debris flow-induced river blockage disasters. Based on the field investigation, data collection, and remote sensing interpretation, this study selected 12 parameters, including the basin area, basin height difference, relief ratio, circularity ratio, landslide density, fault density, lithology index, annual average frequency of daily rainfall exceeding 40 mm, river width, river discharge, river gradient, and confluence angle, as critical factors to determine whether debris flows will cause river blockages. A relationship model between debris flow-induced river blockage and influencing factors was constructed based on machine learning algorithms. Several machine learning algorithms were compared, and the XGB model performed the best, with a prediction accuracy of 0.881 and an area under the ROC curve of 0.926. This study found that the river width is the determining factor for debris flow blocking rivers, followed by the annual average frequency of daily rainfall exceeding 40 mm, basin height difference, circularity ratio, basin area, and river discharge. The early identification method proposed in this study for river blockage disasters caused by debris flows can provide a reference for the quantitative assessment and pre-disaster prevention of debris flow-induced river blockage chain risks in similar high-mountain gorge areas. [ABSTRACT FROM AUTHOR]

Due to the heavy rainfall, from February 26th to 28th 2021, the Moli landslide in Guoye Town, Zhouqu County, experienced creeping deformation with the development of slope cracks and obnious signs of deformation. A total of of 92 households with 402 people were affected, causing a direct economic loss of approximately 14.463 million yuan. This study focuses on the Moli landslide in Guoye Town, where the geological environment and physical and mechanical properties of the soil were deeply understood through remote sensing interpretation, aerial photography, and geological investigation. The cause of the landslide formation was analyzed in detail, and the stability of the landslide was calculated theoretically and analyzed for displacement monitoring. Based on field investigation and drilling analysis, the Massflow numerical model was used to simulate and predict the Moli landslide in Guoye Town, Zhouqu County. The range and thickness of the source area were determined to predict the accumulation process of the landslide and the risk of river blocking. The height of the river blocking and the harm caused by the landslide to the upstream and downstream regions were predicted. The results showed that: (1) Moli landslide is a super-large deep landslide with a long tongue shape, clear shape and deformation, and composed of broken phyllite and broken stone soil. The average depth of the slide body is 40m, and the total volume is 21.2 million stere. (2) The main factors of landslide formation are unfavorable topographic conditions, softening of rock and soil, strong tectonic movement, rainfall infiltration, and front river erosion. (3) The landslide has the risk of destroying the landslide houses, roads, and blocking the river. It is suggested that the residents threatened by the Moli landslide should take measures to avoid danger and relocate as soon as possible. This study can provide reference for the formation mechanism and emergency prevention and control of similar landslide geological disaster chains. [ABSTRACT FROM AUTHOR]