The area comprising the Langma-Baiya fault zone (LBFZ) and the Bailongjiang fault zone (BFZ) in the Western Qinling Mountains in China is characterized by intensive, frequent, multi-type landslide disasters. The spatial distribution of landslides is affected by factors, such as geological structure, landforms, climate and human activities, and the distribution of landslides in turn affects the geomorphology, ecological environment and human activities. Here, we present the results of a detailed landslide inventory of the area, which recorded a total of 2765 landslides. The landslides are divided into three categories according to relative age, area, and type of movement. Sixteen factors related to geological structure, geomorphology, materials composition and human activities were selected and four machine learning algorithms were used to model the spatial distribution of landslides. The aim was to quantitatively evaluate the relationship between the spatial distribution of landslides and the contributing factors. Based on a comparison of model accuracy and the Receiver Operating Characteristic (ROC) curve, RandomForest (RF) (accuracy of 92%, area under the ROC of 0.97) and GradientBoosting (GB) (accuracy of 96%, area under the ROC curve of 0.97) were selected to predict the spatial distribution of unclassified landslides and classified landslides, respectively. The evaluation results reveal the following. The vegetation coverage index (NDVI) (correlation of 0.2, and the same below) and distance to road (DTR) (0.13) had the highest correlations with the distribution of unclassified landslides. NDVI (0.18) and the annual precipitation index (API) (0.14) had the highest correlations with the distribution of landslides of different ages. API (0.16), average slope (AS) (0.14) and NDVI (0.1) had the highest correlations with the landslide distribution on different scales. API (0.28) had the highest correlation with the landslide distribution based on different types of landslide movement.

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.

Debris flow can cause damage only when its discharge exceeds the drainage capacity of the prevention engineering. At present, most rainfall thresholds for debris flows mainly focus on the initiation of debris flow and do not adequately consider the magnitude and drainage measures of debris flows. These thresholds are likely to initiate numerous warnings that may not be related to hazardous processes. This study proposes a method for calculating the rainfall threshold that is related to a defined level of debris flow magnitude, over which certain damage may be caused. This method is constructed by using the transient rainfall infiltration analysis slope stability model (TRIGRS) and the fluid dynamics process simulation model (MassFlow). We first use the TRIGRS model to analyze slope stability in the study area and obtain the distribution of unstable slopes under different rainfall conditions. Afterward, the MassFlow model is employed to simulate the movement process of unstable slope units and to predict the depositional processes at the mouth of the catchment. Lastly a rainfall threshold is constructed by statistically analyzing the rainfall conditions that cause debris flows flushing out of the given drainage ditch. This method is useful to predict debris flow events of a hazardous magnitude, especially for areas with limited historical observational data.