Debris flows are natural disasters that frequently occur in mountainous areas, usually accompanied by serious loss of lives and properties. One of the most commonly used approaches to mitigate the risk associated with debris flows is the implementation of early warning systems based on well-calibrated rainfall thresholds. However, many mountainous areas have little data regarding rainfall and hazards, especially in debris-flow-forming regions. Therefore, the traditional statistical analysis method that determines the empirical relationship between rainstorms and debris flow events cannot be effectively used to calculate reliable rainfall thresholds in these areas. After the severe Wenchuan earthquake, there were plenty of deposits deposited in the gullies, which resulted in several debris flow events. The triggering rainfall threshold has decreased obviously. To get a reliable and accurate rainfall threshold and improve the accuracy of debris flow early warning, this paper developed a quantitative method, which is suitable for debris flow triggering mechanisms in meizoseismal areas, to identify rainfall threshold for debris flow early warning in areas with a scarcity of data based on the initiation mechanism of hydraulic-driven debris flow. First, we studied the characteristics of the study area, including meteorology, hydrology, topography and physical characteristics of the loose solid materials. Then, the rainfall threshold was calculated by the initiation mechanism of the hydraulic debris flow. The comparison with other models and with alternate configurations demonstrates that the proposed rainfall threshold curve is a function of the antecedent precipitation index (API) and 1 h rainfall. To test the proposed method, we selected the Guojuanyan gully, a typical debris flow valley that during the 2008–2013 period experienced several debris flow events, located in the meizoseismal areas of the Wenchuan earthquake, as a case study. The comparison with other threshold models and configurations shows that the selected approach is the most promising starting point for further studies on debris flow early warning systems in areas with a scarcity of data.

The aim of this study was to develop a regional landslide stability analysis method considering the combined impact of rainfall and the roots of vegetation in densely vegetated areas. A typical mountainous watershed in the Nanling National Nature Reserve of South China was chosen as the study area. First, the unmanned aerial vehicle (UAV) method was used to obtain surface element information including topography, vegetation, and landslides. Five main plant species were identified. The RipRoot model was then used to calculate the additional cohesion of these five plant species, and the relationship between the root systems of the different plant species and the soil shear strength was subsequently revealed. Finally, the root cohesion was introduced into the stability index mapping model (SINMAP), and the receiver operating characteristic curve (ROC) method was used to calculate the accuracy of slope stability when considering only soil cohesion as well as the composite cohesion of both soil and roots. The results showed significant differences in the root cohesion of different plants in the study area and a significant increase in the calculation accuracy (from 90% to 95.6%) when root cohesion was considered in the landslide stability calculation. These study results not only enrich theoretical studies on the impact of vegetation roots on landslide stability but also provide a scientific support for preventing disasters in mountainous landslide‐prone areas.

Because of the physical character of soil from granite weathering and the typhoon rains in South China, the model for early warnings of existing shallow landslides cannot be well applied in that area. This study developed a new shallow landslide model based on the effect of soil macropores for determining the rainfall threshold to post an early warning of the possibility of a shallow landslide in South China. We studied the hydrological effects of macropore flow and proposed a mechanistic model of the formation of shallow landslides by introducing the macropore coefficient of granite residual soil. The rainfall threshold of each slope unit was calculated by combining the hydrological model with the proposed shallow landslide model. Lastly, we tested the calculated result in the Maguihe watershed, Guangdong province, South China, which experienced a group of massive shallow landslides on 21 September 2010. The study results showed that the macropores of vegetation roots had a significant effect by increasing the permeability of granite residual soil. Coniferous forest land has the highest initial infiltration rate and stable infiltration rate, followed by shrub forest land, and then bare land. Statistical verification showed that the accurate prediction rate of the proposed model was 80.65%, which is adequate for early warning of shallow landslides in South China. We discuss the application conditions and parameter calibrations of the proposed model, and offer recommendations for future research.

Landslides triggered by extreme precipitation events pose a significant threat to human life and property in mountainous regions. Therefore, accurate identification of landslide locations is crucial for effective prevention and mitigation strategies. During the prolonged heavy rainfall events in Guangdong Province between 21 May and 21 June 2022, shallow and clustered landslides occurred in the mountainous regions of the Beijiang River Basin. This research used high-resolution satellite imagery and integrated the Mask R-CNN algorithm model with spectral, textural, morphological and physical characteristics of landslides in remote sensing imagery, in addition to landslide-influencing factors and other constraints, to interpret the landslides induced by the event through remote sensing techniques. The detection results show that the proposed methodology achieved a high level of accuracy in landslide identification, with a precision rate of 81.91%, a recall rate of 84.07% and an overall accuracy of 87.28%. A total of 3782 shallow landslides were detected, showing a distinct clustered distribution pattern. The performance of Mask R-CNN, Faster-CNN, U-Net and YOLOv3 models in landslide identification was further compared, and the effects of setting the rotation angle and constraints on the identification results of the Mask R-CNN algorithm model were investigated. The results show that each model improves the evaluation indices, but the Mask R-CNN model has the best detection performance; the rotation angle can effectively improve the generalization ability and robustness of the model, and the landslide-inducing factor data and texture feature sample data are the best for landslide identification. The research results provide valuable references and technical support for deepening our understanding of the distribution patterns of rainfall-triggered shallow and cluster landslides in the Beijiang River Basin.