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Abstract The occurrence of debris flow events in small-scale watersheds with dense vegetation in mountainous areas that result in significant loss of life and missing individuals challenges our understanding and expertise in investigating and preventing these disasters. This has raised concerns about the occurrence of large debris flow disasters from small watersheds. This study focused on a catastrophic debris flow that took place in Longtou Gully (0.45 km2) in Tianquan County, Ya’an City on 25 September 2021, which resulted in 14 deaths and missing individuals. Through comprehensive field investigations, high-precision remote sensing data analyses, and numerical simulations, we analyzed the triggering mechanisms and dynamic processes of this event. Our results indicate that the convergence hollow at the channel head exhibited higher hydraulic conditions during rainfall compared to gentle slopes and convex terrains, leading to the instability of colluvial soil due to the expansion of the saturated zone near the soil–bedrock interface. The entrainment of material eroded from the channel resulted in an approximately 4.7 times increase in volume, and the channel scarp with a height of about 200 m amplified the destructive power of the debris flow. We emphasize the need to take seriously the possibility of catastrophic debris flows in small-scale watersheds, with colluvial deposits in hollows at the channel head under vegetation cover that serve as precursor material sources, and the presence of channel scarps formed by changes in the incision rate of the main river, which is common in the small watershed on both sides. This study provides insights for risk assessment of debris flows in small-scale catchments with dense vegetation cover in mountainous areas, highlighting the importance of vigilance in addressing disasters in small-scale catchments, particularly in regions with increasing human–environment conflicts.

ObjectiveExplore the application effects of “Internet +” continuous emotional management training in depression patients and analyze the feasibility of innovative rehabilitation training methods.MethodsConveniently selected 100 discharged patients meeting the diagnostic criteria for depression from the Second Affiliated Hospital of Xinxiang Medical University between August 2022 and January 2023 as study subjects. They were divided into an intervention group and a control group according to their discharge time, with 50 patients in each group. The control group received routine monthly telephone follow-ups and “Internet +” health popularization. The intervention group received “Internet +” continuous emotional management training in addition to the control group’s protocol. Before and after the intervention, the Hamilton Depression Rating Scale (HAMD), the Chinese version of the Cognitive Emotion Regulation Questionnaire (CERQ-C), and the Social Adaptation Function Evaluation Scale (SAFE) were used to compare the differences in depression relief, cognitive emotion regulation levels, and social functions between the two groups.ResultsTwo patients in the control group and three patients in the intervention group were lost to follow-up during the intervention, Ultimately, 48 patients in the control group and 47 patients in the intervention group completed the study. Before the intervention, there were no statistically significant differences between the two groups in HAMD scores, CERQ-C subscale scores, and SAFE scores (P < 0.05). After the intervention, the intervention group had significantly lower HAMD and SAFE scores compared to the control group (P < 0.05). In the CERQ-C questionnaire, the intervention group scored lower in self-blame, rumination, catastrophizing, and blaming others but higher in acceptance, positive refocusing, refocusing on planning, positive reappraisal, and putting into perspective compared to the control group, with statistically significant differences (P < 0.05).Conclusion“Internet +” continuity emotion management training can reduce the severity of depression in post-discharge patients and enhance their emotional management and social adaptability. This approach is feasible in clinical practice.

The Yili Valley, located on the border between China and Kazakhstan, serves as the juncture of North and South Xinjiang, and stands as a pivotal outpost on the Silk Road Economic Belt. This area possesses a fragile ecological environment and experiences frequent debris flow disasters. In this study, four machine learning models:Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Decision Tree (DT)-- were employed to evaluate the debris flow susceptibility and compute the weights of evaluation factors. The models were fed inputs comprising 398 identified debris flow channels and 14 feature parameters such as fault density, topographic relief, land use, NDVI, multi-year average rainfall, etc obtained through remote sensing and field surveys. Also, the accuracy of the four machine learning models was evaluated by ROC curves and calculating the Area Under the Curve (AUC). The research results show that: (1) High debris flow susceptibility areas are mainly located in the Tianshan Mountains in the deep river valley region and the loess-covered areas in the mountain front slopes; (2) Multi-year average rainfall, drought index, and topographic relief variability are the top three influential factors controlling the spatial development of debris flows; (3) The AUC values for the validation datasets of the four models were 0.938 (RF), 0.932 (SVM), 0.89 (LR), 0.79 (DT), with the Random Forest model exhibiting superior predictive capability in assessing susceptibility in the region; (4) The disruption of ecological vegetation in the loess-covered region of the study area is a significant cause of frequent debris flow occurrences. Ecological governance and protection efforts should be emplasized to reduce soil erosion and effectively mitigate debris flow disasters at their source.

The Tien Shan Mountain range connects Central Asia with northwestern China and is a crucial transport junction between East and West Asia. It is a common location for regional debris flows, which pose a significant risk to ecological security and the safety of people and property. Nevertheless, limited knowledge exists about the distribution of disaster risks and the impacted populations. This study uses advanced machine learning techniques to identify the key natural and social factors influencing these hazards and incorporates the Social Vulnerability Index (SoVI) to assess societal vulnerability. The outcomes demonstrate that (1) the debris flow hazard in the Tien Shan Mountain area is primarily governed by the geological structure, which dictates the material source and, in turn, dictates the onset of debris flows. (2) The vulnerability demonstrates a high spatial tendency in the north and a low one in the south, with evident spatial clustering characteristics. (3) A total of 19.13% of the study area is classified as high-hazard, with specific distribution zones including the northern foothills of the Tien Shan Mountains, the low-mountain zones of the southern foothills of the Tien Shan Mountains, and the Yili Valley zone. This holistic approach offers valuable insights into the spatial distribution of risks, aiding in prioritizing disaster preparedness and mitigation efforts. Also, our findings and conclusions are beneficial for local decision makers to allocate resources effectively and promote sustainable development practices in the region.