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High-mountain landslides in the hydrogeological environment expose the residents and properties of mountain settlements to immediate risks, especially in the southwestern part of the Tibetan Plateau, where severe damage is frequent. The risk assessment of geohazards is provided as an effective disaster prevention and mitigation management measure. To reasonably predict the spatial distribution characteristics of high-mountain landslide risks, the Gaussian process classification (GPC) and the improved weight-based generalized objective function were applied to quantify the hazard, vulnerability, and risk values of Li County, Southwest China. The findings revealed that the hazard, vulnerability, and risk levels in the studied areas were comparatively high, with percentages in the moderate–high ranges of 41.715%, 40.634%, and 57.135%, respectively. The towns of Zagunao, Ganbu, and Taoping are among the regions with a high grade of risk, with risk values of 0.407, 0.347, and 0.335, respectively. These high-risk distribution sites are concentrated predominantly in the municipalities and their fringes, whereas other prefectures are in the medium–below risk level range. Following the examination of the GPC model and integral appraisal performance, this risk assessment framework exhibits good predictability, with an accurate proportion of predictions in the hazardous location interval reaching 99.857% and an ROC-AUC value of 0.97. The proceedings of this study can provide essential materials and references for decision-making for hydrogeological-environmental hazard policy formulation, territorial spatial planning, and other regional-related risk assessment projects.

Debris flows are a common natural trigger of disasters in mountainous areas, and check dams are standard structural measures for controlling debris flows. Despite their prevalence in debris flow-prone areas worldwide, the capacity of check dams is still calculated using empirical formulas, which lead to large calculation errors. This paper proposes a new method that uses GIS to calculating the design storage capacity of a check dam in the debris flow-prone Cutou Gully in Wenchuan County, China. Large-scale digital surface models derived from unmanned aerial vehicle imagery and ground surveys identify local topographic changes in the debris flow path and develop appropriate maintenance plans for check dams. The measured storage capacity of the check dam is determined by analyzing the DEM differences. This study uses the newly proposed method to calculate the design storage capacity of the check dam. The accuracy of the calculation results was evaluated using the checkpoint method, and the results showed that the design and measured siltation surface errors ranged from -1.16-2.96 m, with a root mean square error of 0.93 m. The design capacity of the check dam is 33.6× 104 m3, and the actual capacity is 36.7× 104 m3, with an absolute error of 3.1× 104 m3 and relative error of 8.6%. The results prove the validity of the proposed calculation method; moreover, this study shows that the new method is accurate, easy to operate, and highly efficient for visualizing the spatial distribution of the siltation depth behind the check dam. This work will help improve future engineering decisions, design strategies, and find optimal design solutions to minimize the risk of debris flow hazards.

Frequent geological disasters such as debris flow and landslides in seismic zones are the primary causes of regional population and infrastructure losses. In this paper, the Contribution Weighting Superposition (CWS) model was used to establish a quantitative vulnerability assessment method of geohazards in the Qiaojia seismic area (SW China). The CWS approach uses three appraisal dimensions: exposure, response, and resilience. A total of 326 historical hazardous locations were selected and combined with ten subfactors—population density (PD), road density (RD), economic density (ED), building density (BD), farmland coverage (FC), ratio of primary industry (RPI), ratio of government revenue (RGR), urbanization rate (UR), GDP per capita (GPC), and disposable income per capita of rural permanent residents (RPR)—for predicting geological hazards vulnerability. Finally, Cohen's kappa coefficient (k) was applied to examine the consistency between the appraisal outcomes and the actual data concerning hazards. Results show that the overall vulnerability in this area is medium and above, with a high risk of widespread damage from potential calamities. PD, GR, and RPR are the three subfactors that most significantly affect the spatial distribution characteristics of vulnerability. The k value associated with the assessed and actual hazard observation data was 0.771 (95% CI 0.771 ± 0.039) with an approximate significance of P