Avalanches occur frequently in mountainous areas and pose significant threats to roads and infrastructure. Clarifying how terrain conditions influence avalanche initiation and movement is critical to improving hazard assessment and response strategies. This study focused on a wet-snow slab avalanche that occurred on 26 March 2024, in the Simutas region of the northern Tianshan Mountains, Xinjiang, China. The authors combined remote sensing imagery, high-resolution meteorological station observations, field investigations, and numerical simulations (RAMMS::Avalanche) to analyze the avalanche initiation mechanism, dynamic behavior, and path recurrence characteristics. Results indicated that persistent heavy snowfall, rapid warming, and substantial daily temperature fluctuations triggered this avalanche. The predominant southeasterly (SE) winds and the northwest-facing (NW) shaded slopes created favorable leeward snow deposition conditions, increasing snowpack instability. High-resolution meteorological observations provided detailed wind, temperature, and precipitation data near the avalanche release zone, clearly capturing snowpack evolution and meteorological conditions before avalanche initiation. Numerical simulations showed a maximum avalanche flow velocity of 19.22 m/s, maximum flow depth of 12.42 m, and peak dynamic pressure of 129.3 kPa. The simulated avalanche deposition area and depth closely matched field observations. Multi-temporal remote sensing images indicated that avalanche paths in this area remained spatially consistent over time, with recurrence intervals of approximately 2–3 years. The findings highlight the combined role of local meteorological processes and terrain factors in controlling avalanche initiation and dynamics. This research confirmed the effectiveness of integrating remote sensing data, high-resolution meteorological observations, and dynamic modeling, providing scientific evidence for avalanche risk assessment and disaster mitigation in mountain regions.

Avalanche susceptibility mapping is vital for disaster prevention and infrastructure safety in cold mountain regions under climate change. Traditional machine learning (ML) approaches have demonstrated strong predictive capacity, yet their limited interpretability and difficulty in identifying threshold effects hinder their broader application in geohazard risk management. To overcome these limitations, this study develops an explainable ML framework that integrates remote sensing data, topographic and climatic variables, and SHapley Additive exPlanations for the Kanas Scenic Area transportation corridor in the Chinese Altay Mountains. The framework evaluates five classifiers: Random Forest, XGBoost, LightGBM, Soft Voting, and Stacking, and using sixteen conditioning factors that capture topography, climate, vegetation, and anthropogenic influences. Results show that LightGBM achieved the best performance, with an AUC of 0.9428, accuracy of 0.8681, F1-score of 0.8750, and Cohen’s kappa of 0.7366. To ensure transparency for risk decisions, SHAP analyses identify Terrain Ruggedness Index, wind speed, slope, aspect and NDVI as dominant drivers. The dependence plots reveal actionable thresholds and interactions, including a TRI plateau near 5–7, a slope peak between 30° and 40°, a wind effect that saturates above about 2.5 m s⁻¹, and a near-river high-risk belt within 0–2 km. The five-class map aligns with independent field observations, with more than three quarters of events falling in moderate to very high zones. By integrating explainable ML with remote sensing, this study advances avalanche risk assessment in cold region transportation corridors and strengthens the robustness of regional susceptibility mapping.

The avalanche speed is an important indicator for measuring the intensity of avalanches, and its measurement method is relatively complex. In practical engineering, empirical formulas based on statistics are usually adopted. However, research on the applicability of existing calculation methods in different regions is still insufficient, and further verification and improvement are urgently needed. Based on the integrated space–air–ground field survey data, this study uses RAMMS::AVALANCHE to conduct dynamic numerical simulations of 78 avalanche events in the Qiet’ akesu Gully of the Wenquan to Horgos transportation corridor in the Western Tianshan Mountains during the winter of 2023–2024, analyses the avalanche movement process, and compares the calculation results of the numerical tests of avalanche movement speed with empirical formulas. The results indicate that the velocities calculated using Formula A and Formula B are generally overestimated, approaching approximately 1.5 times the reference value. The mean absolute percentage error of Formula A (19.46%) is lower than that of Formula B (48.27%). In contrast, Formula C exhibits a significantly lower mean absolute percentage error (8.42%) compared with the other two methods, and its results remain stably around one-half of the reference value. Based on these findings, a comprehensive estimation strategy is proposed: twice the value calculated by Formula C is adopted as the primary reference, while two-thirds of the value from Formula A is taken into consideration, and the larger of the two is selected as the final estimated velocity. This strategy ensures the robustness of the results while effectively avoiding the potential overestimation or underestimation associated with reliance on a single empirical formula. This study provides a scientific basis for highway route selection and the placement of avalanche mitigation measures in high-altitude mountainous areas, and offers technical support for the construction and operational safety of infrastructure along the G219 Wenquan–Horgos transportation corridor.