Active layer thickness (ALT) is a sensitive indicator of response to climate change. ALT has important influence on various aspects of the regional environment such as hydrological processes and vegetation. In this study, 57 ground-penetrating radar (GPR) sections were surveyed along the Qinghai–Tibet Engineering Corridor (QTEC) during 2018–2021, covering a total length of 58.5 km. The suitability of GPR-derived ALT was evaluated using in situ measurements and reference datasets, for which the bias and root mean square error were approximately −0.16 and 0.43 m, respectively. The GPR results show that the QTEC ALT was in the range of 1.25–6.70 m (mean: 2.49 ± 0.57 m). Observed ALT demonstrated pronounced spatial variability at both regional and fine scales. We developed a statistical estimation model that explicitly considers the soil thermal regime (i.e., ground thawing index, TI_g), soil properties, and vegetation. This model was found suitable for simulating ALT over the QTEC, and it could explain 52% (R² = 0.52) of ALT variability. The statistical model shows that a difference of 10 °C.d in $\sqrt{T{I}_g}$ is equivalent to a change of 0.67 m in ALT, and an increase of 0.1 in the normalized difference vegetation index (NDVI) is equivalent to a decrease of 0.23 m in ALT. The fine-scale (<1 km) variation in ALT could account for 77.6% of the regional-scale (approximately 550 km) variation. These results provide a timely ALT benchmark along the QTEC, which can inform the construction and maintenance of engineering facilities along the QTEC.

Global climate warming has exacerbated extreme snowfall events. The Southeastern Tibet (ST) region has become a high-incidence area for avalanches due to its unique topographical and climatic conditions. However, current research has paid insufficient attention to the thresholds for avalanches triggered by extreme snowfall. Therefore, the aim of this study is to construct the I-D (intensity-duration) thresholds for avalanche events triggered by extreme snowfall in southeastern Tibet, providing a scientific basis for disaster prevention and mitigation work in this region. Based on the snowfall data from 1951 to 2020, this study calculated four extreme snowfall indices, namely SF1d, SF90p, SF95p, and SF99p, to determine extreme snowfall events. And 33 avalanche events during this period were verified through the confusion matrix. This study found that the intensity of extreme snowfall events in southeastern Tibet has increased while the frequency has decreased. The I-D threshold parameters α (from 5.79 to 14.88) and β (from −2.81 to −0.66) within the study area were determined, and the overall threshold is I = 9.29 × D^−2.27 (D represents the duration of snowfall, with the unit being days.). It was also found that extreme snowfall in the study area has a significant positive correlation in with the ST. The terrain has a greater impact on the snowfall intensity, but its regulation on the duration of events is limited. Overall, in southeastern Tibet, if the single-day snowfall exceeds 12.38 mm (the regional average value of the SF1d index) or the cumulative snowfall within the previous 30 days exceeds 64.85 mm (the regional average value of the three indices of SF90p, SF95p, and SF99p), it can be considered that an extreme snowfall event has occurred. At the same time, the threshold of I = 9.29 × D^−2.27 can be used to forecast avalanches triggered by extreme snowfall events in the entire region.

Reliable avalanche forecasting is essential for protecting mountain communities and transportation corridors, but estimating avalanche occurrence from monitored meteorological and snowpack conditions remains difficult. Operational assessments often rely primarily on meteorological thresholds, although avalanche responses depend on both type-specific triggering process and the snowpack conditions. Using meteorological, pre-event snowpack, and avalanche observations collected during the 2024 and 2025 snow seasons, we analysed 37 recorded avalanche events, together with corresponding non-avalanche periods. Separate logistic-regression models were developed for dry- and wet-snow avalanches using the intensity and duration of the preceding snowfall or snowmelt process and background snow depth. Their performance was compared with otherwise identical models excluding snow depth. Under leave-one-out cross-validation, including background snow depth increased the area under the receiver operating characteristic curve from 0.72 to 0.83 for dry-snow avalanches and from 0.85 to 0.94 for wet-snow avalanches. For wet-snow avalanches, the true-positive rate increased from 0.79 to 0.92, while the false-positive rate decreased from 0.21 to 0.12. These results demonstrate that pre-event snowpack conditions provides predictive information beyond meteorological forcing alone and improves avalanche forecasting, particularly for wet-snow avalanches.

Reliable avalanche forecasting is essential for protecting mountain communities and transportation corridors, but estimating avalanche occurrence from monitored meteorological and snowpack conditions remains difficult. Operational assessments often rely primarily on meteorological thresholds, although avalanche responses depend on both type-specific triggering process and the snowpack conditions. Using meteorological, pre-event snowpack, and avalanche observations collected during the 2024 and 2025 snow seasons, we analysed 37 recorded avalanche events, together with corresponding non-avalanche periods. Separate logistic-regression models were developed for dry- and wet-snow avalanches using the intensity and duration of the preceding snowfall or snowmelt process and background snow depth. Their performance was compared with otherwise identical models excluding snow depth. Under leave-one-out cross-validation, including background snow depth increased the area under the receiver operating characteristic curve from 0.72 to 0.83 for dry-snow avalanches and from 0.85 to 0.94 for wet-snow avalanches. For wet-snow avalanches, the true-positive rate increased from 0.79 to 0.92, while the false-positive rate decreased from 0.21 to 0.12. These results demonstrate that pre-event snowpack conditions provides predictive information beyond meteorological forcing alone and improves avalanche forecasting, particularly for wet-snow avalanches.