Due to the increasing air temperatures, retrogressive thaw slumps (RTSs) have become more common on the Qinghai–Tibet Plateau (QTP), but their inventory, frequency, and initiation have not been fully investigated. In satellite imagery for the period 2018–2020, 2669 active RTSs were identified in permafrost regions of the QTP. Most of the RTSs occur in regions with elevations ranging from 4,600 to 5,100 m along north‐facing slopes varying from 3° to 8°. The number of RTSs and their affected areas increased by 4 and 6 times, respectively, in three densely distributed RTS regions from 2008/2013 to 2021. This increase in the RTS occurrence mainly occurred during the warm season in 2010 and 2016 and is likely due to high air temperatures during the thawing season, which induced the massive occurrence of active‐layer detachment slides. Another intensification of RTS occurrences may appear when higher anomaly air temperatures are observed.

Permafrost warming leads to greenhouse gas release to the atmosphere, resulting in a positive feedback to climate change. Earth system models indicate that more than 80% of the near‐surface permafrost is projected to disappear by the end of this century, but with a high degree of uncertainty. Here, we apply the Stefan solution to estimate permafrost degradation under future emission scenarios. We find that the most severe future scenario is likely to lead to only a 14% decrease in area extent of the near‐surface permafrost at 3.5 m depth, and an area extent decrease of 1.3% at a depth of 6.0 m. Relative to active layer thickness increases from historical simulations, we find a less than 30% deepening for most permafrost regions by the end of this century. These results imply that the Stefan solution provides near‐surface permafrost area extent degradation estimates that are substantially lower than directly projected by models.

Retrogressive thaw slumps (RTSs), formed by abrupt degradation of ice‐rich permafrost, are widely distributed on the Qinghai‐Tibet Plateau, causing infrastructure damage and enhancing soil carbon emissions. We compiled annual RTS inventories across the plateau from 2016 to 2022 using a deep‐learning‐aided method to quantify the spatial‐temporal variations. We found that RTS‐affected locations increased from 1,592 to 3,805 in 2016–2022, which increased affected areas by 2.8 times from 1,714 to 6,507 ha. The most active initiation and expansion periods were in 2016–2017 and 2018–2019. RTSs tend to be clustered, showing local heterogeneity among clusters characterized by various responses toward high temperatures and precipitation and tendencies to be on different topography and vegetation types. This research reveals the rapid development, wide distribution and regional heterogeneity of RTS activities, serving as a crucial step toward understanding how RTSs respond to climate change and regional environmental varieties.

Permafrost distribution in the Qinghai-Tibet Engineering Corridor (QTEC) is of growing interest due to the increase in infrastructure development in this remote area. Empirical models of mountain permafrost distribution have been established based on field sampled data, as a tool for regional-scale assessments of its distribution. This kind of model approach has never been applied for a large portion of this engineering corridor. In the present study, this methodology is applied to map permafrost distribution throughout the QTEC. After spatial modelling of the mean annual air temperature distribution from MODIS-LST and DEM, using high-resolution satellite image to interpret land surface type, a permafrost probability index was obtained. The evaluation results indicate that the model has an acceptable performance. Conditions highly favorable to permafrost presence (≥70%) are predicted for 60.3% of the study area, declaring a discontinuous permafrost distribution in the QTEC. This map is useful for the infrastructure development along the QTEC. In the future, local ground-truth observations will be required to confirm permafrost presence in favorable areas and to monitor permafrost evolution under the influence of climate change.