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.

Environmental factors that affect the activity-inactivity variation of periglacial features may differ from those factors that control the distributional patterns of active features. To explore this potential difference, a statistically based modelling approach and comprehensive data on active and inactive cryoturbation and solifluction features from a subarctic area of Finnish Lapland are investigated at a landscape scale. In the cryoturbation modelling, vegetation abundance is the most important environmental variable explaining both the activity-inactivity variation and the distribution of active sites. The next most important variables are soil moisture and (micro)climatological conditions in the activity modelling, and slope angle and ground material in the distribution modelling. For solifluction, the key variables determining the activity-inactivity variation are mean annual air temperature and mean maximum snow depth, whereas vegetation abundance and slope angle control the distribution of active sites. Comparison between the environmental conditions of active and inactive periglacial features may provide new insights into activity-environment relationships, which in turn are valuable when the effects of climate change on periglacial processes are explored. Copyright (c) 2014 John Wiley & Sons, Ltd.