Changing climatic conditions in High Mountain Asia (HMA), especially regional warming and changing precipitation patterns, have led to notable effects on mountain permafrost. Comprehensive knowledge of mountain permafrost in HMA is mostly limited to the mountains of the Qinghai-Tibetan Plateau, with a strong cluster of research activity related to critical infrastructure providing a basis for related climate adaptation measures. Insights related to the extent and changing characteristics of permafrost in the Hindu Kush Himalaya (HKH), are much more limited. This study provides the first comprehensive review of peer-reviewed journal articles, focused on hydrological, ecological, and geomorphic impacts associated with thawing permafrost in HMA, as well as those examining adaptations to changes in mountain permafrost. Studies reveal a clear warming trend across the region, likely resulting in increased landslide activity, effects on streamflow, soil saturation and subsequent vegetation change. Adaptation strategies have been documented only around infrastructure megaprojects as well as animal herding in China. While available research provides important insight that can inform planning in the region, we also identify a need for further research in the areas of hazards related to changing permafrost as well as its effect on ecosystems and subsequently livelihoods. We suggest that future planning of infrastructure in HMA can rely on extrapolation of already existing knowledge within the region to reduce risks associated with warming permafrost. We highlight key research gaps as well as specific areas where insights are limited. These are areas where additional support from governments and funders is urgently needed to enhance regional collaboration to sufficiently understand and effectively respond to permafrost change in the HKH region.

Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.