Climate change impacts and adaptation to permafrost change in High Mountain Asia: a comprehensive review
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".