Adaptation to climate change induced water stress in major glacierized mountain regions
Bibliographic record
Abstract
Mountains are a critical source of water. Cryospheric and hydrological changes in combination with socio-economic development are threatening downstream water security triggering the need for effective adaptation responses. Here, we present a global systematic review (83 peer-reviewed articles) that assesses different water-related stressors and the adaptation responses to manage water stress in major glaciated mountain regions. Globally, agriculture (42%), tourism (12%), hydropower (8%) and health and safety (4%) are among the main sectors affected by hydrological and cryospheric changes . A broad set of adaptation measures has already been implemented in the world’s mountain regions. We find that globally the most commonly used adaptation practices correspond to the improvement of water storage infrastructure (13%), green infrastructure (9.5%), agricultural practices (17%), water governance and policies (21%), disaster risk reduction (9.5%) and economic diversification (10%). Successful implementation of adaptation measures is limited by reduced stakeholder capacities, collaboration and financial resources, and policies and development. To overcome these limitations, funding for climate change adaptation and development programmes in mountains and trust-building measures such as shared stakeholder activities need to be strengthened. Local awareness raising of both, the adverse effects of climate change and potentially positive implications of specific adaptation measures can help to support successful adaptation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".