Increasing cryospheric hazards and sediment supply threaten water quality and hydropower systems in high mountain areas
Bibliographic record
Abstract
Global warming-induced melting and thawing of the cryosphere are rapidly changing hydrogeomorphic processes and cryospheric hazards in high mountain areas worldwide. These processes and hazards include glacial retreat and collapses, permafrost thaw and associated landslides, rock-ice avalanches, debris flows, and outburst floods from glacier lakes and landslide-dammed lakes. The changing slope instability and extreme flood have accelerated landscape erosion and increased fluvial sediment loads. For example, the rivers in High Mountain Asia are becoming muddier due to increased suspended particulate matters from melting glaciers and thawing permafrost, likely degrading water quality as fine-grained sediment are easily bonded with organic carbon, phosphorus and most heavy metals (e.g., mercury, chromium, arsenic and lead). Importantly, numerous hydropower dams and reservoirs are under construction or planning in high-mountain areas worldwide such as in the Himalaya and Andes. The increasing amounts of mobilized sediment can fill up reservoirs, cause dam failure, and degrade power turbines, threatening the short-term safety and longer-term sustainability of these hydropower systems.In the future, we recommend forward-looking design and maintenance solutions that can help transition towards climate change-resilient high-quality water supply and hydropower systems in high-mountain areas. The specific suggestions include: (i) monitor the climate, glaciers and permafrost, glacial lakes, unstable slopes, discharge and sediment yields to better understand the cascading links between climate change, glacier retreat and hazards; (ii) predict future fluvial sediment loads, water quality and reservoir sedimentation in a changing climate and develop sustainable sediment management solutions; (iii) establish real-time early warning systems and enhance social awareness and drills, especially for in-construction dams to minimize human and infrastructure losses; (iv) enhance transboundary cooperation by establishing data-sharing schemes and adopting joint-operation strategies to better cope with hazards and optimise sediment flushing; and (v) promote the inclusion of indigenous and local knowledge in policy, governance, and management for water quality assessment and dam and reservoir construction.The major results of this study have been published online: Li, D., Lu, X., Walling, D. E., Zhang, T., Steiner, J. F., Wasson, R. J., ... & Bolch, T. (2022). High Mountain Asia hydropower systems threatened by climate-driven landscape instability. Nature Geoscience, 15(7), 520-530. https://doi.org/10.1038/s41561-022-00953-y
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".