Stable giants? Persistence and hazard potential of world's largest glacier lakes
Bibliographic record
Abstract
Between 1990 and 2020, retreating glaciers have created accommodation space for ~20,000 new glacier lakes globally (+38%), increasing the total glacier lake area by ~2,000 km² (+9%). Among these, large glacier lakes (>1 km2) have drawn substantial attention due to their roles in hydropower production, freshwater supply, tourism, and landscape protection. Researchers have also stressed their high hazard potential, given that their dams might collapse and release catastrophic outburst floods. Any sustainable use and effective hazard mitigation of large glacier lakes thus require a deeper understanding of their geomorphic setting and long-term dynamics.Using a global catalogue of large lakes mapped within 10 km of contemporary glaciers (Zhang et al., 2024), we find that large lakes comprised only 3.8% (n = 2,781) of global lake abundance in 2020 but accounted for 77% of the total lake area. While the total area of large lakes has grown by 35% overall since 1990, only 14% of individual lakes have significantly expanded. By contrast, the majority remained either stable (73%) or even shrank (13%), suggesting that large glacier lakes can be persistent features in high mountain landscapes. Greenland, Arctic Canada, Patagonia, Alaska, and Western Canada host three-quarters of these lakes, often in low-relief, widely deglaciated catchments disconnected from their parent glaciers. More than half of all large lakes are surrounded by tundra, forests, or grasslands, likely reducing geomorphic activity on adjacent slopes. Where Little Ice Age (LIA) glacier outlines are available, we observe that large lakes have formed both before (e.g., Scandinavia, European Alps) or after (e.g., Southern Andes, Himalayas) this period. Importantly, only a handful of large proglacial lakes had historic outbursts, underscoring their stability on centennial timescales.Average erosion rates in their feeding catchments suggest that many large lakes may persist for another 103–105 years before being entirely filled with sediments, all other constraints held constant. While some large lakes may still occasionally produce catastrophic outbursts, our analysis points to the smaller, disproportionally more abundant lakes in similar geomorphic settings, which have a comparable, if not higher hazard potential. These findings call for focused research on the dynamics of these smaller glacier lakes to better inform hazard assessments and mitigation strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".