Progressively smaller glacier lake outburst floods despite worldwide growth in lake area
Bibliographic record
Abstract
Abstract Glacier lake outburst floods (GLOFs) may originate from larger lakes as these continue to grow with ongoing glacier retreat; however, this perception remains poorly supported in available GLOF databases. Here we mapped the areas of 1,686 glacier lakes, just before they drained, across 13 glaciated mountain regions outside polar regions and examined the trends in pre-GLOF lake areas between 1990 and 2023. We found that pre-GLOF lake areas barely changed, or even decreased, regionally through time, even as the total lake area, and thus hazard potential, grew overall. This counterintuitive finding reflects limits to growing GLOF magnitudes, such as the decoupling of lakes and parent glaciers, the development of wide, low-gradient outlets or human management. Across all regions, pre-GLOF lake areas depend on a few ice-dammed lakes, which have produced ten times more reported outbursts and ten times larger outbursts than moraine- and bedrock-dammed lakes. The latter two dam types will continue to impound growing amounts of meltwater, thereby accounting for most of the overall GLOF hazard potential, while ice-dammed lakes will shrink with deglaciation. As these lake types will evolve differently in the twenty-first century, we call for customized simulations of GLOF outflows and impacts, given the growing exposure of critical infrastructure.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".