Bibliographic record
Abstract
Abstract. Many glaciers dam lakes at their margins that can drain suddenly. Due to downwasting of these glacier dams, the magnitude of glacier lake outburst floods may change. Judging from repeat satellite observations, most ice-dammed lakes with repeated outbursts have decreased in area, volume, and flood size. Yet, we find that some lakes oppose this trend by releasing progressively larger volumes over time, and elevating downstream hazards. One of these exceptions is Desolation Lake, southeastern Alaska, having drained at least 48 times since 1972 with progressively larger volumes despite the surface lowering of the local ice dam. Here we focus on explaining its unusual record of lake outbursts using estimates of flood volumes, lake levels, and glacier elevation based on a time series of elevation models and satellite images spanning five decades. We find that the lake grew by ~10 km2 during our study period, more than any other ice-dammed lake with reported outbursts in Alaska. The associated flood volumes tripled from 200–300 × 106 m3 in the 1980s up to ~700 × 106 m3 in the 2010s, which is more than five times the regional median of reported flood volumes from ice-dammed lakes. Yet, Lituya Glacier, which dams the lake, had a median surface lowering of ~50 m between 1977 and 2019 and the annual maximum lake levels dropped by 110 m since 1985, to a level of 202 m a.s.l. in 2022. We explain the contrasting trend of growing lake volume and glacier surface lowering in terms of the topographic and glacial setting of Desolation Lake. The lake lies in a narrow valley in contact with another valley glacier, Fairweather Glacier, at its far end. During our study period, the ice front of the Fairweather Glacier receded rapidly, creating new space that allowed the lake to expand laterally and accumulate a growing volume of water. We argue that the growth of ice-dammed lakes with outburst activity is controlled more by 1) the potential for lateral expansion and 2) meltwater input due to ablation at the glacier front, than by overall mass loss across the entire glacier surface. Lateral lake expansion and frontal glacier ablation can lead to larger lake outbursts even if ablation of the overall glacier surface accelerates and the maximum lake level drops. Identifying valleys with hazardous ice-topographic conditions can help prevent some of the catastrophic damage that ice dam failures have caused in past decades.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.016 | 0.008 |
| Insufficient payload (model declined to judge) | 0.586 | 0.428 |
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".