Remote sensing based assessment of hazards from glacier lake outbursts: a case study in the Swiss Alps
Bibliographic record
Abstract
Glacier lakes are a common phenomenon in high mountain areas. Outbursts from glacier lakes have repeatedly caused the loss of human lives as well as severe damage to local infrastructure. In several high mountain ranges around the world, a grave uncertainty about the hazard potential of glacier lakes still exists, especially with respect to the effects of accelerating rates of glacier retreat as a consequence of atmospheric warming. Area-wide detection and modeling of glacier lake hazard potentials is, therefore, a major challenge. In this study, an approach integrating three scale levels allows for the progressive focus on critical glacier lakes. Remote sensing methods for application in glacier lake hazard assessment are presented, and include channel indexing, data fusion, and change detection. Each method matches the requirements of a certain scale level. For estimating potential disaster amplitudes, assessments must be made of maximum discharge and runout distance of outbursts floods and debris flows. Existing empirical relations are evaluated and complementary ones as derived from available data are proposed. Tests with observations from a recent outburst event from a moraine-dammed lake in the Swiss Alps show the basic applicability of the proposed techniques and the usefulness of empirical relations for first hazard assessments. In particular, the observed runout distance of the debris flow resulting from the outburst does not exceed the empirically estimated maximum runout distance. A list of decision criteria and related remote sensing techniques are discussed in conclusion. Such a list is an essential tool for evaluating the hazard potential of a lake. A systematic application of remote sensing based methods for glacier lake hazard assessment is recommended.Key words: glacier lake outburst, hazard potential, remote sensing, empirical parameters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".