Debris Emergence Elevations and Glacier Change
Bibliographic record
Abstract
Debris-covered glaciers represent potentially significant stores of freshwater in river basins throughout High Mountain Asia (HMA). Direct glacier mass balance measurements are extremely difficult to maintain on debris-covered glaciers, and optical remote sensing techniques to evaluate annual equilibrium line altitudes (ELAs) do not work in regions with summer-accumulation type glaciers. Surface elevation and glacier velocity change have been calculated previously for debris-covered glaciers across the region, but the response of debris cover itself to climate change remains an open question. In this research we propose a new metric, i.e. the debris emergence elevation (ZDE), which can be calculated from a combination of optical and thermal imagery and digital elevation data. We quantify ZDE for 975 debris-covered glaciers in HMA over three compositing periods (1985–1999, 2000–2010, and 2013–2017) and compare ZDE against median glacier elevations, modelled ELAs, and observed rates of both mass change and glacier velocity change. Calculated values of ZDE for individual glaciers are broadly similar to both median glacier elevations and modelled ELAs, but slightly lower than both. Across the HMA region, the average value of ZDE increased by 70 +/− 126 m over the study period, or 2.7 +/− 4.1 m/yr. Increases in ZDE correspond with negative mass balance rates and decreases in glacier velocity, while glaciers and regions that show mass gains and increases in glacier velocity experienced decreases in ZDE. Regional patterns of ZDE, glacier mass balance, and glacier velocities are strongly correlated, which indicates continued overall increases in ZDEE and expansion of debris-covered areas as glaciers continue to lose mass. Our results suggest that ZDE is a useful metric to examine regional debris-covered glacier changes over decadal time scales, and could potentially be used to reconstruct relative mass and ELA changes on debris-covered glaciers using historical imagery or reconstructed debris cover extents.
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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 | 0.000 | 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) | 0.001 | 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".