Modelling Debris-Covered Glacier Ablation Using the Simultaneous Heat and Water Transport Model. Part 1: Model Development and Application to North Changri Nup
Bibliographic record
Abstract
Modelling ablation of glacier ice under a layer of mineral debris is increasingly important, because the extent of supraglacial debris is expanding worldwide due to glacier recession. Physically based models have been developed, but the uncertainty in predictions is not yet well constrained. A new one-dimensional model of debris-covered ice ablation that is based on the Simultaneous Heat and Water transfer model is introduced here. SHAW-Glacier is a physically based, vertically integrated, fully coupled, water and energy balance model, which includes the advection of heat by rainwater and lateral flow. SHAW-Glacier was applied to North Changri Nup, a high elevation alpine glacier in the monsoon-dominated Central Himalaya. Simulations were compared with observed debris temperature profiles, snow depth, and ablation stake measurements for debris 0.03–0.41 m thick, in a 2500 m 2 study area. Prediction uncertainty was estimated in a Monte Carlo analysis. SHAW-Glacier simulated the characteristic pattern of decreasing ablation with increasing debris thickness. However, the observations of ablation did not follow the characteristic pattern; annual ablation was highest where the debris was thickest. Recursive partitioning revealed a substantial, non-linear sensitivity to the snow threshold air temperature, suggesting a sensitivity to the duration of snow cover. Photographs showed patches of snow persisting through the ablation season, and the observational data were consistent with uneven persistence of snow patches. The analyses indicate that patchy snow cover in the ablation season can overwhelm the sensitivity of sub-debris ablation to debris thickness. Patchy snow cover may be an unquantified source of uncertainty in predictions of sub-debris ablation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".