Diurnal Cycles of Meltwater Percolation, Refreezing, and Drainage in the Supraglacial Snowpack of Haig Glacier, Canadian Rocky Mountains
Bibliographic record
Abstract
Meltwater refreezing and storage in the supraglacial snowpack can reduce and delay meltwater runoff from glaciers. These are well-established processes in polar environments, but the importance of meltwater refreezing and the efficiency of meltwater drainage are uncertain on temperate alpine glaciers. To examine these processes and quantify their importance on a mid-latitude mountain glacier, we measured the temperature and meltwater content in the upper 50 cm of the supraglacial snowpack of Haig Glacier in the Canadian Rocky Mountains. Thermistors and TDR probes were installed at 10-cm intervals at two sites in the glacier accumulation area from May to September, 2015. A Denoth meter was used to make point measurements for comparison with the TDR inferences of snowpack dielectric properties. These data are supplemented by automatic weather station data, used to calculate surface melt rates and drive a model of subsurface temperature, refreezing, and drainage. We observed a strong diurnal cycle in snow water content throughout the summer melt season, but subsurface refreezing was only significant in May; after this, overnight refreezing was restricted to a thin surface layer of the snowpack. Overnight decreases in water content after May are associated with meltwater percolation and drainage. There was negligible meltwater retention in the snow on a daily basis, but the refrozen water does represent an ‘energy sink’, with 10-15% of the available melt energy diverted to recycled rather than new meltwater. This reduces the total meltwater runoff from the site, even though no meltwater is retained in the system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".