Mathematical modelling of supraglacial meltwater production and drainage
Bibliographic record
Abstract
Mountain glaciers and the polar ice sheets exert a critical control on water resource availability, drive sea level change, and impact global ocean circulation. These and other impacts are controlled by surface meltwater that flows through the glacier hydrologic system to the base of the ice and drives seasonal and long-term changes in ice flow velocity. This thesis presents numerical models for the production and transport of meltwater runoff across the surface of melting glaciers and ice sheet. \n \nFirst, a surface energy balance model is developed that improves on existing models by utilizing high resolution satellite data to capture spatial variations in surface melt. The model is applied to Kaskawulsh Glacier and Nàłùdäy (Lowell) Glacier in the St. Elias Mountains, Yukon, Canada using six years of in-situ meteorological data. By validating model outputs against in-situ measurements, it is shown that modelled seasonal melt agrees with observations within 9% across a range of elevations. \n \nIn order to determine how surface meltwater is transported through moulins, we develop the Subaerial Drainage System (SaDS) model. SaDS is a physics-based, finite-volume numerical model that calculates supraglacial runoff in both a distributed sheet and through supraglacial channels. The benefit of this approach is that a connected network of supraglacial channels and lakes naturally emerges without using prior information about the channel network, for example from satellite-derived maps. In synthetic settings and when applied to the Greenland Ice Sheet, model outputs show realistic and varied moulin flux rates, and modelled supraglacial lake and channel locations match those mapped from satellite images. These results demonstrate that SaDS is a promising tool to provide moulin inputs for subglacial and ice dynamic studies. \n \nThese models represent significant steps forward in their respective domains. Together, these tools will be valuable components of future modelling work, including for studies that aim to constrain how climatic variables control sea level contributions from glaciers and ice sheets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".