Modeling mass balance at Robertson Glacier, Alberta, Canada 1912-2012
Bibliographic record
Abstract
Glacier mass balance is important to study due to the role of glaciers in the hydrological cycle. Glacier mass balance is typically difficult to measure without numerous in situ measurements and monitoring over the course of many years. Physically based melt models are a good tool for estimating melt using temperature, solar radiation, and albedo and are used extensively in this thesis. A Degree Day (DD) model and an Enhanced Temperature Index (ETI) model are used to model mass balance for Robertson Glacier, Alberta, Canada during the period 1912-2012. The DD model only incorporates temperature, while the ETI model incorporates temperature, incoming solar radiation, and albedo. Incoming solar radiation was modeled for the period 2007-2012 and parameterized for the period 1912-2006 while temperature was measured at the regional scale and synthesized for Robertson Glacier and the snowpack thickness was modeled using PRISM. The DD and ETI models both assume a static ice mass, i.e. no flow or change in ice elevation due to mass loss over the century time period. Both models estimate a high value of annual and accumulated mean mass loss for the period 1912-2012. Sensitivity analyses of model inputs indicate that snowpack is an important factor, and it appears PRISM estimates may underrepresent beginning of the year snowpack by 220% based on a comparison of modelled to measured values on the adjacent Haig Glacier. Avalanching is not a key component of accumulation on the Haig Glacier but is a key process at Robertson Glacier, and could result in locally doubling the snowpack accumulation in avalanche zones. These factors including the resultant albedo changes with a thicker snowpack are all part of a compounding negative feedback cycle on glacier mass loss. In summary, the thesis has highlighted several potential limitations to the ETI and DD models for assessing mass loss for a small mountain glacier in the southern Canadian Rockies and provides suggestions for future modelling work in this region.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".