Sensitivity of modelled mass balance and runoff to representations of debris and accumulation on the Kaskawulsh Glacier, Yukon, Canada
Bibliographic record
Abstract
Abstract Runoff contributions from glacierized catchments are changing in response to accelerating mass loss. We reconstruct the 1980–2022 mass balance, runoff and water budget of the ∼70% glacierized Kaskawulsh River headwaters in Yukon, Canada, using an enhanced temperature-index model driven by downscaled and bias-corrected reanalysis data. Debris is treated using melt-scaling factors based on site-specific measurements of the critical debris thickness. Accumulation is estimated from downscaled precipitation bias corrected based on in situ measurements. Model tuning incorporates observations of the 2007–18 geodetic mass balance and seasonal snowline positions on the Kaskawulsh Glacier. We assess model sensitivity to the representation of supraglacial debris and accumulation, including treatments of these processes that can be applied in the absence of in situ data. Different representations of debris produce <1% variation in the catchment-wide runoff and water budget. In contrast, accumulation estimates that omit in situ data produce 33–40% variations in modelled runoff relative to those that use these data. This work identifies site-specific measurements of accumulation as critical to accurate estimates of mass balance and runoff for the Kaskawulsh Glacier, in contrast to site-specific characterization of the effects of debris which influence estimated thinning rates at the glacier terminus but have little impact on the glacier-wide runoff.
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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 | 0.000 | 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".