Modelling glacier mass balance and runoff in the Kaskawulsh River headwaters of southwest Yukon, Canada, 1980-2022
Bibliographic record
Abstract
The highly-glacierized headwaters of the Kaskawulsh River are home to 9% of all glacier ice in Yukon, Canada, have been losing glacier mass at regionally representative rates, and were the source of a sudden meltwater-rerouting event in 2016 that has had significant downstream consequences. We use an enhanced temperature-index melt model driven by downscaled and bias-corrected climate reanalysis data to estimate the 1980-2022 glacier mass balance, discharge, and water budget of the Kaskawulsh River headwaters. We estimate a catchment-wide cumulative mass loss of 18.02 Gt over 1980-2022 (-0.38 +- 0.15 m w.e./a) and a mean annual discharge of ~60 m3/s, 25% of which originates from non-renewable glacier wastage. The water budget is dominated by glacier ice melt, accounting for 61% of mean annual discharge, followed by snowmelt at 31%, rainfall at 6%, and melt from refrozen ice layers at 2%. Extreme negative and positive mass-balance years produce the largest perturbations in glacier ice melt contributions to the water budget, ranging from a maximum of 67% following negative years to a minimum of 53% in positive years. Catchment-wide discharge increased by 3.90 m3/s per decade from 1980-2022, with statistically significant contributions from glacier ice melt (2.80 m3/s per decade) and rainfall (0.47 m3/s per decade). Rising air temperatures and declining spring snowfall have lead to seasonally accelerated snowline retreat, earlier ice exposure, and earlier onset of net ablation in the catchment at a rate of ~5 days per decade. Based on summer air temperatures projected by CMIP6, and the empirical sensitivities of modelled runoff we calculate for 1980-2022, we hypothesize a more than doubling of annual runoff from this catchment by 2080-2100. This result, combined with a decrease in the variability of discharge from glacier ice melt over 1980-2022, suggests that this catchment is unlikely to reach "peak water'' (i.e. peak glacier contribution to catchment runoff) this century.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".