Glacier fragmentation effects on surface energy balance and runoff: field measurements and distributed modelling
Bibliographic record
Abstract
Abstract In order to assess glacier runoff to the Upper Columbia River Basin (UCRB) and quantify energy balance effects of tributary‐trunk detachment due to recession, we used field observations to develop a distributed melt model of Shackleton Glacier, Canadian Rockies. Field data were derived from meteorological stations, ablation and snowline measurements, and weather observations between 2004 and 2010. Katabatic wind speed and direction were linked to terrain heat advection and irradiance, potentially resulting in significant cross‐glacier gradients in melt. A geographic information system‐based distributed melt model, using standard energy balance components, was developed for the 2010 melt season. Benchmark model parameterisations were derived for clear, cloudy and overcast days. Novel model parameterisations include terrain irradiance using a sky view factor and an albedo mask, and a katabatic wind ‘switch’ with valley temperature thresholds. Modelled energy balance components suggest significant sensitivities to terrain irradiance and katabatic wind, in part related to cloudiness. Glacier‐wide melt decreased by 10–15% when katabatic wind was turned off, with an interesting spatial pattern. Longwave radiation from valley walls increased local melt up to 30%, but net glacier‐wide effects were <6%. Daily glacier melt was 0.1–0.8 million m 3 w.e. day −1 and peaked in early August. Net 2010 planar‐area melt was 38–50 million m 3 w.e., depending on cold storage, whereas slope‐corrected‐area melt was ~4% higher. Our results indicate that katabatic wind and terrain are important in calculations of ablation in fragmenting glacier systems and that late‐summer glacier contribution to UCRB runoff at Mica Dam is ~25%. Copyright © 2012 John Wiley & Sons, Ltd.
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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".