Modeling the effect of glacier recession on streamflow response using a coupled glacio-hydrological model
Bibliographic record
Abstract
Abstract. We describe an integrated spatially distributed hydrologic and glacier dynamic model, and use it to investigate the effect of glacier recession on streamflow variations for the upper Bow River basin, a tributary of the South Saskatchewan River, Alberta, Canada. Several recent studies have suggested that observed decreases in summer flows in the South Saskatchewan River are partly due to the retreat of glaciers in the river's headwaters. Modeling the effect of glacier changes on streamflow response in river basins such as the South Saskatchewan is complicated due to the inability of most existing physically based distributed hydrologic models to represent glacier dynamics. We compare predicted variations in glacier extent, snow water equivalent (SWE), and streamflow discharge with satellite estimates of glacier area and terminus position, observed glacier mass balance, observed streamflow and snow water-equivalent measurements, respectively over the period of 1980–2007. Observations of multiple hydroclimatic variables compare well with those simulated with the coupled hydrology-glacier model. Our results suggest that, on average, the glacier melt contribution to the Bow River flow upstream of Lake Louise is approximately 22% in summer. For warm and dry years, however, the glacier melt contribution can be as large as 47% in August, whereas for cold years, it can be as small as 15% and the timing of the glacier melt signature can be delayed by a month. The development of this modeling approach sets the stage for future predictions of the influence of warming climate on streamflow in partially glacierized watersheds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".