Quantifying the contribution of glacier runoff to streamflow in the upper Columbia River basin, Canada
Bibliographic record
Abstract
Abstract. Glacier melt provides important contributions to streamflow in many mountainous regions. Hydrologic model calibration in glacier-fed catchments is difficult because errors in modelling snow accumulation can be offset by compensating errors in glacier melt. This problem is particularly severe in catchments with modest glacier cover, where goodness-of-fit statistics such as the Nash-Sutcliffe model efficiency may not be highly sensitive to the streamflow variance associated with glacier melt. While glacier mass balance measurements can be used to aid model calibration, they are absent for most catchments. We introduce the use of glacier volume change determined from repeated glacier mapping in a guided GLUE (generalized likelihood uncertainty estimation) procedure to calibrate a hydrologic model. We also explicitly account for changes in glacier area through the calibration and test periods. The approach is applied to the Mica basin in the Canadian portion of the Columbia River basin using the HBV-EC hydrologic model. Use of glacier volume change in the calibration procedure effectively reduced parameter uncertainty and helped to ensure that the model was accurately predicting glacier mass balance as well as streamflow. The seasonal and interannual variations in glacier melt contributions were assessed by running the calibrated model with historic glacier cover and also after converting all glacierized areas to alpine land cover in the model setup. Although glaciers in the Mica basin only cover 5 % of the watershed, glacier ice melt contributes up to 25 % and 35 % of streamflow in August and September, respectively, and is particularly important during periods of warm, dry weather following winters with low accumulation and early snowpack depletion. The approach introduced in this study provides an effective and widely applicable approach for calibrating hydrologic models in glacier fed catchments, as well as for quantifying the magnitude and timing of glacier melt contributions to streamflow.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".