Snowmelt contribution to discharge from a large mountainous catchment in subarctic Canada
Bibliographic record
Abstract
Abstract Snowmelt is responsible for much of the annual runoff and most of the peak discharges in subarctic mountainous regions. It also provides a significant amount of freshwater inflow to the polar seas, which has implications for Arctic Ocean circulation. Owing to considerable topographic contrasts in large mountainous basins, snow accumulation and melt patterns are highly variable in time and space, but the scarcity of data in these regions prevents the patterns from being discerned. Application of a macro‐scale hydrological model (using reanalysis data from the European Centre for Medium‐Range Weather Forecasts, the National Centers for Environmental Prediction and the North American Regional Reanalysis) offers one suitable approach to estimate the magnitude and timing of snowmelt contribution to discharge from large mountainous catchments. The Liard basin, subarctic Canada, is used as an example and the SLURP (Semi‐distributed Land‐use‐based Runoff Processes) model allows hydrograph simulation for the Liard and its sub‐basins. Three sets of reanalysis temperature and precipitation data provide inputs to assess the sensitivity of model simulation. The spatial patterns of snowmelt, runoff and stream discharge for four water years were simulated. The SLURP model was found to be sensitive to a plausible range of input conditions as depicted by the three sets of reanalysis data. Despite differences in detail among the three sets of simulation results, several generalities emerged. A comparison of simulated snow cover with satellite data confirms that there are altitudinal delays in spring flow generation though latitude has no apparent influence. Runoff lags snowmelt while the catchment integrates flows of its tributaries, yet different combinations of winter snowfall and spring melt rates cause large interannual variations in snowmelt discharge. Streamflow measured and simulated at four stations along the main river permits an evaluation of runoff contribution from various sectors of the basin. The overall pattern of melt runoff generation and the modelling approach used in this investigation are applicable to other large mountainous basins in high latitudes. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".