Snowmelt runoff sensitivity analysis to drought on the Canadian Prairies. Hydrological Processes 21
Bibliographic record
Abstract
The Canadian prairies are subject to severe extended droughts that are characterized by warmer temperatures, lower precipitation, lower soil moisture and sparser vegetation than normal conditions. The physically based cold regions hydrological modelling platform (CRHM) provides a possible means to analyse the sensitivity of prairie snowmelt processes to drought. The model was tested against detailed observations from Creighton Tributary of the Bad Lake Research Basin, Saskatchewan for the 1974–1975 and 1981–1982 hydrological years and found to perform satisfactorily in reproducing snow accumulation and streamflow without parameter calibration. By lowering winter precipitation and raising winter air temperature from actual meteorological observations and by lowering fall soil moisture and vegetation height parameters, the resulting drought condition sensitivity of snow accumulation, snow cover duration, sublimation of blowing snow, evaporation, infiltration into frozen soils, soil moisture storage change, snowmelt runoff and streamflow discharge was estimated. Snow accumulation and snow cover duration were relatively insensitive to meteorological changes associated with drought because the suppression of blowing snow sublimation moderated reduced snowfall. Infiltration, soil moisture storage change and evaporation were also relatively insensitive to drought conditions. However, lower precipitation, higher air temperature and lower initial soil moisture caused a marked reduction in snowmelt runoff. Similarly, large reductions in streamflow discharge were caused by diminished winter precipitation, increased winter air temperature and decreased fall soil moisture content. A scenario showed that a combination of these factors could cause complete cessation of spring streamflow even under moderate drought of 15 % reduction in winter
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".