Snowpack sensitivity to perturbed climate in a cool mid‐latitude mountain catchment
Bibliographic record
Abstract
Abstract There is great interest in ascertaining the degree of climate change necessary to induce substantial changes in snow accumulation and ablation processes in mountain headwater catchments. Therefore, the response of mountain snow hydrology to changes in air temperature and precipitation was examined by simulating a perturbed climate in Reynolds Mountain East (RME), a headwater catchment with a cool mountain climate in Idaho, USA. The cold regions hydrological model was used to calculate snow accumulation, wind redistribution by blowing snow, interception by forest canopies, sublimation and melt for 25 seasons in RME. The uncalibrated simulations of the highly redistributed snow water equivalent compared well to measurements. Results showed that with concomitant occurrence of warming (5 °C) and precipitation change (±20%) in RME, the peak seasonal snow accumulation decreased by 84–90%, snowmelt decreased 51–79%, rainfall to total precipitation ratio increased from 30% to 78%, and overwinter blowing snow transport and sublimation losses from intercepted snow, the snow surface and blowing snow decreased dramatically. Warming causes an increase in inter‐water year snowcover variability but a decrease in spatial snow accumulation variability. When warming exceeded 1 °C and a precipitation increased by less than 20%, the peak snow accumulation declined dramatically. The results contrast with those from further north along the North American Cordillera in Yukon, Canada, where the impacts of similar warming on alpine snow can be partly compensated for by concomitant increases in precipitation of less than 20%. Copyright © 2015 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 | 0.001 |
| 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 | 0.001 | 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".