Snow Drought Risk and Susceptibility in the Western United States and Southwestern Canada
Bibliographic record
Abstract
Abstract In western North America (WNA), mountain snowpack supplies much of the water used for irrigation, municipal, and industrial uses. Thus, snow droughts (a lack of snow accumulation in winter) can have drastic ecological and socioeconomic impacts. In this study, the historical (1951–2013) frequency, severity, and risk (frequency × severity) of dry, warm, and warm and dry snow droughts are quantified at the grid‐cell and ecoregion scale for snow‐dominated regions in the western United States and southwestern Canada (sWNA). Based on multiple linear regression analysis, relationships between mean winter temperature, snow drought risk, and snow water equivalent sensitivity are explored. Piecewise linear regression is used to identify temperature thresholds for mapping temperature‐related snow drought susceptibility. Results highlight spatial differences in snow drought regimes across sWNA and reveal that temperature thresholds exist at −3.1 °C (±0.3 °C) and 1.4 °C (±0.3 °C), above which the warm snow drought risk increases more rapidly. Approximately 3% of the nonglaciated snow storage in this region has high susceptibility to temperature‐related snow drought, representing 11 km 3 of water, or approximately one third the capacity of Lake Mead. Under a +2 °C climate scenario, an additional 8% (28 km 3 ) of this snow storage volume will transition to high susceptibility.
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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 | 0.003 |
| Science and technology studies | 0.002 | 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".