Projections of rain‐on‐snow events in a sub‐arctic river basin under 1.5°C–4°C global warming
Bibliographic record
Abstract
Abstract Rain‐on‐snow (ROS) is a compound hydrometeorological extreme event that can lead to severe socioeconomic impacts and affect ecosystem function. In high‐latitude regions, the percolation of liquid precipitation through snowpack and the associated formation of ice layers can create greater potential for significant runoff and also cause hardship for wintertime ungulate foraging. In this study, we assess the characteristics of ROS events and the corresponding impacts over a large sub‐arctic river basin in northwestern Canada. We propose seven indices to assess the projected changes in both major and minor ROS events, defined as instances of 10 and 3 mm/day, respectively, of rainfall occurring on SWE greater than 5 mm, taking into account precipitation intensity and snowpack depth. We use simulations from the variable infiltration capacity hydrologic model driven by a suite of multivariate bias‐corrected global climate models from the fifth phase of the Coupled Model Intercomparison Project and assess the ROS changes under the 1.5, 2, 3 and 4°C global warming levels above the pre‐industrial period. Overall, ROS events occur more frequently in October–December and January–March (JFM) compared to other seasons. The effects of major and minor ROS events on runoff generation in JAS and OND are considerable at higher elevations, with mean runoff more than 50% greater on ROS days than non‐ROS days in many cases. Furthermore, the analyses project notable increases in the frequency of both Major and Minor ROS events in all summer and fall. However, a notable decrease in ROS frequency is present in spring, and in winter, ROS frequency has inconsistent changes. Our comprehensive assessment of ROS events, their projected changes, and associated impacts in a sub‐arctic river basin underscore these events' critical role in shaping hydrological patterns and affecting communities, infrastructure and ecosystem dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".