Investigating the seasonal influence of atmospheric rivers on runoff generation during rain-on-snow
Bibliographic record
Abstract
Numerous studies in the western United States have shown that atmospheric rivers (AR) have been responsible for flood-prone rain-on-snow (ROS) conditions such as intense rainfall, rapid warming of the air, and important snowmelt. Intense AR events were also shown to affect snowpack depth on a seasonal scale. The documentation of such impacts is less extensive in other areas, such as the east coast of North America. Meanwhile, climate projections indicate that the intensity and frequency of extreme events associated with atmospheric rivers will increase for the region, leading to an elevated hydrological impact caused by AR. This study focuses on the Côte-Nord region in Quebec (Canada), which experiences important yearly snow accumulation (>300 mm) and where snowpack monitoring is crucial due to the high hydroelectricity production in the area. The impacts of AR are analyzed by combining hydrometeorological observations with automated snow water equivalent measurements at 42 sites for the 2012–2021 period, as well as atmospheric river intensity scales derived from reanalysis. The ROS events were separated between those accompanied by AR and those that were not, resulting in 149 AR and 58 non-AR events. The intensity of the events was represented by the generated water available for runoff (WAR), which combines net rainfall and snowmelt. A seasonal analysis revealed that early winter was characterized by a high frequency of AR-associated events (36), exhibiting the greatest yearly frequency of high-scale AR events. The median WAR for AR events during this period was 36 mm, with rainfall predominating. In instances of extreme precipitation, WAR was significantly amplified by snowmelt, resulting from the rapid warming of shallow snowpacks. In late winter, there was a more balanced distribution of non-AR (54) and AR (74) events, which were characterized by generally lower intensity scales. This resulted in lower median WAR of, respectively, 30 mm and 19 mm for AR and non-AR events. However, the contribution of snowmelt during these events closely resembled that of rainfall, due to the generally warmer temperatures and the presence of lower-scale AR. The seasonal behaviour of the ROS events suggests a precipitation phase sensibility for WAR generation and variability in energy balance components. The sensitivity to precipitation phase is expected to vary between early and late winter, due to their distinct WAR compositions. Similarly, the event energy balance is bound to differ between early and late winter due to the contrasting conditions provided by low- and high-scale AR. This study underscores the distinctions between early and late winter ROS events and the necessity of accounting for the effects of atmospheric rivers on snowpack dynamics. Additionally, the findings outline considerations for snowpack modelling to more accurately represent extreme weather events projected to increase in frequency in the future.
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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 | 0.000 | 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) | 0.001 | 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".