Characteristics of Extreme Precipitation and Flood Producing Atmospheric Rivers in the Alouette Watershed of British Columbia and the Development of a Modified Severity Scale
Bibliographic record
Abstract
Abstract Atmospheric rivers (ARs) transport large amounts of atmospheric moisture and can lead to extreme flooding events, particularly when they interact with coastal mountains such as in British Columbia (BC), Canada. Canada is yet to implement a scale to characterize the severity of ARs and there has been little research into the specific features of ARs that cause extreme flooding, especially for regions outside of the US. Using ERA5 data and the Global AR Database (Guan, 2022, https://doi.org/10.25346/S6/YO15ON ), we studied the effects of a range of AR characteristics on extreme precipitation and streamflow in the Alouette watershed, which includes an important reservoir. For this watershed, the majority of extreme‐event producing ARs (X‐ARs) approach the BC coast from compass directions of between 210 and 220°. ARs that approach from a more westerly direction can cause extreme events with an IVT as low as 400 kg m −1 s −1 compared to over 800 kg m −1 s −1 for ARs from a more southerly direction. IVT, the presence of rain‐on‐snow events, and high preceding soil moisture conditions strongly correlate with the production of extreme streamflow in the Alouette watershed. For the Alouette watershed, we recommend that the AR scale used in the US (Ralph, Rutz, et al., 2019, https://doi.org/10.1175/bams‐d‐18‐0023.1 ), be enhanced to better represent the severity of ARs in this watershed by including the surface air temperature, angle of approach, and antecedent conditions in the hazard calculation. A similar analysis should be applied to other high‐impact watersheds where accurate prediction of AR impacts is critical.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".