Linking Atmospheric Rivers to Annual and Extreme River Runoff in British Columbia and Southeastern Alaska
Bibliographic record
Abstract
Abstract This study quantifies the contribution of atmospheric rivers (ARs) to annual and extreme river runoff and evaluates the relationships between watershed characteristics and AR-related maximum river runoff across British Columbia and southeastern Alaska (BCSAK). Datasets used include gauged runoff from 168 unregulated watersheds, topographic characteristics of those watersheds, a regional AR catalog, and integrated vapor transport fields for water years (WYs) 1979–2016. ARs contribute ~22% of annual river runoff along the Coast and Insular Mountains watersheds, which decreases inland to ~11% in the watersheds of the Interior Mountains and Plateau. Average association between ARs and annual maximum river runoff attains >80%, >50%, and <50% along the watersheds of the western flanks of the Coast Mountains, the Interior Mountains, and Interior Plateau, respectively. There is no significant change in AR-related extreme annual maximum runoff across BCSAK during 1979–2016. AR conditions occur during 25 out of 32 of the flood-related natural disasters in British Columbia during WYs 1979–2016. AR-related annual maximum runoff magnitude is significantly higher than non-AR-related annual maximum runoff for 30% of the watersheds studied. Smaller and steeper watersheds closer to the coast are more susceptible to AR-related annual maximum runoff than their inland counterparts. These results illustrate the importance of AR activity as a major control for the distribution of peak runoff in BCSAK. This work provides insights on the hydrological response of watersheds of northwestern North America to landfalling ARs that may improve flood risk assessment and disaster management in this region.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".