Bibliographic record
Abstract
Atmospheric rivers (ARs) are synoptic-scale atmospheric phenomena that transport moisture from the (sub)-tropical regions to the mid-latitudes globally. They play a substantial role on water resources of British Columbia and southeastern Alaska (BCSAK). However, understanding of their climatology and impacts on the hydrology of this region remains limited. I use a combination of a regional AR catalog, reanalysis datasets, gridded precipitation, observed river runoff data, and topographic information, to provide insights on the climatology of landfalling ARs (LARs) and to quantify changes in the contribution of LARs to the precipitation, river runoff, and their extremes in BCSAK. Each year BCSAK experiences, on average, 35±5 LARs with the highest number in autumn (13±2) and an average duration of 2±1.8 days. The 1979-2016 average annual counts of LARs increase by ~20% across BCSAK. Slightly higher numbers of LARs occur during the neutral phase of El Niño-Southern Oscillation, the positive phases of the Pacific Decadal Oscillation and the Pacific-North American Pattern, and the 2013/2014 warm anomaly of the Northeastern Pacific. LARs contribute 13% (spatial range: <5-33%) and 36% (spatial range: <5-97%) of annual total and extreme precipitation, respectively, across BCSAK with higher values over elevated terrain. AR-related precipitation days increase during 1979-2012; however, no change occurs in the average AR-related precipitation amount for most of BCSAK. LARs contribute 14±6% (spatial range: 2%-29%) and 48±24% (spatial range: ~10%-100%) of the total annual and annual maxima runoff in the watersheds of BCSAK. ARs control the distribution of peak runoff in most of BCSAK with >60% of the 168 watersheds analyzed having >5 of the top 10 annual maxima runoff associated with them. My work presents a baseline assessment of the AR regional climatology in BCSAK, provides the proportion of hydrological processes attributed to ARs, and assesses changes in those linkages over time. This work is useful to associate future climate model simulations, extreme weather forecasting, seasonal predictions, and water issues including flood mitigation, hydro-power generation, industrial water use, and ecological, recreational, and cultural water values of western Canada where rapid climate-induced hydrological changes are occurring.
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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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".