Water Budget Input Linked to Atmospheric Rivers in British Columbia's Nechako River Basin
Bibliographic record
Abstract
ABSTRACT This study explores the contribution of atmospheric rivers (ARs) to the water budget input of the Nechako River Basin (NRB) in British Columbia (BC), western Canada. The study quantifies the fraction of precipitation, rainfall, snowfall, and snow water equivalent (SWE) associated with ARs at multiple scales and tests for trends using the Mann–Kendall (MK) test. AR‐related totals for 1950–2021 were created by linking AR events to water budget input variables of the ERA5‐Land reanalysis product on a daily scale. Associations with different phases of the El Niño‐Southern Oscillation (ENSO) climate pattern and AR‐related contributions to the NRB are also investigated. Results indicate an increasing fractional contribution of rain in ARs landfalling in the NRB in the last two decades (2000–2019). Moreover, 21% of the total annual precipitation in the NRB is associated with ARs, with decreasing contributions from west to east. October has higher AR‐related total precipitation than other months, while March, May and June are the least affected. ARs contribute disproportionately more to mid‐ and high‐intensity daily precipitation totals, and provide up to 45% and 24% of the seasonal rainfall and snowfall, respectively. AR‐related SWE is relatively higher in autumn due to the increased frequency and intensity of ARs, resulting in a greater fractional contribution of ARs to the snowpack compared to winter. ARs influence snowpack accumulation during fall (18%) and winter (13%) but also increase the risk of natural hazards. The MK test for AR‐related water budget variables on the annual scale identified no significant trends. However, AR‐related snowfall shows decreasing trends in the NRB, more specifically in the Upper Nechako, Lower Nechako and Stellako sub‐basins during the summer. Over the study period, ARs consistently contribute up to one‐fifth of the annual input to the NRB's water budget. This study provides the first quantitative assessment and trend analyses of AR contributions to the water budget input of a reservoir‐regulated watershed in north‐central BC, yielding valuable information for hydropower production, ecological flows, irrigation, domestic and industrial water use.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; both teacher heads agree on what is shown here.
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".