Atmospheric rivers in British Columbia's Nechako River Basin: Variability, trends and hydrological impacts
Bibliographic record
Abstract
This dissertation provides a comprehensive analysis of atmospheric rivers (ARs) and their impacts on the Nechako River Basin (NRB) in British Columbia (BC), Canada. The study is divided into three main components: (1) the spatio-temporal distribution and trend analyses of AR types impacting the NRB, (2) AR contributions to the NRB's water budget input and their trends, and (3) the synoptic setting and hydrological responses in the NRB to three exceptional AR events. Initially, the spatio-temporal distribution and trends of ARs of various categories were examined. The study found a notable shift from mid- to low-intensity AR types in several sub-basins of the NRB. This shift suggests a potential impact on the regional water budget, as lowerintensity ARs are mostly beneficial and less likely to cause hazardous impacts but may also bring less water vapour to precipitate over the NRB. Spatial analysis revealed that the western and northern parts of the NRB are most affected by ARs, mainly in the fall and winter, with November experiencing the highest average AR intensity for the region. Moreover, the contributions of ARs to the NRB's water budget and their trends were assessed. AR days impacting the NRB are estimated at ~35 AR days yearly, on average, accounting for approximately one-fifth of the total annual precipitation, bringing predominantly rain in the fall and a mix of rain and snow in winter. Additional analyses indicated increasing trends in total precipitation linked to low-intensity ARs in the northern and western sectors of the NRB. The study highlights significant spatial variations in AR contributions to the NRB’s hydrological cycle, impacting total precipitation, snowpack formation, runoff, and the overall regional water budget. The synoptic setting and hydrological responses in the NRB to three exceptional AR events that occurred in 1952, 1978, and 2009 were also explored. These exceptional AR events depict the dual role of ARs in contributing to water replenishment while causing natural hazards. The exceptional ARs significantly contributed to snowpack formation and water replenishment to the Nechako Reservoir, averaging total precipitation accumulations of 81 mm (1.14 km3) in the Upper Nechako, highlighting their importance for regional water storage. However, these events also underscored the potential for flash floods and landslides, particularly during rain-on-snow conditions or when ARs make landfall on saturated soils and steep terrain. The findings of this dissertation emphasize the critical role of ARs in shaping the water budget of the NRB. The increasing frequency of some AR types, while others are diminishing, shows the changing nature of ARs, which may require adaptive water management strategies to balance the beneficial and detrimental impacts of these river-shaped storms. Enhanced understanding of AR dynamics and their impacts can inform the development of more effective flood prevention, water storage, and resource management practices, ensuring sustainable water supply for various stakeholders. The insights gained are particularly valuable for managing the Nechako Reservoir, where accurate water management is crucial for balancing agricultural, industrial, residential, and ecological needs in central BC.
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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 | 0.002 | 0.005 |
| 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) | 0.002 | 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".