Quantifying projected changes in runoff variability and flow regimesof the Fraser River Basin, British Columbia
Bibliographic record
Abstract
Abstract. Canada's Fraser River Basin (FRB), the largest watershed in the province of British Columbia, supplies vital freshwater resources and is the world's most productive salmon river system. We evaluate projected changes in the FRB's runoff variability and regime transitions using the Variable Infiltration Capacity (VIC) hydrological model. The VIC model is driven by an ensemble of 21 statistically downscaled simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5), for a 150-year time period (1950–2099) over which greenhouse gas concentrations follow the CMIP5 Representative Concentration Pathway (RCP) 8.5. Using mean and standard deviation (variability) metrics, we emphasize projected hydroclimatological changes in the cold season (October to March) over different sub-basins and geoclimatic regions of the FRB. Warming consistent with the RCP8.5 scenario would lead to increased precipitation input to the basin with higher interannual variability and considerably reduced winter snowfall shortening the average snow accumulation season by about 38 %. Such changes in temperature and precipitation will increase cold season runoff variability leading to higher cold season peak flows. In the lower Fraser River, cold season runoff will increase by 70 % and its interannual variability will double compared to the 1990s, presenting substantial challenges for operational flow forecasting by the end of this century. Cold season peak flows will increase substantially, particularly in the Coast Mountains, where the peak flow magnitudes will rise by 60 %. These projected changes are consistent with a basin-wide transition from a snow-melt driven flow regime to one that more closely resembles a rainfall driven regime. This study provides key information relating to projected hydroclimate variability across the FRB, describes potential impacts on its water resources, and assesses the implications for future extreme hydrological events.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".