Modelling Future Streamflow Extremes — Floods and Low Flows in Georgia Basin, British Columbia
Bibliographic record
Abstract
The Georgia Basin is one of the most hydrologically complex areas of Canada. Variations in temperature, precipitation and elevation influence the amount and form of water that drives streamflow in its rivers and streams. Climate change could have major regional effects on air temperature, precipitation, evapotranspiration, and ultimately runoff. In previous work, zones of homogenous hydrologic processes were delineated within the basin. Watersheds were separated into three types: rainfall-driven streams, snowmelt-driven streams, and hybrid (mixed rainfall- and snowmelt-driven) streams. Climate change was shown to have major regional effects on each type of watershed, affecting the amounts and patterns of runoff. In the current study we consider changes in extreme hydrologic events, floods and low flows, in these watersheds. Climate data downscaled from the Canadian Coupled General Circulation Model for future time periods are used as inputs to a hydrologic model optimized for mountain watersheds. The discrepancies between observed and modelled streamflows are examined. While the model reproduces central tendency measures well, there are significant biases in the ability of the models to reproduce extremes. Output from the hydrologic model is used to assess relative changes in the frequency, timing, and magnitude of floods and low flows between present and future (2020, 2050 and 2080) climate scenarios. The models suggest that frequency of floods will increase in all watersheds under the projected climate scenarios. In rainfall-driven streams, flood events increase in number, but not in magnitude. In hybrid streams, winter events occur more often while summer snowmelt flood events occur less often. In snowmelt-driven streams, the magnitude and duration of summer floods increase. Low flows in rainfall-driven streams maintain the same frequency and magnitude but occur over an extended period of time during summer. Hybrid streams show an increase in frequency, a decrease in magnitude, and a shift in time of occurrence of low flows to summer rather than winter. In snowmelt-driven streams, low flow events occur less often largely moderated by increased flow due to an overall increase in winter streamflow in a warmer climate.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".