The effects of the management of Lake Diefenbaker on downstream flooding
Bibliographic record
Abstract
The impact of management of Lake Diefenbaker, Saskatchewan, the Canadian Prairies’ largest reservoir, on downstream flooding is examined over three large inflow events in 2005, 2011 and 2013. The reservoir stores inflows for water supply, recreational and ecological purposes and preferentially releases water in mid-winter for hydroelectricity generation. It can also have an important role in downstream flood mitigation. The analysis shows great uncertainty in inflows due to ungauged local inflows, and substantial errors in the mass balance of the reservoir associated primarily with high inflows. The management of the reservoir has been challenged by declining spring inflows since the 1960s with a trend for increasing minimum annual reservoir elevations over time. Management of the reservoir has undergone changes since 2011 to lower the minimum elevation so as to achieve the 1 July target level, resulting in very different effects on flooding in 2005, 2011 and 2013. In all years, the management of the reservoir reduced the maximum flooded area upstream of Saskatoon and the integrated flooded duration and area. In 2005 the area of flooding upstream of Saskatoon was reduced for all durations and in 2013 for durations shorter than 5 days, but in 2011 it was increased for durations between 5 and 20 days. Flooding on the Saskatchewan River, i.e. downstream of the confluence of the North and South Saskatchewan Rivers, was increased in 2011 due to the delay in the timing of the peak flow induced by reservoir operation. Elimination of downstream flooding in these years would have required continuous adjustment of outflows to optimize storage, which itself requires modelling the inflow hydrograph. Future operation of the reservoir should adopt such optimization and modelling, whilst considering the impact of non-stationarity due to climate change.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".