Water managers’ perspectives on reservoir operations for sustainable irrigation in Alberta
Bibliographic record
Abstract
Sustainable reservoir management is essential to ensure the productivity of agriculture and to adapt to a changing climate.Despite progress in reservoir modelling and management with the improvement of computer capabilities and the development of optimization methods, managers and decision-makers still face the challenge of applying the output of more-theoretical optimization models to real-world reservoir operations.This research analyzes reservoir managers' perspectives in Alberta's heavily-allocated South Saskatchewan River basin, in order to improve understanding of the behaviour of reservoir operators under different climatic and hydrological conditions.The method involves in-person interviews with twelve water managers of Southern Alberta's irrigation districts.The data collected suggest that seniority-based allocation priorities are generally not strictly applied.Instead, cooperation between districts and between irrigators within a district indicates that water allocations are driven principally by the infrastructure capacity on a river-basin-scale basis.Of additional importance is recognition of the "day-by-day" approach adopted by all water managers interviewed who will "never sacrifice today for tomorrow".Moreover, water managers do not apply annual or multi-year water deficit-distribution strategies, but instead impose variable water rationing for all irrigators at the beginning of a growing season.The contribution of this research is to provide real-world data and a better understanding of water managers' perspectives that may lead to more valuable outcomes from modelling studies, and results that may be more readily adopted by water managers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".