Sensitivity Analysis of Environmental Flow Rule Curves for Water Allocation Optimization: Case Study, the Upper Oldman River Basin, Alberta, Canada
Bibliographic record
Abstract
Water allocation models use a set of decision rule curves to define constraints and priorities for optimal allocation of limited water supply to all water demands and requirements at the river basin scale. These rules are necessary to achieve optimal tradeoff between different river basin management goals (e.g. environment protection, urban development, agricultural expansion). However, many operating rules need to be modified as water demands and river basin priorities change over time due to socio-political forces and climate change. In particular, environmental flow criteria are of major interest; they can raise challenges for resource allocation, and represent an important interface between societal values and operational policy. In this research, a sensitivity analysis of alternate environmental flow rule curves (EFRCs) is examined for different hydrologic regimes. EFRCs are developed using hydrologic methods including flow duration curves, baseflow separation, and Tessman methods. The Water Resources Management Model (WRMM), developed by Alberta Environment, is used in this research. The Upper Oldman River Basin, one of the headwaters of the trans-boundary South Saskatchewan River Basin (SSRB) in western Canada, is used as a case study. The main objective of this research is to develop different EFRCs to understand their sensitivities to alternative future scenarios. All three operational EFRCs are compared using a group of performance criteria: reliability, resilience, and vulnerability. Results show that water allocation performance is very sensitive to the different EFRCs. The Q90 rule curves are superior to the other rules in several respects, and provide optimal trade-off between different water demands including environmental flow and junior irrigation sectors. However, further work is required to evaluate their ecological impact for practical application.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".