Assessing water system vulnerabilities under changing climate conditions using different representations of a hydrological system
Bibliographic record
Abstract
Changes in climate are altering the historical characteristics of the streamflow regime and affecting the performance of water systems. Here, the role of representing natural streamflow conditions in quantification of water system vulnerability under changing climate is evaluated in the Oldman River Basin, Canada. Four hydrological models are developed using point- and grid-based climate data and considering lumped and semi-distributed representations of the watershed. These hydrological models are then coupled with a reservoir water allocation model. Using an ensemble of climate model projections fed into these integrated models, changes in the water system’s behaviour are evaluated. Although intensified and earlier peak flows and more critical water deficits are projected, the estimated risks of failure strongly depend on the considered hydrological model configuration. The divergence among models’ projections for water deficit can be as high as 300%. Therefore, usage of all configurations is recommended to revise the reservoir operational policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 teacher head, 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".