Evaluating the impact of climate change on water system vulnerabilities using multiple hydrological models 
Bibliographic record
Abstract
Warming climate is altering streamflow characteristics and posing pressure on water systems. Here, the impacts of climate change on a headwater system in Alberta, Canada, is evaluated, with the primary goal of understanding the role of hydrological system representation. For this purpose, a conceptual hydrological model, i.e., HBV-MTL, is coupled with two snowmelt estimation modules, i.e., Degree-Day and CemaNeige. The models are calibrated using point- and grid-based climatic data and considering lumped and semi-distributed representation of the basin and are linked to a water allocation model to simulate reservoir dynamics and downstream water deliveries. The bias-corrected outputs of 19 climate models during 2021-2099 are then used to estimate the future water system conditions. Results show that during the historical period, all models provide acceptable performance, with minor distinctions; however, their simulations highly divergence in the future period. The models unanimously project significant water deficit in meeting agricultural water demands and flood risk in the future. However, the quantified vulnerabilities depend on the considered hydrological models, among the utilized snow routine module highly influences estimated natural and regulated flow values. It is suggested to consider these projections and revise the Oldman reservoir water allocation plans to mitigate climate change's adverse impacts on this water system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".