Modelling climate change impacts in the Peace and Athabasca catchment and delta: III—integrated model assessment
Bibliographic record
Abstract
Abstract This study utilized the hydrodynamic model, ONE‐D, coupled to the distributed hydrological model WATFLOOD, to evaluate the potential effects of a shift in climate on the hydrological regimes of three large lakes (Athabasca, Claire, and Mamawi), and two important sources of inflow (the Peace and Athabasca rivers) in the Peace‐Athabasca Delta (PAD). The coupled WATFLOOD/ONE‐D system was forced by current climatology and downscaled climate change scenarios from five selected general circulation models (GCMs). Under the selected climate change scenarios, water levels in Lakes Athabasca, Claire, and Mamawi peaked, on average, 40–50 days earlier than at present. Some GCM scenarios predicted an increase in peak lake levels while others predicted suppressed lake levels. Inter‐annual water level variability was also sensitive to predicted changes in precipitation and temperature, increasing in winter and decreasing in summer. Water level fluctuations in the major input rivers of the PAD were found to be more variable under the climate change simulations. Moreover, spring freshet peaks were estimated to occur earlier (20–30 days) and to be considerably reduced (up to 1·0 m reductions). Although the simulations converged towards the same general results in seasonality shift, flow level amplitude was GCM‐dependent. Simple downscaling methods may well be too coarse to adequately address the important spatial variations that can occur in the long‐term climate signal. It is therefore important to understand the sensitivity of this regime to local climatic influences to produce more reliable, quantitative results. An ensemble of approaches that provide meaningfully downscaled results should be considered to confirm the results presented. Copyright © 2006 Crown in the right of Canada, and John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".