Canadian <scp>RCM</scp> projected changes to high flows for Québec watersheds using regional frequency analysis
Bibliographic record
Abstract
ABSTRACT Information related to changes in streamflow characteristics is important in the management and future planning of various water resources‐related projects in the context of a changing climate. In this study, projected changes to selected return levels of high flows for 21 watersheds, located mainly in the Québec province of Canada, are assessed following regional frequency analysis (RFA). This assessment is based on a ten‐member ensemble of Canadian regional climate model (CRCM) transient climate change simulations, of which five correspond to current 1970–1999 period and the remaining five are the matching simulations for the future 2041–2070 period. This is the first comprehensive study on high‐flow characteristics for the entire 21 watersheds in the context of a changing climate using the RFA approach, which allows better estimation of return levels, particularly for higher return periods, than single site/grid‐cell based analysis. Validation of the European Centre for Medium‐Range Weather Forecasts Re‐Analysis‐driven CRCM simulated 5‐, 10‐, 30‐ and 50‐year return levels against those observed at selected gauging stations suggests a general overestimation, with average differences of 9 and 12% (3–5%) for the 5‐ and 10‐year (30‐ and 50‐year) return levels, respectively. Results of ensemble averaged projected changes to regional return levels show increases for most of the watersheds. In particular, changes to 5‐ and 10‐year (compared to 30‐ and 50‐year) regional return levels are found to be statistically significant more often for northern watersheds compared to the rest. An evaluation of confidence in projected changes to selected regional return levels, based on five current/future period simulation pairs, reveal a higher level of confidence for northern watersheds. When regional level projected changes are developed at the CRCM grid‐cell level, a pattern similar to that for regional level changes is noticed, though with slightly increased range of changes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".