Environmental flow assessment in the context of climate change: a case study in Southern Quebec (Canada)
Bibliographic record
Abstract
Abstract Through a case study in Southern Quebec (Canada), the assessment of environmental flows in light of the effects of climate change is investigated. Currently, the 7Q2 flow metric (7-day average flow with a 2-year return period) is used for water abstraction management. Several flow metrics were calculated using flow time series simulated by a deterministic hydrological model (HYDROTEL) and climate change scenarios as inputs. Results were compared within homogeneous low flow regions defined using ascendant hierarchical clustering, for the 1990, 2020 and 2050 horizons and annual, summer and winter periods. The impact of each flow metric on the potential availability of physical habitat was analyzed using the wetted perimeter as a proxy. Results indicated that: (1) the increasing non-stationarity of simulated flow data sets over time will complicate the use of frequency analysis to calculate the 7Q2 flow metric; (2) summer low flow values are expected to be lower than winter low flows; and (3) flow-duration curve metrics like the LQ50 (median discharge value of the month with the lowest flow) may become relevant environmental flow metrics by 2050. Results question current water abstraction management tools and permit us to anticipate future local and regional issues during low flow periods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".