Extreme Hydrological Drought in Southern Quebec (Canada): Natural and Anthropogenic Factors Interactions on its Spatio-Temporal Variability
Bibliographic record
Abstract
Despite the relatively humid character of their climate, many cold temperate regions face increasingly extreme hydrological droughts that turn rivers from steady to intermittent.The spatio-temporal variability of these droughts depends on the interaction of natural and anthropogenic factors.The objective of this study is to determine these factors in southern Quebec (Canada).The annual daily minimum flows (1-day) of 17 rivers, grouped into three homogeneous hydroclimatic regions, were analyzed during the period 1930-2019.With regard to spatial variability, the comparison of the flow averages by means of ANOVA and Kruskal-Wallis tests revealed that the flows of rivers of the north shore (southwestern hydroclimatic region) are on average twice as higher than those of the southern shore (Southeastern and Eastern hydroclimatic regions).Linear correlation analysis revealed that these flows are negatively correlated with agricultural areas and snowfall but positively with wetlands areas and watershed mean slopes.As for temporal variability, the southeastern region river flows, the most agricultural, have increased significantly over time due to the significant decrease in agricultural areas and the increase in the amount of rainfall.In the eastern hydroclimatic region, flows have generally decreased due to the significant decrease in the autumn snowfall.Finally, in the southwestern hydroclimatic region, the less agricultural, overall, no significant change in flows occurred, probably due to the low amount of fall snowfall, the decrease in which was offset by the increase in rainfall.This study reveals that the eastern hydroclimatic region is the most vulnerable to the decrease in the amount of snow, which is the main source of low flows in Quebec.Finally, few significant correlations were observed between climatic indices and flows.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".