The impact of climate change on seasonal floods of a southern Quebec River Basin
Bibliographic record
Abstract
Abstract Global warming predicted by general circulation models (GCM) is now a more and more generally agreed upon effect. The impact of climate change on summer and fall flooding on the Châteauguay River Basin (2500 km2), located at the southern end of the Quebec province (Canada), was investigated using results from the Canadian GCM (CGCM1) and a coupled hydrology–hydraulics model of the basin. Three 20‐year periods, corresponding to 1975–1995, 2020–2040 and 2080–2100, were used for the analysis. For each period, 24‐h precipitation depths corresponding to the 20 and 100‐year return periods were determined from a frequency analysis of the summer–fall maximum 24‐h precipitations using a general extreme value frequency distribution. 24‐h rainfall hyetographs were generated using region‐specific cumulative distributions provided by the Canadian Atmospheric Environment Service. These hyetographs were then used as inputs to the hydrology–hydraulics model to simulate hydrographs, maximum discharge and maximum water levels at two sections of the river. Results indicate potentially very serious increases in the volume of runoff, maximum discharge and water level with future climate change scenarios. The changes get more drastic as longer return periods are considered. Increases of up to 250% of the maximum water discharge are encountered and water levels are significantly higher than the current flood levels. If realistic, these scenarios indicate that important decisions will have to be taken to alleviate future increases in flooding damages in what is already a flood prone river. Copyright © 2001 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".