HISTORICAL EVOLUTION OF FLOODING DAMAGE ON A USA/QUEBEC RIVER BASIN<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT:There is a general belief in the public eye that extreme events such as floods are becoming more and more common. This paper explores this hypothesis by examining the historical evolution of annual expected flooding damage on the Chateauguay River Basin, located at the border between the United States and the province of Quebec, Canada. A database of basin land use was constructed for the years 1930 and 1995 to assess anthropogenic changes and their impact on the basin's hydrology. The progressive modification of the likelihood of a flooding event over the same period was then investigated using homogeneity and statistical tests on available hydrometric data. The evolution of the annual expected flooding damage was then evaluated using a coupled hydrologic/hydraulic simulator linked to a damage analysis model. The simulator and model were used to estimate flooding damage over a wide range of flooding return periods, for conditions prevailing in 1963 and 1995. Results of the analysis reveal the absence of any increasing or decreasing trend in the historical occurrence of flooding events. However, a general increase in the annual expected flooding damage was observed for all studied river sections. This increase is linked to an historical increase in damages for a given flooding event, and is the result of unbridled construction and development within the flood zone. To assess for future trends, this study also examined the potential impacts linked to the anticipated global warming. Results indicate that a significant increase in seasonal flooding events and annual expected flooding damage is possible over the next century. In fact, what is now considered a 100‐year flooding event for the summer/fall season could become a ten‐year event by the end of this century. This shows that potentialfutureimpacts linked to climate change should be considerednowby engineers, land planners, and decision makers. This is especially critical if a design return period is part of the decision making process.
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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 | 0.000 | 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) | 0.001 | 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 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".