Constructing Intensity-Duration-Frequency Curves Under Changing Climate in Canada Using CMIP5 and CMIP6 Climate Simulations
Bibliographic record
Abstract
RÉSUMÉ: Depuis la fin du 19ième siècle, le monde est soumis au phénomène de réchauffement climatique, étroitement lié aux activités humaines telles que l'industrie, l'agriculture et le transport, émettrices de gaz à effets de serre. Parmi les conséquences de la hausse des températures terrestres, l'augmentation de la capacité de l'atmosphère à emmagasiner de l'eau, ainsi que l'accélération de l'évaporation sont à la base de nouveau régimes de précipitations. Les pluies extrêmes, plus spécifiquement, deviennent de plus intenses et fréquentes dans de nombreuses régions dans le monde. Le Canada n'est pas une exception, alors qu'il y est prévu un réchauffement plus intense que la moyenne. De plus, la population du pays connaît un fort accroissement, particulièrement dans les villes, qui deviennent plus vulnérables face aux catastrophes naturelles telles que les inondations urbaines. Les courbes Intensité-Duré-Fréquences (IDFs) sont généralement utilisées par les ingénieurs pour estimer statistiquement les caractéristiques de ces pluies sur les lieux d'études. Pour ce faire, les caractéristiques locales des courbes sont souvent utilisées pour le design et la maintenance d'infrastructures. Cependant, le changement climatique affecte les valeurs d'IDF au Canada et il est urgent de les mettre à jour en fonction des projections de pluies extrêmes dans le futur pour assurer la sécurité de projets, mais également afin de réduire la vulnérabilité de la population concentrée en zone urbaine. D'autre part, ces événements extrêmes entraînent des coûts de dédommagement et de réparations en forte croissance sur le territoire. ABSTRACT: Global warming is a worldwide growing threat induced by human activities since late 19th century. One of the consequences of increasing air temperature is the augmentation of the atmosphere's water holding capacity and acceleration of evaporation, which affect formation of extreme rainfalls and their spatial and temporal distributions. Specifically, extreme rainfalls are becoming more intense and frequent in many regions of the world. Canada is not an exception as global warming is expected to be more severe than the global average. Larger amounts of annual rainfalls are already observed, accompanied with intensifications of extreme rainfall events. As the population is growing in the country, particularly in the cities, they become more vulnerable to extreme rainfall disasters such as urban flooding. The Intensity-Duration-Frequency (IDF) curves are typically used by engineers to statistically estimate the characteristics of extreme rainfall at a given location. The historical characteristics of IDF curves are often used for design and maintenance of various urban infrastructures. However, changes in climate can modify the IDF curves in Canadian cities. Therefore, it is urgent to update the IDF curves accounting projected precipitation to ensure the safety of future projects and lessen the vulnerability of the urban population. Moreover, these extreme events lead to increasing compensation and repair costs in the regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".