Assessment of Future Changes in Intensity-Duration-Frequency Curves for\n Southern Ontario using North American (NA)-CORDEX Models with Nonstationary\n Methods
Bibliographic record
Abstract
The evaluation of possible climate change consequence on extreme rainfall has\nsignificant implications for the design of engineering structure and\nsocioeconomic resources development. While many studies have assessed the\nimpact of climate change on design rainfall using global and regional climate\nmodel (RCM) predictions, to date, there has been no comprehensive comparison or\nevaluation of intensity-duration-frequency (IDF) statistics at regional scale,\nconsidering both stationary versus nonstationary models for the future climate.\nTo understand how extreme precipitation may respond to future IDF curves, we\nused an ensemble of three RCMs participating in the North-American (NA)-CORDEX\ndomain over eight rainfall stations across Southern Ontario, one of the most\ndensely populated and major economic region in Canada. The IDF relationships\nare derived from multi-model RCM simulations and compared with the\nstation-based observations. We modeled precipitation extremes, at different\ndurations using extreme value distributions considering parameters that are\neither stationary or nonstationary, as a linear function of time. Our results\nshowed that extreme precipitation intensity driven by future climate forcing\nshows a significant increase in intensity for 10-year events in 2050s\n(2030-2070) relative to 1970-2010 baseline period across most of the locations.\nHowever, for longer return periods, an opposite trend is noted. Surprisingly,\nin term of design storms, no significant differences were found when comparing\nstationary and nonstationary IDF estimation methods for the future (2050s) for\nthe larger return periods. The findings, which are specific to regional\nprecipitation extremes, suggest no immediate reason for alarm, but the need for\nprogressive updating of the design standards in light of global warming.\n
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".