Characterizing the Concurrent Occurrence of Tornadoes and Flash Floods Across Canada
Bibliographic record
Abstract
Compound weather extreme events, such as tornadoes and flash floods, can significantly impact societies and infrastructure systems. Disaster response agencies provide instructions to the exposed communities to retreat to safety specific to the natural hazard. However, the instructions can become confusing if natural hazards demand conflicting responses. This study characterizes the compound tornado and flash flood (TORFF) events to assess and predict the simultaneous occurrence probability of such hazards across Canada in the long term. We quantify dependencies between the tornadoes and flash floods using ground-based and reanalysis datasets. Tornado data are available based on the recorded Fujita rating for each event, and the corresponding wind speed values are determined through a resampling approach. The TORFF events are clustered and the bivariate probability distributions of the resampled windspeed and precipitation are characterized based on Copula. The corresponding individual and joint return levels are investigated under different scenarios (AND, OR, and conditional) across Canada. Results show positive dependencies between resampled windspeed and associated precipitation in Saskatchewan, followed by Alberta, Manitoba, Ontario, and Quebec (least dependency) regions. Higher dependencies between tornadoes and flash floods over regions such as Saskatchewan suggest that analyzing these events in isolation can underestimate the associated risks. Higher precipitation is also expected during extreme wind speed, as observed in the conditional assessment of precipitation given windspeed. This study provides insight for more realistic recurrence interval estimation for tornadoes and flash floods to aid in the evacuation decision-making process.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".