Spatiotemporal pattern of successive hydro-hazards and the influence of low-frequency variability modes over Canada
Bibliographic record
Abstract
Successive hydro-hazards, particularly dry and wet weather whiplash, can severely impact societies and infrastructure undermining the resilience measures developed based on the traditional univariate approaches. Understanding the spatial and temporal characteristics of such lagged compound events is important to improve the efficacy of disaster risk measures (DRMs). This study analyzes the successive dry-wet and wet-dry (SDW/SWD) spells based on the run theory across Canada and proposes a compound magnitude index to identify the corresponding hotspots across 268 watersheds for the historical period of 1963–2012. The frequency, transition time, and magnitude of SDW/SWD events are assessed considering the variable monthly 20th percentile (dry spell) and a fixed 90th percentile (wet spell) of natural streamflow values as thresholds. The potential influence of low-frequency variability modes such as Multivariate El Niño–Southern Oscillation (ENSO) index (MEI) and the North Atlantic Oscillation (NAO) index on the regional hydro-hazards, with a transition time of less than a month, is assessed based on the Bayesian quantile regression approach. Results indicate that the coastal areas, including the west and east coast and the Great Lakes region, are the hotspots for such abrupt transitions. Furthermore, the spatial and temporal characteristics of SDW/SWD events are largely variable and different from those of individual events. The magnitude of these events is influenced by low frequency variability modes particularly at higher quantiles, suggesting that strong phases of MEI and NAO can potentially lead to SDW and SWD events. The findings of this study support the future development of robust DRMs and effective water resource management strategies.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".