Regional frequency analysis of seasonal rainfall and snowfall for the southern interior of British Columbia
Bibliographic record
Abstract
Extreme precipitation events are rare in nature, but they can have a strong impact on society. Canada’s adaptation policies for these extreme events require the availability of precise, up-to-date quantile estimates that relate event magnitudes to probabilities of occurrence. This study developed seasonal maximum daily rainfall and snowfall quantile estimates for the southern interior of British Columbia. This area has complex precipitation patterns and proved to be an excellent site for studying extreme rainfall and snowfall. Instead of the more traditional at-site analysis using method of moment estimators, a regional frequency analysis approach based on L-moments was used. Seasonal rainfall and snowfall data were selected to provide a finer delineation of events and examine the variation in extreme precipitation type during the year. A serial dependence analysis done as part of the regional frequency analysis screening process concluded that overall there appeared to be no change in the frequency of seasonal extreme rainfall or snowfall events for the study area. Homogeneous regions were successfully developed using a cluster analysis and extensive manual refinement. These regions were overall geographically cohesive and reflected the local valley systems. A few regions with high elevation stations were geographically dispersive possibly due to the influence of the freezing level on precipitation type. Regional quantiles estimates with associated root mean square errors and 90% error bounds were developed for fall, spring and summer rainfall data and winter snowfall data. A comparison of select regional and at-site estimations showed quantiles for both methods were comparable, but almost all regional estimations had significantly lower root mean square errors by a factor of 1.5 to 5.0 when compared to at-site estimations. This comparison demonstrates that a regional frequency analysis develops quantile estimates more precise than at-site estimates for the southern interior of British Columbia.
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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 | 0.000 | 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".