Application of the British Columbia MetPortal for Estimation of Probable Maximum Precipitation and Probable Maximum Flood for a Coastal Watershed
Bibliographic record
Abstract
Estimation of the Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) are regulatory requirements in many jurisdictions that are used in the design of dams and assessment of existing infrastructure. The recently available British Columbia MetPortal provides regionally consistent PMP and precipitation frequency estimates across the province of British Columbia (BC). This paper proposes an approach to process and apply this data for the estimation of the PMF for watersheds across British Columbia. Guidelines are presented for selection of transposition points applicable to a watershed, and algorithms are developed for processing the geospatial probable maximum storm and precipitation frequency data. The algorithms developed are generic to multiple software and programming environments, and could also be applied in other regions where spatially and temporally intact PMP estimates are available. A detailed description of data sources and development of PMF scenario inputs is provided, as well as details of important sensitivity analyses. The methodology is applied to estimate the PMF for the Cheakamus Basin north of Squamish British Columbia. The application of the MetPortal PMP and precipitation frequency estimates, when used with a consistent PMF development methodology as proposed in this paper, will help improve the consistency of PMF estimates for watersheds across the province, offering a welcome improvement for dam owners and regulators.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.004 | 0.001 |
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".