Development of methods for regional flood estimates in the province of British Columbia, Canada
Bibliographic record
Abstract
Flood estimation in the province of British Columbia is often based on either single-site frequency analysis or graphical peak-flow regionalization procedures. These methods involve large uncertainties, especially at short-term record stations and ungauged sites, because of the vague selection of frequency distributions and delineation of homogeneous regions. To reduce these uncertainties and overcome the drawbacks of the current methods, an innovative regional frequency analysis was proposed in this study. L-moments were used for the three stages, namely delineating and testing homogeneous regions, identifying and fitting regional distributions, and developing regional functions for the transfer of information from gauged to ungauged watersheds. Based on the most recently available flood database, the province of British Columbia was divided into 19 homogeneous regions of which 14 are non-mixture regions and five are mixture regions. A mixture means in some years the annual floods are generated by one mechanism, while in other years they are generated by other physically different mechanisms. It was found that either the generalized logistic (GLOG) or the generalized extreme value (GEV) may be considered as the regional parent distribution for any of the non-mixture regions, whereas the non-parametric distribution can be used for the mixture regions. In the non-mixture regions, hierarchical approaches and regression models were developed for gauged and ungauged watersheds. For the hierarchical approaches, the first two parameters of the GLOG or GEV distribution were estimated from at-site data while the third parameter was from the region. For the regression models, the parameters of the GLOG or GEV distribution were regressed on the catchment size. In the mixture regions, a non-parametric method was combined with the regression method for the development of regional models. Monte Carlo simulation studies showed that the developed hierarchical approaches were substantially more accurate than the single-site methods, especially for long-term flood quantiles. In particular, it was shown that about three times more data were required for the single-site models to be as accurate as the developed hierarchical approaches. The proposed regression models were validated through split-sampling experiments. Statistical tests showed that the quantiles from the regression models were in good agreement with those from actual observations.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".