Design flood estimation in flood hazard studies: a three-decade systematic review of practices in Canada
Bibliographic record
Abstract
Design floods for flood hazard studies are often estimated through flood frequency analysis (FFA). However, many decisions involved may lead to inconsistencies. There is also a demand to incorporate climate change into FFA, but guidelines are lacking. Although these challenges are acknowledged, limited literature documents how they manifest in practice. We systematically reviewed design flood estimation practices in Canada based on 75 technical reports from 1990 to 2023 across six provinces (with 65% from Alberta due to availability). We found substantial variations in data pre-processing, assumption validation (distribution homogeneity and stationarity, and regional homogeneity), FFA methods (e.g. candidate distributions, parameter estimation, distribution selection, uncertainty quantification), software, and post-processing. While recent reports have increasingly disclosed key methodological details, several decisions remain subjective. Floods are typically assumed to be stationary and homogeneous, overlooking human-induced impacts and multiple flood-generating mechanisms. Nonstationary and mixture FFA remain seldom applied. Climate change considerations are often limited to commentary sections, and climate-adjusted design floods are only mandatory in some jurisdictions (e.g. British Columbia). Open software is commonly used for FFA, but its limitations have increased reliance on proprietary software. Thus, there is a need for improved consistency, realism, and research-to-practice translation in FFA practices.
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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 | 0.071 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.036 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".