Regional flood frequency analysis based on peaks-over-threshold approach: A case study for South-Eastern Australia
Bibliographic record
Abstract
Southeast Australia Regional flood frequency analysis (RFFA) is a widely adopted statistical method to estimate design floods in ungauged catchments. Annual maximum flood (AMF) model is the most popular method in developing RFFA techniques. However, the AMF-based approaches are criticised for its limitations in the range of very frequent to frequent flood estimation. As an alternative, the peaks-over-threshold (POT) based approach has shown theoretical advantages in this flood range. POT based RFFA is currently underemployed internationally due to its complexity in modelling. This study develops POT-based RFFA techniques for south-eastern Australia using data from 151 catchments. A comparison is made between ordinary least squares (OLS) and weighted least squares (WLS) methods in developing POT-based RFFA techniques. The OLS based method is found to perform better than the WLS. The median relative error values of the developed prediction equations range 31–38%. The new POT-based RFFA technique overcomes the limitations of the current Australian Rainfall and Runoff, which does not have any RFFA technique for very frequent floods. It is expected that these new POT-based RFFA technique will be used in practice in south-east Australia.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".