Flood seasonality‐based regionalization methods: a data‐based comparison
Bibliographic record
Abstract
Abstract Improving techniques of flood frequency estimation at ungauged catchments is one of the major challenges for hydrologists, especially in arid and semi‐arid regions with insufficient information. Recently, popularity of flood seasonality‐based descriptors has increased among hydrologists for delineating of hydrologically homogenous regions. This study presents a data‐based comparison of three well‐known flood seasonality‐based regionalization methods in Halilrud basin in southeastern Iran. A Jack‐knife procedure is used to assess the performance of the methods for flood quantile estimation at ungauged sites. The results of these three seasonality‐based methods are compared to those results obtained from two alternative methods: a traditional regionalization method based on catchment's hydrogeomorphic characteristics similarity and a scenario that uses all available information without subdividing area. The results indicate that although peak over threshold (POT) approach which uses all flood events during year leads to better performance than other methods, but applying POT data series in a specific season (critical season) based on POT‐CS approach leads to better homogenous region and improves flood quantile estimation at ungauged sites. Copyright © 2011 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".