A Global Analysis of Daily Streamflow Data to Unravel the Heaviness of Flood Distribution
Bibliographic record
Abstract
Hydroclimatic extremes, such as floods, present complex challenges in risk assessment due to their spatial and temporal compounding nature. This research aims to improve our understanding and modelling capabilities by investigating the complex interactions among record length, flow regime, and upper tail of floods. Our study resolves conflicting results in prior studies by utilizing a quasi-global peak-over-threshold (POT) analysis of flood with the Generalized Pareto (GP) distribution. Based on an analysis of 4,482 streamflow series over six different regime types with record lengths ranging from 30 to 213 years, our results show a strong relationship between the GP shape parameter and record length. The results show that the variance of the shape parameter of GP distribution diminishes with record length, and it eventually converges to a single value depending on the flow regime. We show that the shape parameter of snow-dominated streams is the lowest, whereas intermittent streams have the highest. Our research reveals regime-specific patterns in the impact of hydroclimatic and catchment controls on flood tails, underscoring the necessity of regime-specific strategies for flood risk management. Identifying catchments that are more likely to experience extreme flooding provides useful information for determining which mitigation measures to prioritize.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".