Historical changes in the annual number of large floods in near-natural catchments across North America and Europe, 1931-2010
Bibliographic record
Abstract
Previous investigations have analyzed historical changes in low magnitude floods, such as the annual peak flow, at regional or national scales. These investigations often use catchments where streamflows have been influenced by human alterations such as reservoir regulation or urbanization. No known studies have analyzed changes in large floods (greater than 25-year return period floods) at a continental scale for near-natural catchments. To fill this research gap, this study analyzed flood flows from reference hydrologic networks (RHNs) or RHN-like gauges in North America (United States and Canada) and Europe (United Kingdom, Ireland, France, Spain, Germany, Switzerland, Austria, Iceland, Norway, Denmark, Sweden, and Finland). RHNs are formally defined networks in several countries that comprise gauges with a natural or near-natural flow regime and that provide good quality data. \nSelected RHN-like gauges were included following a major effort to ensure RHN-like status through consultation with local experts. Some 1206 study gauges met near-natural and completeness criteria for 1961-2010 and 322 gauges met criteria for 1931-2010. Peak flows with recurrence intervals of 25, 50, and 100 years were estimated using the generalized extreme-value distribution and L-moments, and peak flows at each gauge that exceeded these flood thresholds in each year were compiled. Continental and regional trends over time in the annual number of large floods, including groups differentiated by catchment size and major Köppen-Geiger climate group, are being computed and will be presented at EGU. Plots will also show the decadal variability in the annual number of major floods. The unique dataset used for this study is an example of successful international collaboration on hydro-climatic data exchange, which is potentially a step towards establishing RHN or RHN-like networks on a global scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".