Climate-driven trends in the occurrence of major floods in North America and Europe
Bibliographic record
Abstract
Every year floods cause much damage around the world. It is important to understand historical changes in major floods to help inform how floods may change in the future. To date there is very limited evidence of past changes in major-flood magnitude and occurrence (floods exceeding 25, 50, and 100 year return-period thresholds). Many studies have analyzed annual-maximum flood trends (which primarily represent minor floods) and they have often not differentiated between trends influenced by human catchment alterations and those caused by climatic changes. Here we present an intercontinental assessment of historical climate-driven trends in major-flood occurrence using many diverse but minimally altered catchments. Based on our results, there is no compelling evidence for consistent changes over time in major-flood occurrence across North America and Europe. Flood occurrence, based on all 1206 gauges in our study, increased from 1961 to 2010 but not significantly, driven primarily by European increases; there was no change overall in the occurrence of major floods in North America (645 gauges). There were non-significant increases in flood occurrence from 1931 to 2010, based on all 322 longer-record gauges in our study, driven primarily by North American increases. There were significant increases from 1931-2010 in the occurrence of 25-year floods in North America but this appears to be driven by a relatively low occurrence of floods in the 1930s and 1940s. There were a few significant flood-occurrence increases and decreases for subgroups of gauges for both time periods, differentiated by catchment size, type of climate, flood threshold, and period of record. Past changes in major-flood occurrence are complex and future changes are also expected to be complex. International hydrologic networks containing minimally altered catchments will play an important role in understanding these complexities for both historical and future climatic conditions.
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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 | 0.001 |
| 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".