Flood seasonality and generating conditions in the Tay catchment, Scotland from 1200 to present
Bibliographic record
Abstract
The new maximum recorded river flows in Scotland since 1988 have triggered widespread interest in whether floods are becoming more frequent and in the conditions that generate floods of different magnitudes and frequencies. There are questions about the longer‐term variability in flood‐generating characteristics, and whether there are past analogues for present hydroclimatic variability. The present paper builds on previous work reconstructing a detailed historic flood chronology for the Tay, the largest catchment in Scotland, and its tributaries over the past 800 years, extending the gauged discharge record (1952 onwards). It categorizes flood‐generating factors in the Tay catchment and analyses the hydro‐meteorological conditions that have generated extreme and moderate floods over a historical period. This work is placed in a broader literature context of historical ‘climaxes of storminess’, periods of higher storm frequency, flood patterns observed in Scotland and Europe during the Little Ice Age and longer‐term rainfall and temperature patterns. The paper concludes that the variability in flood‐generating characteristics is highly dependent on the timescale of observation. Inevitably the relative dominance of winter and early spring flooding can vary from year to year and within specific time‐periods, but so can the level of augmentation of the flood series with summer and autumn floods to produce notable ‘flood years’ and flood clusters. The Tay provides a good ‘all‐Scotland surrogate’ for historical flood patterns, reflecting its gathering areas in eastern and western Scotland. The value of a historical approach to the assessment of flood seasonality and generating characteristics is clearly demonstrated.
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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 | 0.000 | 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".