Time of emergence of extreme floods and droughts over the north-eastern United States
Bibliographic record
Abstract
Natural climate variability is known to be an important source of uncertainty in climate risk assessment. This can pose a substantial obstacle to the implementation of adaptation strategies because it may mask the signal of climate change. In this study, the authors investigate how extreme flows in 133 catchments in the eastern and northeastern United States are affected by internal climatic variability. They evaluate the ratio of internal climate variability to anthropogenic climate change on projected future extreme streamflow using temperature and precipitation data from a single model initial-condition large ensemble (SMILE) at high spatial and temporal resolution. To better understand the role of internal climate variability and its impacts on the climate change signal, the authors use three different parametric and non-parametric tests to evaluate the time of emergence (TOE) of the climate change signal. The results are presented for three classes of catchment area: small (500 km2), medium (500-1000 km2), and large (>1000 km2). The findings suggest that the future intensity of both floods and droughts will gradually increase, with the expected increases in flood and drought signals being strongly influenced by catchment size. Small catchments are likely to see higher increases in flooding than the other catchment sizes, but weaker increases in extremely severe droughts. The size of the catchment also affects TOEs, with smaller catchments seeing earlier TOE for floods and later ones for droughts. These findings provide significant information on adaptation timelines.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".