Trends, teleconnections and nonstationary frequency modeling of riverine heatwaves in North American Atlantic salmon rivers (1979–2100)
Bibliographic record
Abstract
Freshwater ecosystems play a vital role in supporting cold-water species like Atlantic salmon, providing the essential conditions to complete critical life stages. However, climate change is increasingly disrupting these habitats, driving shifts in river hydrology and rising water temperatures that threaten their survival. This study quantifies the past (1979-2020) and assesses potential future changes (2030-2100) in the hydrological and thermal regimes of 35 Atlantic salmon rivers across northeastern North America. According to the selected climate change scenarios, the results reveal a significant potential rise in water temperature (Tw > 20 °C), drought conditions, and an increasing trend in heatwave frequency, duration, and intensity by the end of the century. Under the most pessimistic SSP5-8.5 scenario, the total duration of summer riverine heatwave (RH) averaged from 2061 to the end of the century is projected to rise 12-fold (98.0 days) compared to the average of 8.5 days, calculated from 1979 to 2020. Consequently, nearly half of the studied rivers are projected to enter a permanent state of summer heatwaves. We also propose a nonstationary riverine heatwave frequency (RHF) modeling framework, integrating both climate change through a temporal trend and climate variability through large-scale atmospheric-ocean oscillations (teleconnection indices) as covariates. Models incorporating climate-related covariates outperform stationary models without covariates, with the North Atlantic Oscillation and the Arctic Oscillation indices emerging as the most influential predictors. However, with the small sample sizes used in this study, the uncertainty can increase especially for extreme non-exceedance probabilities and for the most extreme values of the climate indices.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".