Compound hydrological and thermal extremes: A nonstationary risk modeling approach for riverine ecosystems
Bibliographic record
Abstract
The increasing frequency and severity of compound hydro-climatic extremes pose a growing threat to cold-water aquatic ecosystems. This study develops a nonstationary multivariate risk modeling framework to assess the joint behavior of extreme summer river water temperature (Tw) and concurrent low flow (Q) in six unregulated Atlantic salmon rivers in eastern Canada. A dynamic additive copula approach is employed to model both the structure dependence and nonstationarity, with time-varying effects modeled via large-scale climate oscillation indices (teleconnections) and a temporal trend representing climate change. The proposed joint nonstationary model ( J NS ) is benchmarked against a joint stationary model ( J S ) and a univariate nonstationary model ( U NS ). Results show that J NS systematically outperforms both alternatives across all study sites. Temporal trends significantly increased Tw extremes at most rivers, while teleconnections emerged as dominant drivers of variability. Negative phases of the Southern Oscillation Index (SOI, El Niño conditions) and the North Atlantic Oscillation Index (NAO) increase the variability of Tw and low-flow events, respectively, while positive phases of the SOI (La Niña conditions) and NAO are associated with elevated joint and conditional exceedance probabilities, rising by up to 66 % in the Restigouche River and 45 % in the Highland River. By linking joint extremes to both long-term warming and oscillatory climate patterns, the study provides a predictive framework for anticipating compound risks and protecting thermally sensitive aquatic habitats under ongoing climate change and variability. • A dynamic copula model is developed for joint summer elevated Tw and corresponding low Q. • Nonstationarity is driven by trends and teleconnections within the study region. • Negative SOI and NAO phases increase Tw and low flow variability, respectively. • Positive SOI and NAO phases increase joint and conditional exceedance probabilities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".