River Thermal Dynamics and Heatwaves of Polish Rivers Under Climate Change
Bibliographic record
Abstract
Abstract Progression of global warming poses a significant risk to river ecosystems. However, how river heatwaves' characteristics across complex hydrological systems alter under climate change is still poorly understood. In this study, long‐term reconstructed daily river water temperatures (RWTs) from 125 hydrological stations in 70 rivers across Poland, were used. Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) method was used to track the abrupt changes of RWTs. Moreover, the characteristics of river heatwaves, including number, duration, intensity, and category, were evaluated. BEAST analysis revealed pronounced spatiotemporal variability in RWT trends in Poland, influenced by natural and anthropogenic factors. Notably, the maximum abrupt changes of RWT were observed during the 1980s and 1990s. Southern Poland, particularly mountainous regions, exhibited more pronounced river temperature changes and severe heatwaves compared to the milder northern regions. Our results also showed a statistically significant increase in frequency and intensity of river heatwaves at 121 out of the 125 studied stations (p‐value < 0.05), which were consistent with the warming of air temperatures. For all the designated stations, the majority of river heatwaves belonged to the category “moderate,” followed by “strong,” “severe,” and “extreme.” Number, duration, and intensity of the river heatwaves were highly correlated with air temperatures, with the correlation coefficients being 0.624, 0.631, and 0.604, respectively. Our findings further suggest that mitigation measures shall be taken to reduce the effects of climate warming on Polish river ecosystems, especially under low flow conditions which are more vulnerable to the intensified river heatwaves.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".