Unveiling spatiotemporal patterns of compound hydrological droughts and river heatwaves in Poland
Bibliographic record
Abstract
64 Polish rivers in Central Europe. Despite their profound ecological and societal impacts, the dynamics of compound droughts and heatwaves (CDHWs) in river systems remain a largely uncharted territory. This study provides the first comprehensive assessment of riverine CDHWs, utilizing long-term river discharge and water temperature datasets from 64 Polish rivers during the period 1966–2020. Our analysis reveals a marked increase in the severity and frequency of CDHWs across Poland. Specifically, 58 stations exhibit significant upward trends in CDHWs frequency (0.38 times per decade, p < 0.05). Notably, more than half of all stations demonstrate substantial changes in CDHWs characteristics: duration (4.27 days per decade, p < 0.05), maximum CDHWs-related heatwave intensity (0.50 °C per decade, p < 0.05), and maximum CDHWs-related drought intensity (-0.10 in Standardized Streamflow Index (SSI) per decade, p < 0.05). Moreover, CDHWs become severe with more high-level categories. Distinct regional patterns reveal that CDHWs are more frequent and severe in western Poland, while extreme CDHWs are prevalent in eastern Poland. These insights underline the necessity for integrating riverine CDHWs dynamics into global water management and climate adaptation frameworks. As the pioneering study on riverine CDHWs, this work provides a foundational reference for future investigations. • Polish rivers have experienced substantial increases in the frequency, intensity, and duration of CDHWs from 1966 to 2020. • Hydrological droughts increased in the severity and frequency for Polish rivers from 1966 to 2020. • CDHWs-related hydrological droughts and river heatwaves become severe with more high-level categories.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".