Significant Rise in Sava River Water Temperature in the City of Zagreb Identified across Various Time Scales
Bibliographic record
Abstract
The study analyzed available data series of the Sava River’s water temperature measured at the Zagreb gauging station. Official data from the Croatian Meteorological and Hydrological Service (DHMZ) in Zagreb were utilized. Over the 73 years from 1948 to 2020, there are only 53 years with complete measurement records. Despite this limiting fact, it was considered important to analyze the behavior of the Sava River’s water temperatures in Zagreb over the past 70 years, during which a significant increase in air temperatures has been observed in the region, particularly in the city of Zagreb. Analyses were conducted on the characteristic (minimum, mean, and maximum) water temperatures over timescales of years, months, and days. The relationship between water temperatures (TW) and air temperatures (TA) measured at the Grič Observatory and the flows (Q) of the Sava River in Zagreb were investigated. A trend of rising water temperatures was observed throughout the entire period from 1948 to 2020, with the intensity significantly increasing in the recent period starting from 1988. The trend of rising air temperatures is the primary driver of the increase in the water temperatures of the Sava River in Zagreb. However, the impact of reduced water flow, especially during the warm season, should not be overlooked. This effect is amplified by the observed trend of decreasing minimum flows of the Sava during the warm season, from June to September. As a result, the most significant rise in water temperatures of the Sava River in Zagreb occurs during prolonged low-water summer periods, particularly in July. A strong trend has been observed in the increasing number of days per year with mean daily water temperatures of the Sava River exceeding 20 °C. This higher water temperature occurs increasingly earlier in the year, lasts longer, and ends later, often extending into September.
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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) | 0.001 | 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".