Assessing Recent Changes in the Contribution of Rainfall and Air Temperature Effects to Mean Flow and Runoff in Two Slovenian–Croatian Basins Using MLR and MLLR
Bibliographic record
Abstract
This study investigates the recent changes in the relationship between annual precipitation, mean annual air temperature, mean annual river discharge, and annual runoff coefficients in two small, neighboring continental catchments in Slovenia and Croatia: the Sutla/Sotla and Krapina River basins. Analyses of discharge, precipitation, and temperature time series were conducted on an annual scale using simple linear regression, multiple linear regression (MLR), and multiple log-linear regression (MLLR). Despite their geographical proximity and similar climatic conditions, the two basins exhibit markedly different runoff coefficients. Lower values observed in the Krapina River at Kupljenovo likely reflect gentle slopes, permeable soils, dense vegetation, and significant infiltration losses, while higher runoff coefficients at the Sutla River near Rakovec suggest more rapid surface runoff, reduced infiltration, and potentially distinct land use. In both basins, a pronounced rise in mean annual air temperatures has been evident since 1992, followed approximately eight years later by a sharp decline in mean annual flows and annual runoff coefficients. Our results show that the influence of air temperature on both discharge and runoff coefficients has become significantly stronger in recent decades, especially since the year 2000, contributing to a notable decline in mean annual discharges as well as annual runoff coefficients. Mean annual discharges have decreased by 19% in the Sutla and 15% in the Krapina basin, coinciding with temperature increases. Regression analyses confirm that air temperature has become a dominant negative predictor of discharge and runoff, with its influence intensifying over the past two decades. The runoff coefficient declined from 0.483 to 0.394 in the Sutla basin and from 0.325 to 0.270 in the Krapina basin during the same period. These findings highlight the importance of catchment-specific assessments for understanding and managing the localized impacts of climate change on hydrological processes. However, future work should incorporate evaporation as a key variable to better attribute the observed runoff reductions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".