Streamflow extremes alter air-water relationships in South American river-floodplain systems of the Uruguay River Basin
Bibliographic record
Abstract
Extreme hydroclimate events affect the thermal regimes of freshwater systems. In a four-year period spanning periods of flooding and severe drought in Southeastern South America (2019-2023), we monitored temperature variability in streams and floodplain lakes in an undammed tributary of the Lower Uruguay River, using Onset HOBO dataloggers and modeled daily streamflow (Q). We compare the performance of linear and nonlinear regression and generalized additive models (GAM) to evaluate how air temperature (T air ) variability affects water temperature (T water ) at daily, weekly and monthly time scales and under different Q percentile ranges. Mean daily temperatures ranged from 6.4ºC to 30.2ºC with daily maxima coinciding with record highs >40 ºC in T air and record low streamflow in summer months. Daily and weekly air-water relationships for rivers, streams and lakes were best fit by nonlinear sinusoidal models (Nash Sutcliffe Error - NSE ≥ 0.68). Extremely low-flow periods (<10% Q) showed a linear relationship to T air , while high-flow periods (>90%) showed a nonlinear sinusoidal air-water relationship. Moreover, high-flow events showed weaker T air -T water model perforamce than moderate to low flow periods. Preliminary comparisons between basin land-cover and T water suggest that forest cover favors lower T min . This study fills a geographical gap in thermal regime data in freshwater ecosystems in South America and suggests that extreme hydroclimate events have important implications for the thermal behavior of freshwater systems.
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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 | 0.000 |
| 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) | 0.001 | 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".