Lake Outflow and Hillslope Lateral Inflows Dictate Thermal Regimes of Forested Streams Draining Small Lakes
Bibliographic record
Abstract
Abstract Empirical studies have highlighted the important influence of lakes on stream temperature at landscape scales, even when lakes comprise just a small fraction of the catchment area. However, only a few studies have focused on the hydrologic and thermal processes underpinning these landscape patterns. We collected detailed field measurements at a boreal stream that drains a headwater lake and used these data within a process‐based stream temperature model to, (a) document the downstream extent of lake influences at both seasonal and event‐based timescales, (b) assess the hydrologic and thermal processes that control the observed downstream variability, and (c) compare downstream temperature for streams with and without a headwater lake. Summer and autumn lake outlet temperatures were elevated compared to hillslope lateral inflow temperatures. During periods of low lake outflow, stream temperatures decreased rapidly downstream as local energy fluxes, primarily lateral inflows from the hillslopes and hyporheic exchange, overwhelmed the lake effects. The lake influence on downstream temperature was the greatest during periods of high lake outflow and persisted at least 1.4 km downstream. Since lakes can moderate and delay upstream rainfall runoff response, periods of high lake outflow and high hillslope inflow rates were generally out‐of‐phase. This difference in timing of warm lake outlet and cool hillslope water creates a dynamic thermal environment downstream of the small lake. Such lakes are ubiquitous in northern landscapes, and accounting for the competing influence of lake and hillslope contributions on downstream water temperature is critical for predicting how network‐scale thermal regimes will respond to environmental change.
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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 | 0.001 | 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".