Do headwater lakes moderate downstream temperature response to forest harvesting? Illustrating opportunities and obstacles associated with virtual experiments
Bibliographic record
Abstract
Abstract There are concerns that environmental changes, such as climate variability and forest harvesting, are altering stream thermal regimes and impacting aquatic ecosystems. Previous studies have suggested that the abundant headwater lakes found in northern landscapes may moderate downstream temperature response to forest harvesting. We investigated this hypothesis using a virtual experimental approach based on detailed field measurements made at boreal catchments in northern Sweden coupled with a process‐based stream temperature model. We simulated streamside harvesting for stream reaches with and without a headwater lake. Mean daily summer stream temperature response to harvesting was generally between 0.5 and 1.5°C higher for the stream without a lake than for the stream with a lake. However, during rain events the stream with the lake showed a greater stream temperature response than the stream without a lake. Headwater lakes typically store and delay runoff from rain events, augment baseflow, and have elevated outflow temperatures. These differences in upstream boundary conditions, in terms of flow and water temperature, were the key drivers for the contrasting harvest responses between streams with and without headwater lakes. These findings were generally consistent across different harvesting scenarios; however, uncertainty in the hyporheic term and post‐harvest microclimate conditions influenced the simulated magnitude of post‐harvest stream temperature response. Our study highlights the utility of virtual experiments for gaining insight on systems understanding but caution is needed when using models for predictions outside the conditions for which models are calibrated.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".