Sensitivity of lake thermal and mixing dynamics to climate change
Bibliographic record
Abstract
Warming-induced changes in lake thermal and mixing regimes present risks to water quality and ecosystem services provided by U.S. lakes and reservoirs. Modulation of responses by different physical and hydroclimatic settings are not well understood. We explore the potential effects of climate change on 27 lake “archetypes” representative of a range of lakes and reservoirs occurring throughout the U.S. Archetypes are based on different combinations of depth, surface area, and water clarity. LISSS, a one-dimensional dynamic thermal simulation model, is applied to assess lake response to multiple mid-21st century change scenarios applied to nine baseline climate series from different hydroclimatic regions of the U.S. Results show surface water temperature increases of about 77 % of increase in average air temperature change. Bottom temperature changes are less (around 30 %) for deep lakes and in regions that maintain mid-winter air temperatures below freezing. Significant decreases in length of ice cover are projected, and the extent and strength of stratification will increase throughout the U.S., with systematic differences associated with depth, surface area, and clarity. These projected responses suggest a range of future challenges that lake managers are likely to face. Changes in thermal and mixing dynamics suggest increased risk of summer hypoxic conditions and cyanobacterial blooms. Increased water temperatures above the summer thermocline could be a problem for cold water fisheries management in many lakes. Climate-induced changes in water balance and mass inputs of nutrients may further exacerbate the vulnerability of lakes to climate 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 | 0.001 |
| 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".