Interplay between convection and wind in driving surface mixing in the Xiangjiaba Reservoir, China
Bibliographic record
Abstract
Near-surface processes in lakes and reservoirs strongly influence basin-scale circulation, mixing, and ecosystem functioning. While wind- and convection-driven mixing are recognized in lakes, their individual and interactive contributions in morphologically complex reservoirs remain insufficiently quantified. Using high-frequency temperatures in a large reservoir, we identified three mixing regimes during autumn cooling through the scale comparisons between convection (Thorpe scale: LT) and wind (Monin-Obukhov: LMO): convective, wind-dominated mixing, and diurnal stratification. A diurnal-scale thermocline often developed under net heat input, suppressing vertical mixing, but was disrupted when either convective or wind intensified. Strong convection occurred under sustained surface heat loss (LT ≫ LMO), producing turbulence dissipation rates (εConvection ~ 10‒8–10‒7 W/kg), exceeding wind-driven values (εWind) by an order of magnitude. Conversely, wind forcing dominated (LMO ≫ LT), yielding εWind~10⁻7 W/kg. The mixed layer deepening correlated with w*3, but not with shear velocity. These findings refine mechanistic understanding of reservoir hydrodynamics and support ecologically sustainable management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".