Variable withdrawal elevations as a management tool to counter the effects of climate warming in Germany’s largest drinking water reservoir
Bibliographic record
Abstract
Thermal stratification in reservoirs is a significant factor affecting water quality, and can be strongly influenced by climate change and operational strategies. Reservoirs in the temperate zone react most sensitively to climate warming during winter as ice cover and inversed stratification are about to disappear in a warmer world. In this study, two well-established hydrodynamic models, the one-dimensional General Lake Model (GLM) and the two-dimensional CE-QUAL-W2 (W2), were used to investigate the response of winter inversed stratification in the Rappbode Reservoir to future climate warming, combined with different water withdrawal elevations. Under increased air temperature, the duration of inversed stratification is reduced and the inversion phenomenon will entirely disappear under current management if the air temperature is increased high enough (more than 4.5 K) in the future. Under strong climate warming, the Rappbode Reservoir will therefore change from a dimictic to a monomictic mixing type. Changing the reservoir management from deep withdrawal (e.g., below 350 m a.s.l.) to shallow withdrawal elevations (e.g., above 390 m a.s.l.) reduces internal heat energy stored in the reservoir in summer and prolongs the inversed stratification period in winter. This strategy can retain the dimictic behavior even under strong warming. Our study indicates that adjusting the withdrawal elevation is an effective management instrument to control the winter conditions and can, in fact, mitigate climate warming effects on winter hydrodynamics by stabilizing the dimictic mixing type.
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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 | 0.000 |
| 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) | 0.000 | 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 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".