Water Level Drawdown Affects Physical and Biogeochemical Properties of Littoral Sediments of a Reservoir and a Natural Lake
Bibliographic record
Abstract
To determine the influence of water level drawdown on littoral areas, we compared the temporal and spatial changes in the water column and sediment in the littoral region of a drinking water reservoir and a natural lake. The reservoir (Sooke) experiences more than six meters of seasonal drawdown compared to a nearby, morphometrically and trophically similar lake (Shawnigan) that experiences less than one meter of drawdown. A greater drawdown in Sooke increased the littoral area and resulted in more littoral water column mixing, more solar warming, and higher PAR at a greater range of littoral depths than in Shawnigan. Based on sediment physical and chemical characteristics, sites farthest from shore were most similar, whereas sites in the drawdown exposure zone of Sooke and the upper littoral area of Shawnigan showed the largest differences. Low macrophyte abundance and loss of fine sediments, nutrients, and organic matter from the drawdown exposure zone in Sooke compared to the equivalent littoral area in Shawnigan suggest that drawdown enhances sediment erosion and focusing. Element and stable isotope ratios of sediment carbon and nitrogen suggest organic matter in the drawdown zone in Sooke is more allochthonous in origin and is coupled more strongly with deeper sites than in Shawnigan. Organic matter source and distribution also suggests that the littoral area extends out farther in Sooke than Shawnigan. This study demonstrates that drawdown has the potential to fundamentally change reservoir littoral sediment and biogeochemical characteristics. Understanding how littoral zones in reservoirs respond to drawdown compared to natural lakes may help water managers make more ecologically informed decisions regarding drawdown impacts on the ecology of littoral zones and water quality.
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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 | 0.001 |
| 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".