Blooms and flows: Effects of variable hydrology and management on reservoir water quality
Bibliographic record
Abstract
Abstract Flow management has the potential to significantly affect ecosystem condition. Shallow lakes in arid regions are especially susceptible to flow management changes, which can have important implications for the formation of cyanobacterial blooms. Here, we reveal water quality shifts associated with changing source water inflow management. Using in situ monitoring data, we studied a seven‐year time span during which inflows to a shallow, eutrophic drinking water reservoir transitioned from primarily natural landscape runoff (2014–2015) to managed flows from a larger upstream reservoir (Lake Diefenbaker; 2016–2020) and identified significant changes in cyanobacteria (as phycocyanin) using generalized additive models to classify cyanobacterial bloom formation. We then connected changes in water source with shifts in chemistry and the occurrence of cyanobacterial blooms using principal components analysis. Phycocyanin was greater in years with managed reservoir inflow from a mesotrophic upstream reservoir (2016–2020), but dissolved organic matter (DOM) and specific conductivity, important determinants of drinking water quality, were greatest in years when landscape runoff dominated lake water source (2014–2015). Most notably, despite changing rapidly, it took multiple years for lake water to return to a consistent and reduced level of DOM after managed inflows from the upstream reservoir were resumed, an observation that underscores how resilience may be hindered by weak resistance to change and slow recovery. Environmental flows for water quality are rarely defined, yet we show that trade‐offs exist between poor water quality via elevated conductivity and DOM and higher bloom risk, depending on water source. Our work highlights the importance of source water quality, not just quantity, to water security, and our findings have important implications for water managers who must protect ecosystem services while adapting to projected hydroclimatic change.
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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 | 0.000 | 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) | 0.001 | 0.001 |
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".