Climate variability is an important driver of water treatability in a shallow reservoir
Bibliographic record
Abstract
Drinking water treatability is defined by multiple parameters that are strongly impacted by climatic and anthropogenic drivers. Working in a shallow reservoir in the Canadian Prairies, generalized additive models (GAMs) were applied to a 33-year dataset to identify drivers of interannual variability in multiple indicators of drinking water treatability. Interannual variability in treatability indicators was substantial. In the most extreme years, annual means were 2.9, 2.4, 1.5, and 1.7 times higher than the long-term averages for odour, turbidity, dissolved organic carbon (DOC), and total dissolved solids (TDS), respectively. GAMs showed that these treatability indicators are highly responsive to two modes of climate variability: the El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Specifically, cool/wet cycles contributed to elevated turbidity, odour, and DOC, while warm/dry cycles contributed to higher levels of TDS, DOC, odour, and turbidity. The effects of climate variability equate to 0.5–1.7 times the long-term average for each treatability indicator. Hydrological management and nutrients also play a key role, with effects equating to 0.10–1.1 times the long-term average in treatability indicators. Together, these findings show these predictors contributed to substantial variability in water treatability. Although shallow systems in dryland regions may represent extreme examples of climate sensitivity, extreme climatic conditions are expected to become more common, posing substantial risks to water treatment. This study is the first to use GAMs to provide long-term evidence of impacts of natural climate variability and water management to drinking water treatability, potentially offering early warning about changes to source water quality. • Drinking water treatability indicators showed extreme interannual variability. • Large-scale climate oscillations were the strongest driver of treatability. • Effects of climate variability equated to nearly twice the long-term indicator means. • Nutrient and flow management had substantial, but lesser effects on treatability. • Indicators of poor drinking water may be predictable by anticipating climate oscillations.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".