Climate and flow management interact to impact reservoir water quality
Bibliographic record
Abstract
Shallow, eutrophic lakes often exhibit high and extremely variable phytoplankton biomass. This variability makes drinking water supplies from shallow lakes particularly vulnerable to rapid change, as phytoplankton blooms can strongly impact treatment processes. We use 39 years of data to assess the interacting roles of climate and flow management in driving change in chlorophyll a (an indicator of phytoplankton biomass) in a shallow, hydrologically managed drinking water reservoir. Generalized additive modelling shows a significant increase in phytoplankton biomass (1992–1997), leading to a doubling of average chlorophyll a concentrations. Interestingly, we also see a shift towards smaller spring blooms and larger summer blooms. Our results show an impact of nutrients, climate, and flow source on phytoplankton biomass. Specifically, the increase in phytoplankton biomass coincided with periods with greater precipitation and associated nutrient-rich inflows from an agriculturally dominated catchment, along with strong El Niño events that potentially contributed to a warm, stable water column during the growing season. During high flows from the local catchment, flows from the upstream supply reservoir are typically reduced to prevent downstream flooding. However, flows from the supply reservoir have a diluting impact due to its lower nutrient concentrations. Thus, where reservoir flow sources vary in chemistry, considering water quality in flow management could help to ameliorate bloom severity and reduce drinking water treatment costs. Understanding interacting management and climatic drivers of reservoir water quality can also contribute to advance planning and mitigation of risks to water treatment.
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 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".