Turbidity dynamics in small streams as a key component of water quality management
Bibliographic record
Abstract
Given the reliance of many communities on surface water, and the continued degradation of aquatic ecosystems, understanding the limits and uncertainties of water quality assessment is vital. Turbidity is a common measurement of water quality for public health and ecosystem function. It has been frequently studied in larger water bodies but not in small streams. We characterized turbidity dynamics in two sets of small streams over seasonal and spatial scales, by monitoring. We collected continuous turbidity measures every 15 minutes, for one year, with monthly spot turbidity samples, in two regions in British Columbia with varying degrees of land use for over a year. Three streams were in the University of British Columbia’s Malcolm Knapp Research Forest, with forestry as the dominant land use type. Our other study area was the Shawnigan Lake Watershed, located on southern Vancouver Island. We had three sites on each of two creeks, McGee Creek and Van Horne Creek, where Van Horne Creek had higher percentages of industrial and urban land uses determined using a Normalized Difference Vegetation Index. In the Research Forest streams, turbidity maximums were ~16 NTU, whereas McGee Creek reached a maximum of 67 NTU, and Van Horne Creek reached 371 NTU. Using Principle Component Analysis and Linear Mixed Effects Models, we found that both rainfall and discharge were significant drivers of turbidity, particularly during periods of intense precipitation. Turbidity also displayed mostly clockwise hysteresis dynamicss during storm events. Interestingly, turbidity displayed a highly significant seasonal response, where the first-flush response of a few of the highest turbidity events occurred during the spring and summer. Land use was also a significant driver of turbidity, particularly forestry, urban and construction land uses. Our research showed that turbidity was spatially complex, and highly variable over time and space, with individual sites and streams being significantly different from each other. Our results have important implications for turbidity monitoring and assessment, given that current monitoring schemes may be insufficient to determine changes in turbidity due to land uses and to assess water quality accurately over spatial scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".