Continuous Wavelet Analysis of Water Quality Time Series in a Rapidly Urbanizing Mixed-Land-Use Watershed in Ontario, Canada
Bibliographic record
Abstract
Urbanization and mixed-land-use development significantly impact water quality dynamics in watersheds, necessitating continuous monitoring and advanced analytical techniques for sustainable water management. This study employs continuous wavelet analysis to investigate the temporal variability and correlations of real-time water quality parameters in the Credit River watershed, Ontario, Canada. The Integrated Watershed Monitoring Program (IWMP), initiated by the Credit Valley Conservation (CVC) Authority, has facilitated long-term real-time water quality monitoring since 2010. Fundamental and exploratory statistical analyses were conducted to identify patterns, trends, and anomalies in key water quality parameters, including pH, specific conductivity, turbidity, dissolved oxygen (DO), chloride, water temperature (TH2O°), air temperature (Tair°), streamflow, and water level. Continuous wavelet transform and wavelet coherence techniques revealed significant temporal variations, with “1-day” periodicities for DO, pH, (TH2O°), and (Tair°) showing high power at a 95% confidence level against red noise, particularly from late spring to early fall, rather than throughout the entire year. These findings underscore the seasonal influence on water quality and highlight the need for adaptive watershed management strategies. The study demonstrates the potential of wavelet analysis in detecting temporal patterns and informing decision-making for sustainable water resource management in rapidly urbanizing mixed-land-use watersheds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".