Continuous Wavelet Analysis of Water Quality Time Series in a Rapidly Urbanizing Mixed-Land-Use Watershed in Ontario, Canada

Sukhmani Bola, Ramesh Rudra, Rituraj Shukla (), Amanjot Singh, Pradeep Goel, Prasad Daggupati and Bahram Gharabaghi
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Sukhmani Bola: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Ramesh Rudra: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Rituraj Shukla: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Amanjot Singh: Credit Valley Conservation Authority, 1255 Old Derry Road, Mississauga, ON L5N 6R4, Canada
Pradeep Goel: Ontario Ministry of the Environment, Conservation and Parks, Toronto, ON M9P 3V6, Canada
Prasad Daggupati: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Bahram Gharabaghi: School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada

Sustainability, 2025, vol. 17, issue 19, 1-23

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 ( T H 2 O ° ), air temperature ( T a i r ° ), streamflow, and water level. Continuous wavelet transform and wavelet coherence techniques revealed significant temporal variations, with “1-day” periodicities for DO, pH, ( T H 2 O ° ), and ( T a i r ° ) 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.

Keywords: wavelet coherence; real-time data; watershed management; water quality; mixed-land-use watershed (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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