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Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Pathogen and indicator variability in a heavily impacted watershed.

Sarah M Dorner¹, William B Anderson, Terri Gaulin

¹Department of Public Health, University of Massachusetts Amherst, Amherst MA 01003, USA. sdorner@tei.umass.edu

Journal of Water and Health

|August 7, 2007

Summary

Pathogen levels in the Grand River Watershed often peaked before indicator organisms, suggesting complex sources impacting water quality. These findings are crucial for effective water monitoring and risk assessment.

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Area of Science:

Environmental microbiology
Water quality assessment
Public health

Background:

Grand River Watershed in Southwestern Ontario, Canada, faces potential contamination risks.
Understanding pathogen dynamics is crucial for safeguarding drinking water sources.

Purpose of the Study:

To analyze pathogen levels and their correlation with indicator organisms and turbidity during storm and snowmelt events.
To assess the implications for water quality monitoring and risk assessment models.

Main Methods:

Collected water samples from 36 locations (July 2002-December 2003).
Analyzed for total coliforms, fecal coliforms, Escherichia coli (E. coli), E. coli O157:H7, Campylobacter spp., Cryptosporidium spp., Giardia spp., enteric viruses, and Clostridium perfringens.
Sampled storm and snowmelt events at two locations, including a drinking water intake.

Main Results:

A positive correlation was observed between E. coli levels and turbidity for most events.
Pathogen peaks frequently preceded peaks in indicator organisms and turbidity.
Weak correlations between pathogen and indicator peaks suggest diverse pathogen sources and complex environmental transport.

Conclusions:

Pathogen levels and indicator organism peaks do not always coincide, complicating direct water quality monitoring.
Findings highlight the need for advanced pathogen fate and transport models for accurate source water risk assessment.
Results inform the planning of more effective water quality monitoring programs in watersheds.