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A
GIS-based
approach to modelling potential agricultural
contamination of surface
and groundwater in the Fairchild Creek
Watershed, Ontario
(©
Jim
Wark/AgStock Images/Corbis)
Colin Gutcher, Jason
Krompart, & Peter Nowell
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Abstract
Agricultural
contaminants pose a serious threat to
environmental and human
well-being if introduced to fresh water
resources. The Fairchild Creek
Watershed of southern Ontario is an
agriculturally intense region,
where there exists the need to protect both
surface and groundwater
sources from the negative impacts associated
with the use of
agrochemicals. Contaminants can be
transported into water resources
either from surface runoff after a storm
event, or through infiltration
into groundwater. As such, a GIS based
multi-criteria evaluation (MCE)
model was created to encompass both of these
mechanisms, thus
determining agricultural areas which pose
the greatest risk to water
resources across the entire watershed. The
Universal Soil Loss Equation
(USLE), combined with a transfer component
(USLETrans), and the DRASTIC
model were used to estimate potential risk
to surface and groundwater
resources respectively. The outputs of these
models were then combined
into a MCE to obtain the overall risk
potential to water resources
within the watershed. The USLE was used in
conjunction with a transport
component to determine the potential for
contaminants to enter nearby
surface fresh water reserves. The output of
the model classified
agricultural land within the watershed as
being either at low, medium,
or high risk of contributing to
contamination. Areas of high and medium
risk consisted of 17% of the area, while 54%
of the area was classified
as low risk. The remaining area was not
considered as it was
non-agricultural in nature and consisted
predominantly of urban, water,
and forested areas. Areas of high and medium
risk for groundwater
contamination specifically, made up 96% of
the watershed, compared to
only 10% of the watershed which is at medium
or high risk of
contaminating surface water. These models
provide a robust foundation
in a GIS environment and are highly
adaptable to a variety of
applications and geographical areas.
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