Assessing the Risks of Salinity Fact Sheet

A framework for assessing the
biological risks of increasing salinity in
Fact sheet - April 2008
Increasing salinity threatens a wide range of native
plants and animals in Victoria. A key stage in managing
the effects of increasing salinity is predicting which
plants and animals are likely to be impacted and where.
The risk modelling approaches and decision frameworks
developed have a number of applications:
Determination of how best to allocate resources for
protecting biodiversity assets from salinity;
A team of researchers from DSE has developed new
Salt Impact Modelling approaches to estimate the
proportion of species likely to be affected at different
concentrations of salts in soil or water. Funded by the
National Action Plan for Salinity and Water Quality
(NAP) and the National Heritage Trust (NHT), this
project has developed decision-frameworks and
modelling tools for assessing the risks of salinity. These
tools have been applied to a number of case studies to
demonstrate their application.
Figure 1. Species Sensitivity Distribution
Salt Impact Models
The risk models developed in this project form a central
component in the Ecological Risk Assessment (ERA) of
the effects of salinity. Quantitative approaches to ERA
are being increasingly applied in environmental
management as these methods reduce the subjectivity
in decision making, can be informative when data are
limited or absent, and provide a framework for
incorporating estimates of uncertainty into risk
Derivation of target salinity levels for setting
objectives and environmental standards;
Comparison of the relative risks of effects between
different situations or processes;
Identification of priority
management works;
Estimation of the risks of effects on sensitive or
vulnerable taxa.
Risk models can take different forms depending on the
input data. A useful approach to predicting the effects
of salinity is to construct a Species Sensitivity
e liz _ b 4
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Input Parameters
Distribution (Figure 1) which describes the proportion
of species affected at different salinities. These models
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0 .5
M od el
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co n cA
30 00 0
to prioritise management actions.
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A second approach is to construct a more complicated
model of the likelihood of effects of salinity. This often
requires combining data from different sources to
develop an overall picture of the risks of change (Figure
20 000
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70 000
8000 0
Figure 2. Probabilistic Risk Model
Published by the Victorian Government Department of Sustainability and Environment, Melbourne, April 2008
© The State of Victoria Department of Sustainability and Environment 2008
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ISBN 978-1-74208-340-7 (PDF)
For more information contact the DSE Customer Service Centre 136 186 or Dr Bill Dixon at [email protected], Arthur Rylah Institute, Department of Sustainability and Environment, PO
Box 137, Heidelberg 3084.
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