Bayesian Inference For Predicting Ecological
Water Quality Under Different Climate Change
Scenarios
Author(s)
J. Kotta, R. Aps & H. Orav-Kotta
Abstract
The aim of this paper is to assess the separate and interactive effects of
eutrophication and climate variables on the sea water quality in Pärnu Bay (the
Gulf of Riga, Baltic Sea) using multivariate statistical analyses and the Bayesian
Belief Network (BBN) methodology. The assessment was based on the
following biological quality elements: phytoplankton, submerged aquatic
vegetation and benthic invertebrates. The multivariate statistical analyses suggest
that zoobenthos communities are largely driven by weather conditions
(i.e. climate variables), phytoplankton by nutrient loads while the dynamics of
macrophyte communities was due to the combined effect of weather and nutrient
loads. The BBN constructed for this study represents uncertainty in ecological
water quality assessment. Probabilistic modeling shows that phytoplankton and
zoobenthos are not sensitive to climate change impacts while phytobenthos
would suffer from decrease in sea water salinity. Under climate change,
therefore, phytobenthos is one of the key variables in determining the water
quality in the study area.
Keywords: Baltic Sea, Bayesian inference, climate change, eutrophication,
water quality assessment.
Keywords
Baltic Sea, Bayesian inference, climate change, eutrophication,water quality assessment
Related Book
Other papers in this volume
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