Presenter/Author Information

George B. Arhonditsis

Keywords

process-based modelling, eutrophication, bayesian inference, water quality criteria, decision-making

Start Date

1-7-2012 12:00 AM

Description

Skeptical views of the scientific value of modelling argue that there is no true model of an ecological system, but rather several adequate descriptions of different conceptual basis and structure. My study addresses this question using a complex ecosystem model, developed to guide the water quality criteria setting process in the Hamilton Harbour (Ontario, Canada), along with a simpler plankton model that considers the interplay among phosphate, detritus, and generic phytoplankton and zooplankton state variables. Predictions from the two models are combined using the respective standard error estimates as weights in a weighted model average. The two eutrophication models are used in conjunction with the SPAtially Referenced Regressions On Watershed attributes (SPARROW) watershed model. The Bayesian nature of my work is used: (i) to alleviate problems of spatiotemporal resolution mismatch between watershed and receiving waterbody models; and (ii) to overcome the conceptual or scale misalignment between processes of interest and supporting information. The lessons learned from this study will contribute towards the development of integrated modelling frameworks

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Jul 1st, 12:00 AM

Integration of Bayesian inference techniques with mathematical modelling

Skeptical views of the scientific value of modelling argue that there is no true model of an ecological system, but rather several adequate descriptions of different conceptual basis and structure. My study addresses this question using a complex ecosystem model, developed to guide the water quality criteria setting process in the Hamilton Harbour (Ontario, Canada), along with a simpler plankton model that considers the interplay among phosphate, detritus, and generic phytoplankton and zooplankton state variables. Predictions from the two models are combined using the respective standard error estimates as weights in a weighted model average. The two eutrophication models are used in conjunction with the SPAtially Referenced Regressions On Watershed attributes (SPARROW) watershed model. The Bayesian nature of my work is used: (i) to alleviate problems of spatiotemporal resolution mismatch between watershed and receiving waterbody models; and (ii) to overcome the conceptual or scale misalignment between processes of interest and supporting information. The lessons learned from this study will contribute towards the development of integrated modelling frameworks