Document Type

Presentation

Publication Date

8-1-2014

Abstract

E. Coli is widely used as an indicator organism to assess the risk of pathogenic bacteria in water bodies. Due to their strong association with suspended and bed sediments, the fate and transport of micro-organisms in water bodies is strongly controlled by sediment dynamics. It has been shown that bed sediments can contain orders of magnitude larger pathogen concentration than the water column and these sediment-associated bacteria can be released into the water column as a result of high flow velocities that cause sediment resuspension. In this presentation parameter estimation of a mechanistic model of bacteria-sediment interaction using a deterministic method through a hybrid genetic algorithm and also stochastically through Makov-Chain Monte Carlo (MCMC) approach will be presented. The physically-based model considers the advective-dispersive transport of sediments as well as both free-floating and sediment-associated bacteria in the water column and also the fate and transport of bacteria in the bed sediments. The bed sediments are treated as a distributed system which allows modeling the evolution of the vertical distribution of bacteria as a result of sedimentation, resuspension, diffusion, and bioturbation in the sediments. The model is applied to sediment and E. coli concentration data collected during a high flow event in a small stream historically receiving agricultural runoff. The genetic algorithm and MCMC method are used to estimate the likeliest values as well as the joint probability density functions of model parameters including sediment deposition and erosion rates, critical shear stress for deposition and erosion, attachment and detachment rate constants of E. coli to/from sediments and also the effective diffusion coefficients of E. coli in the bed sediments. The uncertainties associated with the estimated parameters are quantified via the MCMC approach and the correlation between the posterior distribution of parameters have been used to assess the model adequacy and parsimony.

Comments

Session R64, Parameter Estimation: Calibration

 
 

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