Identifiability and uncertainty analysis of the River Water Quality Model No. 1 (RWQM1)

Abstract

State of the art models as used in activated sludge modelling and recently proposed for river water quality modelling integrate the knowledge in a certain field. If applied to data from a specific site, such models are nearly always overparameterised. This raises the question of how many parameters can be fitted in a given context and how to find identifiable parameter subsets given the experimental layout. This problem is addressed for the kinetic parameters of a simplified version of the recently published river water quality model no. 1 (RWQM1). The selection of practically identifiable parameter subsets is discussed for typical boundary conditions as a function of the measurement layout. Two methods for identifiable subset selection were applied and lead to nearly the same results. Assuming upstream and downstream measurements of dissolved substances to be available, only a few (5-8) model parameters appear to be identifiable. Extensive measurement campaigns with dedicated experiments seem to be required for successful calibration of RWQM1. The estimated prior uncertainties of the model parameters are used to estimate the uncertainty of model predictions. Finally an estimate is provided for the maximum possible decrease in prediction uncertainty achievable by a perfect determination of the values of the identifiable model parameters.