PROBABILISTIC METHODS FOR UNCERTAINTY ANALYSIS AND PARAMETER-ESTIMATION FOR DISSOLVED-OXYGEN MODELS

Water quality models are essential to the development of least-cost wa ter quality control strategies based on ambient criteria. Such policie s are particularly important if financial resources are limited which is currently the case in Central and Eastern European countries. In tu rn, the derivation of realistic model parameters is a pre-requisite of successful model application. Often, longitudinal water quality profi le measurements are performed for the above purpose, but the tradition al evaluation of this data encounters significant difficulties due to measurement and other uncertainties. Thus, probabilistic methods are p referred. This paper discusses two of them: the Hornberger-Spear-Young procedure using Monte Carlo simulation and a Bayesian approach. Both methods are rather generic, but they are applied here solely for the t raditional Streeter-Phelps model and its extensions. For the purpose o f illustration, water quality measurements from the highly polluted Ni tra River in Slovakia are employed as a part of a policy oriented stud y. The BOD decay rate obtained was rather high due to partial biologic al wastewater treatment and small water depth, but overall, derived pa rameter values were in harmony with literature findings. Alternative d issolved oxygen models (2-3 state variables and 2-5 parameters) could also be calibrated to the data set. Ranges of probability density func tions (PDFs) for model parameters were rather broad calling for a well suited formulation of a water quality management model.