Water quality models are essential to the development of least-cost water quality control strategies based on ambient criteria. Such policies are particularly important if financial resources are limited which is currently the case in Central and Eastern European countries. In turn, the derivation of realistic model parameters is a pre-requisite of successful model application. Often, longitudinal water quality profile measurements are performed for the above purpose, but the traditional evaluation of this data encounters significant difficulties due to measurement and other uncertainties. Thus, probabilistic methods are preferred. 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 traditional Streeter‒Phelps model and its extensions. For the purpose of illustration, water quality measurements from the highly polluted Nitra River in Slovakia are employed as a part of a policy oriented study. The BOD decay rate obtained was rather high due to partial biological wastewater treatment and small water depth, but overall, derived parameter values were in harmony with literature findings. Alternative dissolved oxygen models (2‒3 state variables and 2‒5 parameters) could also be calibrated to the data set. Ranges of probability density functions (PDFs) for model parameters were rather broad calling for a well suited formulation of a water quality management model.
Research Article|July 01 1994
PROBABILISTIC METHODS FOR UNCERTAINTY ANALYSIS AND PARAMETER ESTIMATION FOR DISSOLVED OXYGEN MODELS
Water Sci Technol (1994) 30 (2): 99-108.
I. Masliev, L. Somlyódy; PROBABILISTIC METHODS FOR UNCERTAINTY ANALYSIS AND PARAMETER ESTIMATION FOR DISSOLVED OXYGEN MODELS. Water Sci Technol 1 July 1994; 30 (2): 99–108. doi: https://doi.org/10.2166/wst.1994.0033
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