Probabilistic methods for uncertainty analysis and parameter estimation for dissolved oxygen models

Masliev I & Somlyody L (1994). Probabilistic methods for uncertainty analysis and parameter estimation for dissolved oxygen models. Water Science & Technology 30 (2): 99-108.

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Abstract

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.

The Hornberger-Spear-Young procedure and the Bayesian approach probabilistic methods are presented in this article. These methods are applied to the traditional Streeter-Phelps model and its extensions for water quality models. To demonstrate this, water quality measurements from a polluted river are employed as part of the policy oriented study. The BOD decay rate obtained was rather high due to partial biological wastewater treatment and small water depth. However the overall derived parameter values were in harmony with the previous findings. Consequently, alternative oxygen models can also be calibrated to the data set. Ranges for probability density functions were rather broad calling for a well suited formulation of a water quality management model.

Item Type: Article
Uncontrolled Keywords: Bayesian statistics; Oxygen household; Parameter estimation; Probabilistic methods of model identification; Uncertainty; Water quality management; Water quality modelling
Research Programs: Regional Water Policies (RWP)
Bibliographic Reference: Water Science and Technology; 30(2 pt 2):99-108 (1994)
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Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:03
Last Modified: 25 Apr 2017 12:06
URI: http://pure.iiasa.ac.at/3824

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