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Least-squares calibration of QUAL2E

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In recent years, much research has been directed toward developing objective, statistically valid methods for water quality model calibration (parameter estimation). Few of these methods have found their way into routine practice. This paper describes the application of a nonlinear regression technique to calibrate the popular water quality model, QUAL2E. A nonlinear programming model was developed to minimize the sum of squares of differences in model predictions and pollutant observations. QUAL2E is called as a subroutine by the program. Six parameters are simultaneously estimated for each of two intensive survey data sets. Because optimal parameter estimates were found to be considerably different for each data set, suggesting that the parameters were not true constants, these estimates were used to develop probability distributions for the parameters for use in a Monte Carlo model.

Keywords: Monte Carlo; calibration; model; parameters; water quality

Document Type: Research Article


Publication date: March 1, 1992

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  • Water Environment Research® (WER®) publishes peer-reviewed research papers, research notes, state-of-the-art and critical reviews on original, fundamental and applied research in all scientific and technical areas related to water quality, pollution control, and management. An annual Literature Review provides a review of published books and articles on water quality topics from the previous year.

    Published as: Sewage Works Journal, 1928 - 1949; Sewage and Industrial Wastes, 1950 - 1959; Journal Water Pollution Control Federation, 1959 - Oct 1989; Research Journal Water Pollution Control Federation, Nov 1989 - 1991; Water Environment Research, 1992 - present.
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