Improved first-order uncertainty method for water-quality modeling
Journal of Environmental Engineering
By: Charles S. Melching and S. Anmangandla
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Abstract
Uncertainties are unavoidable in water-quality modeling and subsequent management decisions. Monte Carlo simulation and first-order uncertainty analysis (involving linearization at central values of the uncertain variables) have been frequently used to estimate probability distributions for water-quality model output due to their simplicity. Each method has its drawbacks: Monte Carlo simulation's is mainly computational time; and first-order analysis are mainly questions of accuracy and representativeness, especially for nonlinear systems and extreme conditions. An improved (advanced) first-order method is presented, where the linearization point varies to match the output level whose exceedance probability is sought. The advanced first-order method is tested on the Streeter-Phelps equation to estimate the probability distribution of critical dissolved-oxygen deficit and critical dissolved oxygen using two hypothetical examples from the literature. The advanced first-order method provides a close approximation of the exceedance probability for the Streeter-Phelps model output estimated by Monte Carlo simulation using less computer time - by two orders of magnitude - regardless of the probability distributions assumed for the uncertain model parameters.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Improved first-order uncertainty method for water-quality modeling |
| Series title | Journal of Environmental Engineering |
| Volume | 118 |
| Issue | 5 |
| Year Published | 1992 |
| Language | English |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Journal of Environmental Engineering |
| First page | 791 |
| Last page | 805 |
| Google Analytic Metrics | Metrics page |