Stormwater quality modelling in combined sewers: calibration and uncertainty analysis.

  • Kanso A
  • Chebbo G
  • Tassin B
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Abstract

Estimating the level of uncertainty in urban stormwater quality models is vital for their utilization. This paper presents the results of application of a Monte Carlo Markov Chain method based on the Bayesian theory for the calibration and uncertainty analysis of a storm water quality model commonly used in available software. The tested model uses a hydrologic/hydrodynamic scheme to estimate the accumulation, the erosion and the transport of pollutants on surfaces and in sewers. It was calibrated for four different initial conditions of in-sewer deposits. Calibration results showed large variability in the model's responses in function of the initial conditions. They demonstrated that the model's predictive capacity is very low.

Author-supplied keywords

  • Bayes Theorem
  • Calibration
  • Cities
  • France
  • Markov Chains
  • Models, Chemical
  • Rain
  • Rain: chemistry
  • Sewage
  • Sewage: chemistry
  • Uncertainty
  • Water Supply
  • Water Supply: standards

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Authors

  • A Kanso

  • G Chebbo

  • B Tassin

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