Stochastic Long Term Modelling of a Drainage System with Estimation of Return Period Uncertainty

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Long term prediction of maximum water levels and combined sewer overflow (CSO) in drainage systems are associated with large uncertainties. Especially on rainfall inputs, parameters, and assessment of return periods. This paper proposes a Monte Carlo based methodology for stochastic prediction of both maximum water levels as well as CSO volumes based on operations of the urban drainage model MOUSE (Lindberg and Joergensen 1986) in a single catchment case study. Results show quite a wide confidence interval of the model predictions especially on the large return periods. Traditionally, return periods of drainage system predictions are based on ranking, but this paper proposes a new methodology for the assessment of return periods. Based on statistics of characteristic rainfall parameters and correlation with drainage system predictions, it is possible to predict return periods more reliably, and with smaller confidence bands compared to the traditional methodology.
Original languageEnglish
Title of host publicationConfernce Proceedings : 11th International Conference on Urban Drainage : Edinburgh International Conference Centre, Scotland : 11 ICUD: 31st August - 5th September 2008
Number of pages10
Publication date2008
Publication statusPublished - 2008
EventThe International Conference on Urban Drainage - Edinburgh, United Kingdom
Duration: 31 Aug 20085 Sep 2008
Conference number: 11


ConferenceThe International Conference on Urban Drainage
Country/TerritoryUnited Kingdom

Bibliographical note

Published on a cd


  • Urban drainage modelling
  • Long term simulation
  • Extreme statistics
  • Uncertainties
  • Monte Carlo simulation
  • Combined sewer overflow
  • Return period


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