To what extent does variability of historical rainfall series influence extreme event statistics of sewer system surcharge and overflows?

Kjeld Schaarup-Jensen, Michael R. Rasmussen, Søren Thorndahl

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In urban drainage modeling long term extreme statistics has become an important basis for decision-making e.g. in connection with renovation projects. Therefore it is of great importance to minimize the uncertainties concerning long term prediction of maximum water levels and combined sewer overflow (CSO) in drainage systems. These uncertainties originate from large uncertainties regarding rainfall inputs, parameters, and assessment of return periods. This paper investigates how the choice of rainfall time series influences the extreme events statistics of max water levels in manholes and CSO volumes. Traditionally it is rarely to dispose of long term rainfall time series from a local catchment rain gauge. In the present case study this is actually the case. 2 rainfall gauges have recorded events for approximately 9 years at 2 locations within the catchment. Beside these 2 gauges another 7 gauges are located at a distance of max 20 kilometers from the catchment. All gauges are included in the Danish national rain gauge system which was launched in 1976. The paper describes to what extent the extreme events statistics based on these 9 series diverge from each other and how this diversity can be handled. All simulations are performed by means of the MOUSE LTS model.
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


  • Historical rainfall series
  • Sewer system surcharge
  • CSO volumes
  • Extreme events statistics


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