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Open AccessArticle

Estimating Rainfall Design Values for the City of Oslo, Norway—Comparison of Methods and Quantification of Uncertainty

The Norwegian Meteorological Institute (MET Norway), Henrik Mohns plass 1, 0313 Oslo, Norway
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Water 2020, 12(6), 1735; https://doi.org/10.3390/w12061735
Received: 30 April 2020 / Revised: 12 June 2020 / Accepted: 12 June 2020 / Published: 17 June 2020
Due to its location, its old sewage system, and the channelling of rivers, Oslo is highly exposed to urban flooding. Thus, it is crucial to provide relevant and reliable information on extreme precipitation in the planning and design of infrastructure. Intensity-Duration-Frequency (IDF) curves are a frequently used tool for that purpose. However, the computational method for IDF curves in Norway was established over 45 years ago, and has not been further developed since. In our study, we show that the current method of fitting a Gumbel distribution to the highest precipitation events is not able to reflect the return values for the long return periods. Instead, we introduce the fitting of a Generalised Extreme Value (GEV) distribution for annual maximum precipitation in two different ways, using (a) a modified Maximum Likelihood estimation and (b) Bayesian inference. The comparison of the two methods for 14 stations in and around Oslo reveals that the estimated median return values are very similar, but the Bayesian method provides upper credible interval boundaries that are considerably higher. Two different goodness-of-fit tests favour the Bayesian method; thus, we suggest using the Bayesian inference for estimating IDF curves for the Oslo area. View Full-Text
Keywords: IDF; extreme precipitation; design precipitation; extreme value distribution; GEV; Bayesian inference; Norway IDF; extreme precipitation; design precipitation; extreme value distribution; GEV; Bayesian inference; Norway
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Lutz, J.; Grinde, L.; Dyrrdal, A.V. Estimating Rainfall Design Values for the City of Oslo, Norway—Comparison of Methods and Quantification of Uncertainty. Water 2020, 12, 1735.

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