Probability of combined high sea levels and large rains in Malmö, Sweden, southern Öresund

Research output: Contribution to journalArticlepeer-review

Abstract

Observations of sea level and precipitation in Malmö, Sweden in the southeastern part of the sound Öresund have been used to estimate the probabilities of local compound events of high sea level and large daily and hourly rains. There are observations of sea level and daily rains extending back to 1930. The observations of short-term rainfall are from 1980 and onwards. Most large rainfalls come in the summer, while the highest sea levels are in the autumn and in the winter. The highest observed sea level is about 130cm above mean sea level, and the largest daily rain is close to 100mm. However, the highest sea level observed during a day with rainfall corresponding to the 1-year rain is less than 60cm. The highest sea level observed during an hour with 1-year hourly rainfall is 30cm. From the statistics of daily rains, hourly rains and sea level, extreme values for each of them have been computed. For events with frequency higher than one per four years the probabilities of combined events sea level - rainfall are determined directly from the observations. For more rare events, marginal distributions of sea level and rainfall are determined. Copulas and conditional probabilities are used. When the sea level exceeds 20cm above mean sea level, daily rains exceeding 10mm are almost independent of the sea level and so are hourly rains exceeding 5mm. It is extremely rare that large rains occur when the sea level is very high. The combination of 1-year rainfall and the 1-year sea level has a return period of more than 200years.

Original languageEnglish
Pages (from-to)3172-3183
Number of pages12
JournalHydrological Processes
Volume30
Issue number18
Early online date2016
DOIs
Publication statusPublished - 2016 Aug 30

Subject classification (UKÄ)

  • Water Engineering

Free keywords

  • Conditional probability
  • Extreme events
  • Frank's copula
  • Seasonal distribution
  • Urban environment

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