2014
DOI: 10.1007/s00382-014-2276-4
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Long term variations of extreme rainfall in Denmark and southern Sweden

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Cited by 35 publications
(29 citation statements)
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“…Using Danish rainfall data on a daily scale Gregersen et al (2014a) have been able to identify multidecadal climate oscillations (Ntegeka and Willems, 2008;Willems, 2013a) as well as climate-related changes in precipitation patterns over the past 140 years. Nevertheless, since this paper is based on evidently shorter rainfall series, it is assumed that no significant trends or climate changes in this period are present.…”
Section: Datamentioning
confidence: 99%
“…Using Danish rainfall data on a daily scale Gregersen et al (2014a) have been able to identify multidecadal climate oscillations (Ntegeka and Willems, 2008;Willems, 2013a) as well as climate-related changes in precipitation patterns over the past 140 years. Nevertheless, since this paper is based on evidently shorter rainfall series, it is assumed that no significant trends or climate changes in this period are present.…”
Section: Datamentioning
confidence: 99%
“…They provide valuable information to decision-making within design and analysis of urban water infrastructure, both for quantifying uncertainty and for studying non-stationary behaviour (e.g. Ntegeka and Willems, 2008;Madsen et al, 2009;Willems, 2013a, b;Gregersen et al, 2014). When using single site rain gauges for large catchments, simple areal reduction factors (ARFs) can be applied to account for the spatial distribution of extremes (e.g.…”
Section: General Statistical and Hydrometeorological Characterizationmentioning
confidence: 99%
“…This is because the Poison-cluster model is only parameterized on a monthly basis to reflect monthly variability. Larger scale internal variability may be significant as shown by Willems (2013) and Gregersen et al (2015) who identified multi-decadal oscillations in precipitation extremes. Advances in reflecting internal climate variability by the NSRP model at least on an inter-annual scale has made by Fatichi et al (2011).…”
Section: Climate Model and Sampling Uncertaintymentioning
confidence: 92%