2017
DOI: 10.1088/1748-9326/aa7c22
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The role of climate variability in extreme floods in Europe

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Cited by 65 publications
(73 citation statements)
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References 71 publications
(85 reference statements)
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“…Further, Steirou et al () note the importance of the winter NAO state for the amplitude and timing of spring snowmelt as well as the varying importance of other modes of climate variability such as the Scandinavia (SCA) and the East Atlantic/West Russian (EA/WR) patterns. These findings resonate with those of Nobre, Jongman, Aerts, and Ward () in relation to the influence of the NAO and EA patterns on extreme rainfall, flood occurrence, and flood damage across Europe. For the eastern United States, stream flow demonstrates strong seasonal and NAO strength dependency (Coleman & Budikova, ; Sheldon & Burd, ).…”
Section: Modes Of Climate Variability and River Flowsupporting
confidence: 89%
“…Further, Steirou et al () note the importance of the winter NAO state for the amplitude and timing of spring snowmelt as well as the varying importance of other modes of climate variability such as the Scandinavia (SCA) and the East Atlantic/West Russian (EA/WR) patterns. These findings resonate with those of Nobre, Jongman, Aerts, and Ward () in relation to the influence of the NAO and EA patterns on extreme rainfall, flood occurrence, and flood damage across Europe. For the eastern United States, stream flow demonstrates strong seasonal and NAO strength dependency (Coleman & Budikova, ; Sheldon & Burd, ).…”
Section: Modes Of Climate Variability and River Flowsupporting
confidence: 89%
“…Flooding at individual stations is known to exhibit serial correlation (Douglas et al, 2000;Mallakpour et al, 2017), with flood-rich and flood-poor periods; our results show that at a larger scale, years with widespread flooding are also clustered in time. However, although flood risks in Europe are known to be affected by the El Niño-Southern Oscillation and the North Atlantic Oscillation (Emerton et al, 2017;Nobre et al, 2017;Ward et al, 2014), neither has a measurable correlation with the average European flood synchrony scale ( Figure S5). However, although flood risks in Europe are known to be affected by the El Niño-Southern Oscillation and the North Atlantic Oscillation (Emerton et al, 2017;Nobre et al, 2017;Ward et al, 2014), neither has a measurable correlation with the average European flood synchrony scale ( Figure S5).…”
Section: Resultsmentioning
confidence: 99%
“…This serial correlation in flood synchrony suggests a possible link to drivers that vary over multiannual time scales, such as long-term climatic oscillations. However, although flood risks in Europe are known to be affected by the El Niño-Southern Oscillation and the North Atlantic Oscillation (Emerton et al, 2017;Nobre et al, 2017;Ward et al, 2014), neither has a measurable correlation with the average European flood synchrony scale ( Figure S5). This disconnect may arise from regional differences on how El Niño-Southern Oscillation and the North Atlantic Oscillation affect flood risks in Europe (Emerton et al, 2017;Nobre et al, 2017;Ward et al, 2014).…”
Section: 1029/2018gl081883mentioning
confidence: 99%
“…Furthermore, positive NAO (in winter) and negative phases of NAO (in summer) and EAWR (in fall) are related to an increase in the probability of Low and Medium Damaging flood events. In northern Europe, during winter months rainfall variability is strongly modulated by the NAO and the EA, in which their positive phases are linked to higher than average and more intense extreme rainfall in the United Kingdom, Ireland, and in the Scandinavian countries (Casanueva et al, 2014;Guimarães Nobre et al, 2017). In addition, the combination of positive SOI and negative NAO has previously been linked with increased precipitation and high peak discharges in northern European regions in summer (Shaman, 2014;Zanardo et al, 2019), whereas negative EAWR is related to wetter autumns in the latter regions (Casanueva et al, 2014).…”
Section: 1029/2019ef001450mentioning
confidence: 99%
“…We use the NatCatSERVICE dataset of Munich Re (2016) to derive time-series of direct tangible flood losses. This dataset registers flood events in Europe, and their respective period, timing, location, and damages (in US$) since 1980, and has been widely used in previous studies (e.g., Bischiniotis et al, 2018;Guimarães Nobre et al, 2017;Hoeppe, 2016). We adjust the nominal flood losses value into 2016 US$ values according to the inflation rate obtained from the World Development indicators produced by the World Bank (available at https://data.worldbank.org/indicator/ny.gdp.defl.zs).…”
Section: Step 1: Extracting Indicators 211 Extracting Flood Lossesmentioning
confidence: 99%