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What Will the Weather Do? Forecasting Flood Losses Based on Oscillation Indices

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Atmospheric oscillations are known to drive the large-scale variability of hydrometeorological extremes in Europe, which can trigger flood events and losses. However, to date there are no studies that have assessed the combined influence of different large-scale atmospheric oscillations on the probabilities of flood losses occurring. Therefore, in this study we examine the relationship between five indices of atmospheric oscillation and four classes of flood losses probabilities at subregional European scales. In doing so, we examine different combinations of atmospheric oscillations, both synchronous and seasonally lagged. By applying logistic regressions, we aim to identify regions and seasons where probabilities of flood losses occurring can be estimated by indices of atmospheric oscillation with higher skill than historical probabilities. We show that classes of flood losses can be predicted by synchronous indices of atmospheric oscillation and that in some seasons and regions lagged relationships may exist between the indices of atmospheric oscillation and the probability of flood losses. Furthermore, we find that some models generate increased (or decreased) probability of flood losses occurring when the indices are at their extreme positive or negative phases. A better understanding of the effects of atmospheric oscillations on the likelihood of flood losses occurring represents a step forward in achieving flood resilience in Europe. For instance, improved early predictions of the indices that represent such atmospheric oscillations, or the evidence of a lagged relationship between their teleconnections and floods, can significantly contribute to mitigating the socioeconomic burden of floods.

Original languageEnglish
Article numbere2019EF001450
Pages (from-to)1-16
Number of pages16
JournalEarth's Future
Volume8
Issue number3
DOIs
Publication statusPublished - 12 Mar 2020

Funding

The research leading to this article is funded by the Horizon 2020 Framework programme through the project IMPREX (Grant Agreement 641811). Further funding was provided by an NWO-VICI grant (Grant 45314006) and an NWO-VIDI grant (Grant 016.161.324). We are very grateful to Munich Reinsurance Company for supplying data on flood losses from the NatCatSERVICE database. Flood losses records are available at the NatCatSERVICE database (https://natcatservice.munichre.com/). The research leading to this article is funded by the Horizon 2020 Framework programme through the project IMPREX (Grant Agreement 641811). Further funding was provided by an NWO‐VICI grant (Grant 45314006) and an NWO‐VIDI grant (Grant 016.161.324). We are very grateful to Munich Reinsurance Company for supplying data on flood losses from the NatCatSERVICE database. Flood losses records are available at the NatCatSERVICE database ( https://natcatservice.munichre.com/ ).

FundersFunder number
NWO-VIDI
NWO-VICI
NWO‐VIDI
Horizon 2020 Framework Programme641811
NWO‐VICI45314006
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    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 11 - Sustainable Cities and Communities
      SDG 11 Sustainable Cities and Communities

    Keywords

    • atmospheric oscillation
    • disaster risk reduction
    • ENSO
    • flood forecasting
    • impact-based forecasting
    • NAO

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