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Bottom-up identification of key elements of compound events
  • +17
  • Emanuele Bevacqua,
  • Carlo De Michele,
  • Colin Manning,
  • Anaıs Couasnon,
  • Andreia F S Ribeiro,
  • Alexandre M Ramos,
  • Edoardo Vignotto,
  • Ana Bastos,
  • Suzana Blesic,
  • Fabrizio Durante,
  • John Hillier,
  • Sérgio C Oliveira,
  • Joaquim G Pinto,
  • Elisa Ragno,
  • Pauline Rivoire,
  • Kate Saunders,
  • Karin Van Der Wiel,
  • Wenyan Wu,
  • Tianyi Zhang,
  • Jakob Zscheischler
Emanuele Bevacqua
Helmholtz Centre for Environmental Research - UFZ

Corresponding Author:[email protected]

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Carlo De Michele
Politecnico di Milano
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Colin Manning
Newcastle University
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Anaıs Couasnon
Vrije Universiteit Amsterdam
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Andreia F S Ribeiro
ETH Zurich
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Alexandre M Ramos
Instituto Dom Luiz (IDL)
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Edoardo Vignotto
University of Geneva
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Ana Bastos
Max Planck Institute for Biogeochemistry
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Suzana Blesic
University of Belgrade and Center for Participatory Science
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Fabrizio Durante
University of Salento
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John Hillier
Loughborough University
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Sérgio C Oliveira
Universidade de Lisboa
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Joaquim G Pinto
Karlsruhe Institute of Technology
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Elisa Ragno
Delft University of Technology
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Pauline Rivoire
University of Bern
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Kate Saunders
Queensland University of Technology
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Karin Van Der Wiel
Royal Netherlands Meteorological Institute (KNMI)
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Wenyan Wu
The University of Melbourne
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Tianyi Zhang
Chinese Academy of Sciences
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Jakob Zscheischler
Helmholtz Centre for Environmental Research -UFZ
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Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (1) preconditioned, (2) multivariate, (3) temporally compounding, and (4) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, based on a bottom-up approach, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (1) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (2) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non-stationary multivariate process. For instance, future mean sea-level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (3) In Portugal, deep-landslides are often caused by temporal clusters of moderate precipitation events. Finally, (4) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors.