Articles | Volume 21, issue 6
https://doi.org/10.5194/hess-21-2701-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-2701-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy)
Emanuele Bevacqua
CORRESPONDING AUTHOR
Wegener Center for Climate and Global Change, University of Graz, Graz, Austria
Douglas Maraun
Wegener Center for Climate and Global Change, University of Graz, Graz, Austria
Ingrid Hobæk Haff
Department of Mathematics, University of Oslo, Oslo, Norway
Martin Widmann
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
Mathieu Vrac
Laboratoire des Sciences du Climat et de l'Environnement, CNRS/IPSL, Gif-sur-Yvette, France
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- Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
- Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods W. Zhang et al. 10.3389/feart.2021.673495
- Simultaneous flood risk analysis and its future change among all the 109 class-A river basins in Japan using a large ensemble climate simulation database d4PDF T. Tanaka et al. 10.1088/1748-9326/abfb2b
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- Compound Events under Global Warming: A Dependence Perspective Z. Hao & V. Singh 10.1061/(ASCE)HE.1943-5584.0001991
- Regional analysis of multivariate compound coastal flooding potential around Europe and environs: sensitivity analysis and spatial patterns P. Camus et al. 10.5194/nhess-21-2021-2021
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Latest update: 03 Jun 2023
Short summary
We develop a conceptual model to quantify the risk of compound events (CEs), i.e. extreme impacts to society which are driven by statistically dependent climatic variables. Based on this model we study compound floods, i.e. joint storm surge and high river level, in Ravenna (Italy). The model includes meteorological predictors which (1) provide insight into the physical processes underlying CEs, as well as into the temporal variability, and (2) allow us to statistically downscale CEs.
We develop a conceptual model to quantify the risk of compound events (CEs), i.e. extreme...