Articles | Volume 18, issue 5
https://doi.org/10.5194/nhess-18-1297-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-18-1297-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2018
Research article |
| 03 May 2018
| 03 May 2018
Multi-model ensembles for assessment of flood losses and associated uncertainty
Rui Figueiredo
CORRESPONDING AUTHOR
Scuola Universitaria Superiore IUSS Pavia, Pavia, Italy
GFZ German Research Centre for Geosciences, Sect. 5.4: Hydrology, Potsdam, Germany
Kai Schröter
GFZ German Research Centre for Geosciences, Sect. 5.4: Hydrology, Potsdam, Germany
Alexander Weiss-Motz
GFZ German Research Centre for Geosciences, Sect. 5.4: Hydrology, Potsdam, Germany
Mario L. V. Martina
Scuola Universitaria Superiore IUSS Pavia, Pavia, Italy
Heidi Kreibich
GFZ German Research Centre for Geosciences, Sect. 5.4: Hydrology, Potsdam, Germany
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- How does the selection of wave hindcast datasets and statistical models influence the probabilistic design of offshore scour protections? R. Figueiredo et al. 10.1016/j.oceaneng.2022.113123
- Probabilistic Flood Loss Models for Companies L. Schoppa et al. 10.1029/2020WR027649
- Preface: Damage of natural hazards: assessment and mitigation H. Kreibich et al. 10.5194/nhess-19-551-2019
- From Hazard to Consequences: Evaluation of Direct and Indirect Impacts of Flooding Along the Emilia-Romagna Coastline, Italy C. Armaroli et al. 10.3389/feart.2019.00203
- A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe S. Lüdtke et al. 10.1029/2019WR026213
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- Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification T. Sieg et al. 10.1371/journal.pone.0212932
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- What drives uncertainty surrounding riverine flood risks? I. Hosseini-Shakib et al. 10.1016/j.jhydrol.2024.131055
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Latest update: 02 Apr 2025
Short summary
Flood loss modelling is subject to large uncertainty that is often neglected. Most models are deterministic, and large disparities exist among them. Adopting a single model may lead to inaccurate loss estimates and sub-optimal decision-making. This paper proposes the use of multi-model ensembles to address such issues. We demonstrate that this can be a simple and pragmatic approach to obtain more accurate loss estimates and reliable probability distributions of model uncertainty.
Flood loss modelling is subject to large uncertainty that is often neglected. Most models are...
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