Articles | Volume 22, issue 8
https://doi.org/10.5194/nhess-22-2491-2022
© Author(s) 2022. 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-22-2491-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Aug 2022
Research article |
| 03 Aug 2022
| 03 Aug 2022
Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding
Mariana C. A. Clare
CORRESPONDING AUTHOR
Department of Earth Science and Engineering, Imperial College London, London, UK
Forecast department, ECMWF, Bonn, Germany
Tim W. B. Leijnse
Deltares, Delft, the Netherlands
Robert T. McCall
Deltares, Delft, the Netherlands
Ferdinand L. M. Diermanse
Deltares, Delft, the Netherlands
Colin J. Cotter
Department of Earth Science and Engineering, Imperial College London, London, UK
Matthew D. Piggott
Department of Earth Science and Engineering, Imperial College London, London, UK
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Short summary
Assessing uncertainty is computationally expensive because it requires multiple runs of expensive models. We take the novel approach of assessing uncertainty from coastal flooding using a multilevel multifidelity (MLMF) method which combines the efficiency of less accurate models with the accuracy of more expensive models at different resolutions. This significantly reduces the computational cost but maintains accuracy, making previously unfeasible real-world studies possible.
Assessing uncertainty is computationally expensive because it requires multiple runs of...
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