Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 22, issue 8
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.
https://doi.org/10.5194/nhess-22-2491-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 8
Research article
 | 
03 Aug 2022
Research article |  | 03 Aug 2022

Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding

Mariana C. A. Clare, Tim W. B. Leijnse, Robert T. McCall, Ferdinand L. M. Diermanse, Colin J. Cotter, and Matthew D. Piggott

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Cited articles

Athanasiou, P., van Dongeren, A., Giardino, A., Vousdoukas, M. I., Ranasinghe, R., and Kwadijk, J.: Uncertainties in projections of sandy beach erosion due to sea level rise: an analysis at the European scale, Sci. Rep., 10, 1–14, 2020. a
Bates, P. D., Horritt, M. S., and Fewtrell, T. J.: A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling, J. Hydrol., 387, 33–45, 2010. a, b, c, d
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Callaghan, D. P., Ranasinghe, R., and Roelvink, D.: Probabilistic estimation of storm erosion using analytical, semi-empirical, and process based storm erosion models, Coast. Eng., 82, 64–75, 2013. a, b
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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.
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