Articles | Volume 25, issue 7
Research article
14 Jul 2021
Research article |  | 14 Jul 2021

Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept

Concetta Di Mauro​​​​​​​, Renaud Hostache, Patrick Matgen, Ramona Pelich, Marco Chini, Peter Jan van Leeuwen, Nancy K. Nichols, and Günter Blöschl

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Cited articles

Andreadis, K. M., Clark, E. A., Lettenmaier, D. P., and Alsdorf, D. E.: Prospects for river discharge and depth estimation through assimilation of swath-altimetry into a raster-based hydrodynamics model, Geophys. Res. Lett., 34, L10403,, 2007. a, b
Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Trans. Sig. Process., 50, 174–188,, 2002. a
Bates, P. and Roo, A. D.: A simple raster-based model for flood inundation simulation, J. Hydrol., 236, 54–77,, 2000. a, b
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
Bates, P., Horritt, M., Wilson, M., Hunter, N., Fewtrell, T., Trigg, M., Neal, J., de Almeida, G., and Sampson, C.: LISFLOOD-FP shareware version, Code release 5.9.6, available at:, last access: 5 July 2021. a
Short summary
This study evaluates how the sequential assimilation of flood extent derived from synthetic aperture radar data can help improve flood forecasting. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. Our empirical results demonstrate the efficiency of the proposed data assimilation framework, as forecasting errors are substantially reduced as a result of the assimilation.