Articles | Volume 25, issue 7
Hydrol. Earth Syst. Sci., 25, 4081–4097, 2021
https://doi.org/10.5194/hess-25-4081-2021
Hydrol. Earth Syst. Sci., 25, 4081–4097, 2021
https://doi.org/10.5194/hess-25-4081-2021

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​​​​​​​ et al.

Data sets

ERA5 hourly data on single levels from 1979 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.adbb2d47

Model code and software

LISFLOOD-FP shareware version P. Bates, M. Horritt, M. Wilson, N. Hunter, T. Fewtrell, M. Trigg, J. Neal, G. de Almeida, and C. Sampson http://www.bristol.ac.uk/geography/research/hydrology/models/lisflood

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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.