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

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

doi:https://doi.org/10.5194/hess-25-4081-2021

Articles | Volume 25, issue 7

https://doi.org/10.5194/hess-25-4081-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-4081-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

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

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.