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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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This study evaluates how the sequential assimilation of flood extent derived from synthetic aperture radar data can help improving flood forecasting. In particular, we carried out twin experiments based on a synthetically generated data-set 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.
Preprints
https://doi.org/10.5194/hess-2020-403
https://doi.org/10.5194/hess-2020-403

  15 Sep 2020

15 Sep 2020

Review status: this preprint is currently under review for the journal HESS.

Assimilation of probabilistic flood maps from SAR data into ahydrologic-hydraulic forecasting model: a proof of concept

Concetta Di Mauro1,2, Renaud Hostache1, Patrick Matgen1, Ramona Pelich1, Marco Chini1, Peter Jan van Leeuwen2,4, Nancy Nichols2, and Günter Blöschl3 Concetta Di Mauro et al.
  • 1Luxembourg Insitute of Science and technology
  • 2University of Reading, UK
  • 3Vienna University of Technology
  • 4Department of Atmospheric Science, Colorado State University, USA

Abstract. Coupled hydrologic and hydraulic models represent powerful tools for simulating streamflow and water levels along the riverbed and in the floodplain. However, input data, model parameters, initial conditions and model structure represent sources of uncertainty that affect the reliability and accuracy of flood forecasts. Assimilation of satellite-based Synthetic Aperture Radar observations into a flood forecasting model are generally used to reduce such uncertainties. In this context, we evaluate how sequential assimilation of flood extent derived from synthetic aperture radar data can help in improving flood forecasts. In particular, we carried out twin experiments based on a synthetically generated data-set with controlled uncertainty. To this end, two assimilation methods are explored and compared: the Sequential Importance Sampling (standard method) and its enhanced method where a tempering coefficient is used to inflate the posterior probability (adapted method) and to reduce degeneracy. The experimental results show that the assimilation of SAR probabilistic flood maps significantly improves the predictions of streamflow and water elevation, thereby confirming the effectiveness of the data assimilation framework. In addition, the assimilation method significantly reduces the spatially averaged root mean square error of water levels with respect to the case without assimilation. The critical success index of predicted flood extent maps is significantly increased by the assimilation. While the standard method proves to be more accurate in estimating the water levels and streamflow at the assimilation time step, the adapted method enables a more persistent improvement of the forecasts. However, although the use of a tempering coefficient reduces the degeneracy problem, the accuracy of model simulation is lower at the assimilation time step.

Concetta Di Mauro et al.

 
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Concetta Di Mauro et al.

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Short summary
This study evaluates how the sequential assimilation of flood extent derived from synthetic aperture radar data can help improving flood forecasting. In particular, we carried out twin experiments based on a synthetically generated data-set 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.
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