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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2018-589
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-589
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 20 Dec 2018

Research article | 20 Dec 2018

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Hydrology and Earth System Sciences (HESS) and is expected to appear here in due course.

Observation operators for assimilation of satellite observations in fluvial inundation forecasting

Elizabeth S. Cooper1, Sarah L. Dance1,2, Javier García-Pintado3, Nancy K. Nichols1,2, and Polly Smith1 Elizabeth S. Cooper et al.
  • 1Department of Meteorology, University of Reading,UK
  • 2Department of Mathematics and Statistics, University of Reading, UK
  • 3MARUM Center for Marine environmental Sciences and Department of Geosciences, University of Bremen, Germany

Abstract. Images from satellite-based synthetic aperture radar (SAR) instruments contain large amounts of information about the position of flood water during a river flood event. This observational information typically covers a large spatial area, but is only relevant for a short time if water levels are changing rapidly. Data assimilation allows us to combine valuable SAR-derived observed information with continuous predictions from a computational hydrodynamic model and thus to produce a better forecast than using the model alone. In order to use observations in this way a suitable observation operator is required. In this paper we show that different types of observation operator can produce very different corrections to predicted water levels; this impacts on the quality of the forecast produced. We discuss the physical mechanisms by which different observation operators update modelled water levels and introduce a novel observation operator for inundation forecasting. The performance of the new operator is compared in synthetic experiments with that of two more conventional approaches. The conventional approaches both use observations of water levels derived from SAR to correct model predictions. Our new operator is instead designed to use backscatter values from SAR instruments as observations; such an approach has not been used before in an ensemble Kalman filtering framework. Direct use of backscatter observations opens up the possibility of using more information from each SAR image and could potentially speed up the time taken to produce observations needed to update model predictions. We compare the strengths and weaknesses of the three different approaches with reference to the physical mechanisms by which each of the observation operators allow data assimilation to update water levels in synthetic twin experiments in an idealised domain.

Elizabeth S. Cooper et al.
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Elizabeth S. Cooper et al.
Elizabeth S. Cooper et al.
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Short summary
Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river flood water, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help keep these forecasts on track. Here we investigate different ways of using information from satellite images, and look at the effect on computer predictions. This will help to develop flood warning systems.
Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to...
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