Satellite-supported flood forecasting in river networks: A real case study

Journal of Hydrology, Volume 523, April 2015, Pages 706–724.

Javier García-Pintado1,2, David C. Mason3, Sarah L. Dance1,2, Hannah L. Cloke1,3, Jeff C. Neal4, Jim Freer4, Paul D. Bates4.


  1. School of Mathematical and Physical Sciences, University of Reading, UK
  2. National Centre for Earth Observation, University of Reading, Reading, UK
  3. School of Archaelogy, Geography and Environmental Science, University of Reading, UK
  4. School of Geographical Sciences, University of Bristol, Bristol, UK



Satellite-based (e.g., Synthetic Aperture Radar [SAR]) water level observations (WLOs) of the floodplain can be sequentially assimilated into a hydrodynamic model to decrease forecast uncertainty. This has the potential to keep the forecast on track, so providing an Earth Observation (EO) based flood forecast system. However, the operational applicability of such a system for floods developed over river networks requires further testing. One of the promising techniques for assimilation in this field is the family of ensemble Kalman (EnKF) filters. These filters use a limited-size ensemble representation of the forecast error covariance matrix. This representation tends to develop spurious correlations as the forecast-assimilation cycle proceeds, which is a further complication for dealing with floods in either urban areas or river junctions in rural environments. Here we evaluate the assimilation of WLOs obtained from a sequence of real SAR overpasses (the X-band COSMO-Skymed constellation) in a case study. We show that a direct application of a global Ensemble Transform Kalman Filter (ETKF) suffers from filter divergence caused by spurious correlations. However, a spatially-based filter localization provides a substantial moderation in the development of the forecast error covariance matrix, directly improving the forecast and also making it possible to further benefit from a simultaneous online inflow error estimation and correction. Additionally, we propose and evaluate a novel along-network metric for filter localization, which is physically-meaningful for the flood over a network problem. Using this metric, we further evaluate the simultaneous estimation of channel friction and spatially-variable channel bathymetry, for which the filter seems able to converge simultaneously to sensible values. Results also indicate that friction is a second order effect in flood inundation models applied to gradually varied flow in large rivers. The study is not conclusive regarding whether in an operational situation the simultaneous estimation of friction and bathymetry helps the current forecast. Overall, the results indicate the feasibility of stand-alone EO-based operational flood forecasting.

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Significance Statement: 

Improving Flood forecasting accuracy is essential for better safety to the public. The authors evaluated and improved the family of ensemble Kalman (EnKF) filters which resulted in improved forecast. Moreover, novel along-network metric for filter localization was proposed.

Satellite-supported flood forecasting in river networks

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