EGU2020-18472
https://doi.org/10.5194/egusphere-egu2020-18472
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Multi-model data assimilation techniques for flood forecasts

Gwyneth Matthews1, Hannah Cloke1,2, Sarah Dance1,3, and Christel Prudhomme4,5,6
Gwyneth Matthews et al.
  • 1Department of Meteorology, University of Reading, Reading, United Kingdom of Great Britain and Northern Ireland
  • 2Department of Geography and Environmental Science, University of Reading, Reading, United Kingdom of Great Britain and Northern Ireland
  • 3Department of Mathematics and Statistics, University of Reading, Reading, United Kingdom of Great Britain and Northern Ireland
  • 4European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom of Great Britain and Northern Ireland
  • 5Department of Geography, Loughborough University, Loughborough, United Kingdom of Great Britain and Northern Ireland
  • 6Centre for Ecology and Hydrology, Wallingford, United Kingdom of Great Britain and Northern Ireland

Floods are the most common and disastrous natural hazards and due to climate change and socio-economic growth they are becoming more dangerous. Early warning systems are one of the best ways to decrease the effect of floods by increasing preparedness. The European Flood Awareness System (EFAS), part of the European Commission's Copernicus Emergency Management Service, provides medium-range ensemble flood forecasts for the whole of Europe but only calibrates its forecasts locally at river gauge stations where sufficiently long and reliable observations are available. These corrections do not consider the natural relationships that occur between points up and downstream. In this PhD project, data assimilation techniques will be used, in post-processing, to combine the available gauge observations with the forecasts. Using a weighting matrix, the influence of the observations will be extended along the river channel network, taking account of the ensemble and observation uncertainty. EFAS uses meteorological forcings from four numerical weather prediction (NWP) systems, so a multi-model approach will need to be developed.  This requires new data assimilation theory and hydrological process knowledge to ensure consistent updates. Delocalising calibrations will improve the accuracy of forecasts at unobserved locations allowing end-users to make more informed decisions to mitigate flood damage.

How to cite: Matthews, G., Cloke, H., Dance, S., and Prudhomme, C.: Multi-model data assimilation techniques for flood forecasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18472, https://doi.org/10.5194/egusphere-egu2020-18472, 2020

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