Multi-model data assimilation techniques for flood forecasts

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.