Real time updating of the flood frequency distribution through data assimilation

Abstract

We explore the memory properties of catchments for predicting the likelihood of floods basing on observations of average flows in pre-flood seasons. Our approach assumes that flood formation is driven by the superimposition of short and long term perturbations. The former is given by the short term meteorological forcing leading to infiltration and/or saturation excess, while the latter is originated 15 by higher-than-usual storage in the catchment. To exploit the above sensitivity to long term perturbations a Meta-Gaussian model is implemented for updating a season in advance the flood frequency distribution, through a data assimilation approach. Accordingly, the peak flow in the flood season is predicted by exploiting its dependence on the average flow in the antecedent seasons. We focus on the Po River at Pontelagoscuro and the Danube river at Bratislava. We found that the shape of 20 the flood frequency distribution is significantly impacted by higher-than-usual flows occurred up to several months earlier. The proposed technique may allow one to reduce the uncertainty associated to the estimation of flood frequency