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 frequenc
