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We explore the memory properties of catchments for predicting the\udlikelihood of floods based on observations of average flows in pre-flood\udseasons. Our approach assumes that flood formation is driven by the\udsuperimposition of short- and long-term perturbations. The former is given by\udthe short-term meteorological forcing leading to infiltration and/or\udsaturation excess, while the latter is originated by higher-than-usual\udstorage in the catchment. To exploit the above sensitivity to long-term\udperturbations, a meta-Gaussian model and a data assimilation approach are\udimplemented for updating the flood frequency distribution a season in\udadvance. Accordingly, the peak flow in the flood season is predicted in\udprobabilistic terms by exploiting its dependence on the average flow in the\udantecedent seasons. We focus on the Po River at Pontelagoscuro and the Danube\udRiver at Bratislava. We found that the shape of the flood frequency\uddistribution is noticeably impacted by higher-than-usual flows occurring up\udto several months earlier. The proposed technique may allow one to reduce the\uduncertainty associated with the estimation of flood frequency

Real-time updating of the flood frequency distribution through data assimilation