Articles | Volume 21, issue 7
Hydrol. Earth Syst. Sci., 21, 3687–3700, 2017
Hydrol. Earth Syst. Sci., 21, 3687–3700, 2017

Research article 20 Jul 2017

Research article | 20 Jul 2017

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

Cristina Aguilar1, Alberto Montanari2, and María-José Polo1 Cristina Aguilar et al.
  • 1Fluvial dynamics and hydrology research group, Andalusian Institute of Earth System Research, University of Córdoba, Cordoba, 14071, Spain
  • 2Department DICAM, University of Bologna, Bologna, 40136, Italy

Abstract. We explore the memory properties of catchments for predicting the likelihood of floods based 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 by higher-than-usual storage in the catchment. To exploit the above sensitivity to long-term perturbations, a meta-Gaussian model and a data assimilation approach are implemented for updating the flood frequency distribution a season in advance. Accordingly, the peak flow in the flood season is predicted in probabilistic terms 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 the flood frequency distribution is noticeably impacted by higher-than-usual flows occurring up to several months earlier. The proposed technique may allow one to reduce the uncertainty associated with the estimation of flood frequency.

Short summary
Assuming that floods are driven by both short- (meteorological forcing) and long-term perturbations (higher-than-usual moisture), we propose a technique for updating a season in advance the flood frequency distribution. Its application in the Po and Danube rivers helped to reduce the uncertainty in the estimation of floods and thus constitutes a promising tool for real-time management of flood risk mitigation. This study is the result of the stay of the first author at the University of Bologna.