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Proceedings of the International Association of Hydrological Sciences An open-access publication for refereed proceedings in hydrology
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Volume 373
Proc. IAHS, 373, 189-192, 2016
https://doi.org/10.5194/piahs-373-189-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Proc. IAHS, 373, 189-192, 2016
https://doi.org/10.5194/piahs-373-189-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  12 May 2016

12 May 2016

Long term prediction of flood occurrence

Cristina Aguilar1, Alberto Montanari2, and María José Polo3 Cristina Aguilar et al.
  • 1Fluvial dynamics and hydrology research group, Andalusian Institute of Earth System Research, University of Granada, Granada, Spain
  • 2Department DICAM, University of Bologna, Bologna, Italy
  • 3Fluvial dynamics and hydrology research group, Andalusian Institute of Earth System Research, University of Cordoba, Cordoba, Spain

Abstract. How long a river remembers its past is still an open question. Perturbations occurring in large catchments may impact the flow regime for several weeks and months, therefore providing a physical explanation for the occasional tendency of floods to occur in clusters. The research question explored in this paper may be stated as follows: can higher than usual river discharges in the low flow season be associated to a higher probability of floods in the subsequent high flow season? The physical explanation for such association may be related to the presence of higher soil moisture storage at the beginning of the high flow season, which may induce lower infiltration rates and therefore higher river runoff. Another possible explanation is persistence of climate, due to presence of long-term properties in atmospheric circulation. We focus on the Po River at Pontelagoscuro, whose catchment area amounts to 71000km2. We look at the stochastic connection between average river flows in the pre-flood season and the peak flows in the flood season by using a bivariate probability distribution. We found that the shape of the flood frequency distribution is significantly impacted by the river flow regime in the low flow season. The proposed technique, which can be classified as a data assimilation approach, may allow one to reduce the uncertainty associated to the estimation of the flood probability.

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