Abstract
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.
Highlights
The physical, chemical and ecological states of processes leading to the formation and quality of river flow are characterized by persistence at several different timescales (Koutsoyiannis, 2014)
From a technical point of view, we aim to propose a technique for updating a season in advance of the flood frequency distribution estimated for a given river, through a data assimilation approach, by exploiting the information provided by river flows in the pre-flood seasons
We assume that peak flows can be adequately modeled through the Extreme Value Type 1 (EV1) distribution and we present a comparison between the unconditioned peak flows frequency distribution and the updated peak flows frequency distributions
Summary
The physical, chemical and ecological states of processes leading to the formation and quality of river flow are characterized by persistence at several different timescales (Koutsoyiannis, 2014). The study of persistence has been one of the most classical research endeavors in hydrology since the early works by Rippl (1883) and Hazen (1914) on the estimation of the optimal storage for reservoirs. Hurst (1951) investigated the Nile River flows while working at the design of the Aswan Dam and postulated that geophysical records may be affected by a complex form of persistence that may last for a long time (O’Connell et al, 2016). Later on, Thomas and Fiering (1962) and Yevjevich (1963) introduced autoregressive models for annual and seasonal streamflow simulation, thereby stimulating the development of subsequent models of increasing complexity for simulating hydrological persistence
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