Catchments
; Digital storage
; Frequency estimation
; Runoff
; Data assimilation
; Flood frequency
; Flood frequency distributions
; Memory properties
; Meta-Gaussian model
; Meteorological forcing
; Real-time updating
; Saturation excess
; Floods
; catchment
; climate forcing
; data assimilation
; flood frequency
; Gaussian method
; hydrological modeling
; infiltration
; peak flow
; perturbation
; real time
; spatial distribution
; Danube River
; Italy
; Po River
英文摘要:
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.
Fluvial Dynamics and Hydrology Research Group, Andalusian Institute of Earth System Research, University of Córdoba, Cordoba, Spain; Department DICAM, University of Bologna, Bologna, Italy
Recommended Citation:
Aguilar C,, Montanari A,, Polo M,et al. Real-time updating of the flood frequency distribution through data assimilation[J]. Hydrology and Earth System Sciences,2017-01-01,21(7)