Articles | Volume 22, issue 10
https://doi.org/10.5194/hess-22-5299-2018
https://doi.org/10.5194/hess-22-5299-2018
Research article
 | 
16 Oct 2018
Research article |  | 16 Oct 2018

Discharge hydrograph estimation at upstream-ungauged sections by coupling a Bayesian methodology and a 2-D GPU shallow water model

Alessia Ferrari, Marco D'Oria, Renato Vacondio, Alessandro Dal Palù, Paolo Mignosa, and Maria Giovanna Tanda

Data sets

Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST: documentation and instructions M. N. Fienen, M. D'Oria, J. E. Doherty, and R. J. Hunt https://pubs.usgs.gov/tm/07/c09/

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Short summary
The knowledge of discharge hydrographs is useful for flood modelling purposes, water resource management, and the design of hydraulic structures. This paper presents a novel methodology to estimate the unknown discharge hydrograph in an ungauged river section using only water level information recorded downstream. A Bayesian procedure is coupled with a 2-D hydraulic model parallelized for GPUs. Finally, the proposed procedure has been applied to estimate inflow hydrographs in real river reaches.