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

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

doi:https://doi.org/10.5194/hess-22-5299-2018

Articles | Volume 22, issue 10

https://doi.org/10.5194/hess-22-5299-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-22-5299-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 10

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/

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.