Sequential data assimilation for streamflow forecasting: assessing the sensitivity to uncertainties and updated variables of a conceptual hydrological model at basin scale

2021 ◽  
Author(s):  
G. Piazzi ◽  
G. Thirel ◽  
C. Perrin ◽  
O. Delaigue
Keyword(s):  
Data Assimilation ◽  
Hydrological Model ◽  
Sequential Data ◽  
Streamflow Forecasting ◽  
Basin Scale ◽  
Conceptual Hydrological Model ◽  
Sequential Data Assimilation

scholarly journals Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model

2017 ◽  
Vol 107 ◽  
pp. 301-316 ◽  
Author(s):  
M. Khaki ◽  
I. Hoteit ◽  
M. Kuhn ◽  
J. Awange ◽  
E. Forootan ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hydrological Model ◽  
Sequential Data ◽  
Grace Data ◽  
Sequential Data Assimilation

TREATMENT OF THE ERROR DUE TO UNRESOLVED SCALES IN SEQUENTIAL DATA ASSIMILATION

2011 ◽  
Vol 21 (12) ◽  
pp. 3619-3626 ◽  
Author(s):  
ALBERTO CARRASSI ◽  
STÉPHANE VANNITSEM
Keyword(s):  
Dynamical System ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Large Scale ◽  
Model Error ◽  
Environmental Modeling ◽  
Sequential Data ◽  
Chaotic Dynamical System ◽  
Error Covariance ◽  
Sequential Data Assimilation

In this paper, a method to account for model error due to unresolved scales in sequential data assimilation, is proposed. An equation for the model error covariance required in the extended Kalman filter update is derived along with an approximation suitable for application with large scale dynamics typical in environmental modeling. This approach is tested in the context of a low order chaotic dynamical system. The results show that the filter skill is significantly improved by implementing the proposed scheme for the treatment of the unresolved scales.

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