Assimilating Observation Data into Hydrological Model with Ensemble Kalman Filter

Jun Xiong; Xiao Lan Huang; Zeng Yan Cao

doi:10.4028/www.scientific.net/AMR.255-260.3632

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 255-260Assimilating Observation Data into Hydrological...

Assimilating Observation Data into Hydrological Model with Ensemble Kalman Filter

Abstract:

The ensemble Kalman filter (EnKF) is employed to simulate of streamflow of a slope sub-catchment during the rainfall infiltration process. With this method the whole process is treated as a dynamic stochastic system, and its streamflow is taken as the variable to describe the state of system. Furthermore, it is coupled with a hydrology model to cope with system uncertainty. Thus, the dynamical estimation of hydrological parameters is performed; the model variables and their uncertainty are obtained simultaneously. Numerical examples show that this strategy can effectively deal with observation noises and can provide the inversion results and the posteriori distribution of the priori information together. Compared with the conventional optimization algorithm, the new strategy combined with EnKF shows better character of real time response and model reliability.

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Periodical:

Advanced Materials Research (Volumes 255-260)

Pages:

3632-3636

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.255-260.3632

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Online since:

May 2011

Authors:

Jun Xiong, Xiao Lan Huang, Zeng Yan Cao

Keywords:

Data Assimilation, Ensemble Kalman Filter, Hydrological Forecast, Numerical Simulation, Rainfall Infiltration, Tank Model

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