Paper Title:
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.

  Info
Periodical
Advanced Materials Research (Volumes 255-260)
Edited by
Jingying Zhao
Pages
3632-3636
DOI
10.4028/www.scientific.net/AMR.255-260.3632
Citation
J. Xiong, X. L. Huang, Z. Y. Cao, "Assimilating Observation Data into Hydrological Model with Ensemble Kalman Filter", Advanced Materials Research, Vols. 255-260, pp. 3632-3636, 2011
Online since
May 2011
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Price
$32.00
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