Dual state-parameter simultaneous estimation using localised ensemble Kalman filter and application in environmental model Online publication date: Thu, 17-Dec-2015
by Yan Li; Chong Chen; Jian Zhou; Gaofeng Zhang; Xiaolong Chen
International Journal of Embedded Systems (IJES), Vol. 8, No. 1, 2016
Abstract: Parameters in a hydrological model play a pivotal role for prediction. Good estimates of the parameters and state variables enable the model to generate accurate forecasts. In this paper, a dual state-parameter estimation approach is realised based on the localised ensemble Kalman filter (EnKF) for estimation of both parameters and state variables of a groundwater flow model. The hydraulic head field (state variable) and the distribution of heterogeneous hydraulic conductivity (parameter) are simultaneously estimated through limited groundwater level observations and the localisation method. The localisation method is used to map updated weights and correlation distances from measured grid point to the grid point to be updated. The horizontal de-correlation length is determined by geostatistical method with a value L = 25 m. The analysed results indicate that the distribution of hydraulic conductivity estimated by the localised EnKF approach could well match the real field only after five data assimilation steps with few 25 observation wells. Meanwhile, the simulation of hydraulic conductivity is significantly improved by the localised EnKF approach.
Online publication date: Thu, 17-Dec-2015
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