EnKF-based synchronous estimation of parameters and variables of Xinanjiang model
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Abstract:
The data assimilation method can improve the timeliness and accuracy of numerical fo recasting , and has been applied and developing rapidly in the field of hydrology . In order to improve the accuracy of runoff fo recast of Xinanjiang model, we adopted the ensemble Kalman filter method for synchronous correction of the model parameters and state variables. We designed anumerical experiment of the three-component Xinanjiang model under ideal conditions, and analyzed the effects of the mean and variance of parameters, the ensemble size, and the sensitivity and correlation of parameters on the data assimilation with consideration to the uncertainty of the model itself, model parameters, and the observation data. Results showed that the ensemble Kalman filter algorithm is feasible. Moreover, the accur cy of data assimilation can be improved when the mean value of the parameter is closer to the true value, the variance is increased appropriately , the ensemble size is proper, the sensitivity of parameters is lower, and the correlation between parameters and variables is small. This study can provide a reference for similar research of data assimilation.
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