Articles | Volume 20, issue 12
https://doi.org/10.5194/hess-20-4949-2016
https://doi.org/10.5194/hess-20-4949-2016
Research article
 | 
16 Dec 2016
Research article |  | 16 Dec 2016

Identification of hydrological model parameter variation using ensemble Kalman filter

Chao Deng, Pan Liu, Shenglian Guo, Zejun Li, and Dingbao Wang

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Latest update: 18 Jul 2024
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Short summary
Hydrological model parameters may vary in time under nonstationary conditions, i.e., climate change and anthropogenic activities. The technique of the ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model. Through a synthesis experiment and two case studies, the EnKF is demonstrated to be useful for the identification of parameter variations.