Articles | Volume 20, issue 12
https://doi.org/10.5194/hess-20-4949-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-4949-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
16 Dec 2016
Research article |
| 16 Dec 2016
Identification of hydrological model parameter variation using ensemble Kalman filter
Chao Deng
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
Shenglian Guo
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
Zejun Li
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
Dingbao Wang
Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL, USA
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Latest update: 17 Jul 2024
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.
Hydrological model parameters may vary in time under nonstationary conditions, i.e., climate...
