Identification of hydrological model parameters variation using ensemble Kalman filter
Chao Deng1,2, Pan Liu1,2, Shenglian Guo1,2, Zejun Li1,2, and Dingbao Wang31State Key Laboratory of Water Resources and Hydropower Engineer ing Science, Wuhan University, Wuhan, China 2Hubei Provincial Collaborative Innovation Center for Water Reso urces Security, Wuhan, China 3Department of Civil, Environmen tal & Construction Engineering, University of Central Florida, Orlando, USA
Received: 23 Sep 2015 – Accepted for review: 25 Jan 2016 – Discussion started: 26 Jan 2016
Abstract. Hydrological model parameters play an important role in the ability of model prediction. In a stationary content, parameters of hydrological models are treated as constants. However, model parameters may vary dynamically with time under climate change and human activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model by assimilating the runoff observations, where one of state equations is that the model parameters should not change much within a short time period. Through a synthetic experiment, the proposed method is evaluated with various types of parameter variations including trend, abrupt change, and periodicity. The application of the method to the Wudinghe basin shows that the water storage capacity, a parameter in the model, has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of water storage capacity is explained by land use and land cover changes due to soil and water conservation measurements. Whereas, the application to the Tongtianhe basin demonstrates that the parameter of water storage capacity has no significant variation during the simulation of 1982–2013, corresponding to the relatively stationary catchment characteristics. Additionally, the proposed method improves the performance of hydrological modeling, and provides an effective tool for quantifying temporal variation of model parameters.
Deng, C., Liu, P., Guo, S., Li, Z., and Wang, D.: Identification of hydrological model parameters variation using ensemble Kalman filter, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2015-407, 2016.