Abstract. Although the parameters of hydrological models are usually regarded as constant, temporal variations can occur in a changing environment. Thus,
effectively estimating time-varying parameters becomes a significant challenge. Two methods, including split-sample calibration (SSC) and data
assimilation, have been used to estimate time-varying parameters. However, SSC is unable to consider the parameter temporal continuity, while data
assimilation assumes parameters vary at every time step. This study proposed a new method that combines (1) the basic concept of split-sample
calibration, whereby parameters are assumed to be stable for one sub-period, and (2) the parameter continuity assumption; i.e. the
differences between parameters in consecutive time steps are small. Dynamic programming is then used to determine the optimal parameter trajectory
by considering two objective functions: maximization of simulation accuracy and maximization of parameter continuity. The efficiency of the proposed
method is evaluated by two synthetic experiments, one with a simple 2-parameter monthly model and the second using a more complex 15-parameter
daily model. The results show that the proposed method is superior to SSC alone and outperforms the ensemble Kalman filter if the proper sub-period
length is used. An application to the Wuding River basin indicates that the soil water capacity parameter varies before and after 1972, which can be
interpreted according to land use and land cover changes. A further application to the Xun River basin shows that parameters are generally
stationary on an annual scale but exhibit significant changes over seasonal scales. These results demonstrate that the proposed method is an
effective tool for identifying time-varying parameters in a changing environment.
Data assimilation in large time-varying multidimensional fields