https://doi.org/10.5194/hess-2019-639 Preprint. Discussion started: 20 December 2019 c Author(s) 2019. CC BY 4.0 License. A time-varying parameter estimation approach using split-sample calibration based on dynamic programming Xiaojing Zhanga,b, Pan Liua,b* aState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China bHubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University *Corresponding author. Email:
[email protected]; Tel: +86-27-68775788; Fax: +86-27-68773568 1 / 58 https://doi.org/10.5194/hess-2019-639 Preprint. Discussion started: 20 December 2019 c Author(s) 2019. CC BY 4.0 License. 1 Abstract: Although the parameters of hydrological models are usually regarded as 2 constant, temporal variations can occur in a changing environment. Thus, effectively 3 estimating time-varying parameters becomes a significant challenge. Following a 4 survey of existing estimation methodologies, this paper describes a new method that 5 combines (1) the basic concept of split-sample calibration (SSC), whereby parameters 6 are assumed to be stable for one sub-period, and (2) the parameter continuity 7 assumption, i.e., the differences between parameters in consecutive time steps are small. 8 Dynamic programming is then used to determine the optimal parameter trajectory by 9 considering two objective functions: maximization of simulation accuracy and 10 maximization of parameter continuity. The efficiency of the proposed method is 11 evaluated by two synthetic experiments, one with a simple two-parameter monthly 12 model and the second using a more complex 15-parameter daily model.