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https://doi.org/10.5194/hess-2019-639
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-639
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 20 Dec 2019

Submitted as: research article | 20 Dec 2019

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This preprint is currently under review for the journal HESS.

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

Xiaojing Zhang1,2 and Pan Liu1,2 Xiaojing Zhang and Pan Liu
  • 1State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
  • 2Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University

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. Following a survey of existing estimation methodologies, this paper describes a new method that combines (1) the basic concept of split-sample calibration (SSC), 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 two-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. 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.

Xiaojing Zhang and Pan Liu

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Xiaojing Zhang and Pan Liu

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Short summary
Rainfall-runoff models are useful tools for streamflow simulation, however, efforts are needed to investigate how their parameters vary in response to climate changes and human activities. Thus, this study proposes a new method for estimating time-varying parameters, by considering both simulation accuracy and parameter continuity. The results show the proposed method is more effective for identifying temporal variations of parameters, and can simultaneously provide good streamflow simulation.
Rainfall-runoff models are useful tools for streamflow simulation, however, efforts are needed...
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