Articles | Volume 25, issue 2
Hydrol. Earth Syst. Sci., 25, 711–733, 2021
https://doi.org/10.5194/hess-25-711-2021
Hydrol. Earth Syst. Sci., 25, 711–733, 2021
https://doi.org/10.5194/hess-25-711-2021

Research article 18 Feb 2021

Research article | 18 Feb 2021

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

Xiaojing Zhang and Pan Liu

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Cited articles

Alvisi, S., Mascellani, G., Franchini, M., and Bárdossy, A.: Water level forecasting through fuzzy logic and artificial neural network approaches, Hydrol. Earth Syst. Sci., 10, 1–17, https://doi.org/10.5194/hess-10-1-2006, 2006. 
Bellman, R.: Dynamic programming, Princeton University Press, Princeton, 1957. 
Broderick, C., Matthews, T., Wilby, R. L., Bastola, S., and Murphy, C.: Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods, Water Resour. Res., 52, 8343–8373, https://doi.org/10.1002/2016wr018850, 2016. 
Bronstert, A.: Rainfall-runoff modelling for assessing impacts of climate and land-use change, Hydrol. Process., 18, 567–570, https://doi.org/10.1002/hyp.5500, 2004. 
Chen, Y. and Zhang, D.: Data assimilation for transient flow in geologic formations via ensemble Kalman filter, Adv. Water Resour., 29, 1107–1122, https://doi.org/10.1016/j.advwatres.2005.09.007, 2006. 
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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 effective for identifying temporal variations of parameters and can simultaneously provide good streamflow simulation.