Articles | Volume 21, issue 1
https://doi.org/10.5194/hess-21-251-2017
https://doi.org/10.5194/hess-21-251-2017
Research article
 | 
11 Jan 2017
Research article |  | 11 Jan 2017

Physically based distributed hydrological model calibration based on a short period of streamflow data: case studies in four Chinese basins

Wenchao Sun, Yuanyuan Wang, Guoqiang Wang, Xingqi Cui, Jingshan Yu, Depeng Zuo, and Zongxue Xu

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Cited articles

Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large area hydrologic modeling and assessment – Part 1: Model development, J. Am. Water. Resour. As., 34, 73–89, 1998.
Beven, K.: How far can we go in distributed hydrological modelling?, Hydrol. Earth Syst. Sci., 5, 1–12, https://doi.org/10.5194/hess-5-1-2001, 2001.
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, 1992.
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, 2001.
Callahan, B., Miles, E., and Fluharty, D.: Policy implications of climate forecasts for water resources management in the Pacific Northwest, Pol. Sci., 32, 269–293, 1999.
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Short summary
The possibility of using a short period of streamflow data (less than one year) to calibrate a physically based distributed hydrological model is evaluated. Contrary to the common understanding of using data of several years, it is shown that only using data covering several months could calibrate the model effectively, which indicates that this approach is valuable for solving the calibration problem of such models in data-sparse basins.