Articles | Volume 21, issue 1
Hydrol. Earth Syst. Sci., 21, 251–265, 2017

Special issue: Modeling hydrological processes and changes

Hydrol. Earth Syst. Sci., 21, 251–265, 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 Sun1,2, Yuanyuan Wang1,2, Guoqiang Wang1,2, Xingqi Cui1,2, Jingshan Yu1, Depeng Zuo1,2, and Zongxue Xu1,2 Wenchao Sun et al.
  • 1College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China
  • 2Joint Center for Global Change Studies (JCGCS), Beijing 100875, China

Abstract. Physically based distributed hydrological models are widely used for hydrological simulations in various environments. As with conceptual models, they are limited in data-sparse basins by the lack of streamflow data for calibration. Short periods of observational data (less than 1 year) may be obtained from fragmentary historical records of previously existing gauging stations or from temporary gauging during field surveys, which might be of value for model calibration. However, unlike lumped conceptual models, such an approach has not been explored sufficiently for physically based distributed models. This study explored how the use of limited continuous daily streamflow data might support the application of a physically based distributed model in data-sparse basins. The influence of the length of the observation period on the calibration of the widely applied soil and water assessment tool model was evaluated in four Chinese basins with differing climatic and geophysical characteristics. The evaluations were conducted by comparing calibrations based on short periods of data with calibrations based on data from a 3-year period, which were treated as benchmark calibrations of the four basins, respectively. To ensure the differences in the model simulations solely come from differences in the calibration data, the generalized likelihood uncertainty analysis scheme was employed for the automatic calibration and uncertainty analysis. In the four basins, contrary to the common understanding of the need for observations over a period of several years, data records with lengths of less than 1 year were shown to calibrate the model effectively, i.e., performances similar to the benchmark calibrations were achieved. The models of the wet Jinjiang and Donghe basins could be effectively calibrated using a shorter data record (1 month), compared with the dry Heihe and upstream Yalongjiang basins (6 months). Even though the four basins are very different, when using 1-year or 6-month (covering a whole dry season or rainy season) data, the results show that data from wet seasons and wet years are generally more reliable than data from dry seasons and dry years, especially for the two dry basins. The results demonstrated that this idea could be a promising approach to the problem of calibration of physically based distributed hydrological models in data-sparse basins, and findings from the discussion in this study are valuable for assessing the effectiveness of short-period data for model calibration in real-world applications.

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.