Articles | Volume 20, issue 4
Hydrol. Earth Syst. Sci., 20, 1447–1457, 2016
https://doi.org/10.5194/hess-20-1447-2016

Special issue: Modeling hydrological processes and changes

Hydrol. Earth Syst. Sci., 20, 1447–1457, 2016
https://doi.org/10.5194/hess-20-1447-2016

Research article 18 Apr 2016

Research article | 18 Apr 2016

A hybrid model to simulate the annual runoff of the Kaidu River in northwest China

Jianhua Xu1, Yaning Chen2, Ling Bai1, and Yiwen Xu1 Jianhua Xu et al.
  • 1The Research Center for East-West Cooperation in China, School of Geographic Sciences, East China Normal University, Shanghai, 200241, China
  • 2State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China

Abstract. Fluctuant and complicated hydrological processes can result in the uncertainty of runoff forecasting. Thus, it is necessary to apply the multi-method integrated modeling approaches to simulate runoff. Integrating the ensemble empirical mode decomposition (EEMD), the back-propagation artificial neural network (BPANN) and the nonlinear regression equation, we put forward a hybrid model to simulate the annual runoff (AR) of the Kaidu River in northwest China. We also validate the simulated effects by using the coefficient of determination (R2) and the Akaike information criterion (AIC) based on the observed data from 1960 to 2012 at the Dashankou hydrological station. The average absolute and relative errors show the high simulation accuracy of the hybrid model. R2 and AIC both illustrate that the hybrid model has a much better performance than the single BPANN. The hybrid model and integrated approach elicited by this study can be applied to simulate the annual runoff of similar rivers in northwest China.