Articles | Volume 16, issue 5
Hydrol. Earth Syst. Sci., 16, 1465–1480, 2012
Hydrol. Earth Syst. Sci., 16, 1465–1480, 2012

Research article 22 May 2012

Research article | 22 May 2012

Modelling irrigated maize with a combination of coupled-model simulation and uncertainty analysis, in the northwest of China

Y. Li1,2, W. Kinzelbach3, J. Zhou4, G. D. Cheng4, and X. Li4 Y. Li et al.
  • 1College of Earth and Environmental Science, Lanzhou University, Lanzhou, 730000, China
  • 2School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
  • 3Institute of Environmental Engineering, ETH Zurich, Wolfgang-Pauli-Strasse 15, 8093 Zurich, Switzerland
  • 4Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 730000, China

Abstract. The hydrologic model HYDRUS-1-D and the crop growth model WOFOST are coupled to efficiently manage water resources in agriculture and improve the prediction of crop production. The results of the coupled model are validated by experimental studies of irrigated-maize done in the middle reaches of northwest China's Heihe River, a semi-arid to arid region. Good agreement is achieved between the simulated evapotranspiration, soil moisture and crop production and their respective field measurements made under current maize irrigation and fertilization. Based on the calibrated model, the scenario analysis reveals that the most optimal amount of irrigation is 500–600 mm in this region. However, for regions without detailed observation, the results of the numerical simulation can be unreliable for irrigation decision making owing to the shortage of calibrated model boundary conditions and parameters. So, we develop a method of combining model ensemble simulations and uncertainty/sensitivity analysis to speculate the probability of crop production. In our studies, the uncertainty analysis is used to reveal the risk of facing a loss of crop production as irrigation decreases. The global sensitivity analysis is used to test the coupled model and further quantitatively analyse the impact of the uncertainty of coupled model parameters and environmental scenarios on crop production. This method can be used for estimation in regions with no or reduced data availability.