Volume 6 Issue 1
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Xiao-meng SONG, Fan-zhe KONG, Che-sheng ZHAN, Ji-wei HAN, Xin-hua ZHANG. 2013: Parameter identification and global sensitivity analysis of Xinanjiang model using meta-modeling approach. Water Science and Engineering, 6(1): 1-17. doi: 10.3882/j.issn.1674-2370.2013.01.001
Citation: Xiao-meng SONG, Fan-zhe KONG, Che-sheng ZHAN, Ji-wei HAN, Xin-hua ZHANG. 2013: Parameter identification and global sensitivity analysis of Xinanjiang model using meta-modeling approach. Water Science and Engineering, 6(1): 1-17. doi: 10.3882/j.issn.1674-2370.2013.01.001
shu

Parameter identification and global sensitivity analysis of Xinanjiang model using meta-modeling approach

doi: 10.3882/j.issn.1674-2370.2013.01.001
  • 1.

    Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing 210029, P. R. China

  • 2.

    School of Resource and Earth Science, China University of Mining and Technology, Xuzhou 221116, P. R. China

  • 3.

    Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences  and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, P. R. China

  • 4.

    State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, P. R. China

Funds:  This work was supported by the National Natural Science Foundation of China (Grant No. 41271003) and the National Basic Research Program of China (Grants No. 2010CB428403 and 2010CB951103).
More Information
  • Corresponding author: Xiao-meng SONG
  • Received Date: 2011-10-11
  • Rev Recd Date: 2012-02-06
    Abstract
  • Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1) a screening method (Morris) for qualitative ranking of parameters, and (2) a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol). First, the Morris screening method was used to qualitatively identify the parameters’ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM) were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.

     

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