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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 8
Hydrol. Earth Syst. Sci., 17, 3279–3293, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Statistical methods for hydrological applications

Hydrol. Earth Syst. Sci., 17, 3279–3293, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 21 Aug 2013

Research article | 21 Aug 2013

Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis

J. Li1, Q. Y. Duan1, W. Gong1, A. Ye1, Y. Dai1, C. Miao1, Z. Di1, C. Tong2, and Y. Sun2 J. Li et al.
  • 1College of Global Change and Earth System Science (GCESS), Beijing Normal University, Beijing, 100875, China
  • 2Lawrence Livermore National Laboratory, Livermore, CA 94551, USA

Abstract. Proper specification of model parameters is critical to the performance of land surface models (LSMs). Due to high dimensionality and parameter interaction, estimating parameters of an LSM is a challenging task. Sensitivity analysis (SA) is a tool that can screen out the most influential parameters on model outputs. In this study, we conducted parameter screening for six output fluxes for the Common Land Model: sensible heat, latent heat, upward longwave radiation, net radiation, soil temperature and soil moisture. A total of 40 adjustable parameters were considered. Five qualitative SA methods, including local, sum-of-trees, multivariate adaptive regression splines, delta test and Morris methods, were compared. The proper sampling design and sufficient sample size necessary to effectively screen out the sensitive parameters were examined. We found that there are 2–8 sensitive parameters, depending on the output type, and about 400 samples are adequate to reliably identify the most sensitive parameters. We also employed a revised Sobol' sensitivity method to quantify the importance of all parameters. The total effects of the parameters were used to assess the contribution of each parameter to the total variances of the model outputs. The results confirmed that global SA methods can generally identify the most sensitive parameters effectively, while local SA methods result in type I errors (i.e., sensitive parameters labeled as insensitive) or type II errors (i.e., insensitive parameters labeled as sensitive). Finally, we evaluated and confirmed the screening results for their consistency with the physical interpretation of the model parameters.

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