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Sobol' sensitivity analysis of a complex environmental model
Nossent, J.; Elsen, P.; Bauwens, W. (2011). Sobol' sensitivity analysis of a complex environmental model. Environ. Model. Softw. 26(12): 1515-1525. https://dx.doi.org/10.1016/j.envsoft.2011.08.010
In: Environmental Modelling & Software. Elsevier: Oxford. ISSN 1364-8152; e-ISSN 1873-6726
Peer reviewed article  
Available in  Authors 
    Waterbouwkundig Laboratorium: Non-open access 363626 [ request ]
Author keywords
    Sensitivity analysis; Process identification; Sobol’ method; Environmental modeling; Model parameters; Interaction effects
Authors  Top 
  • Nossent, J.
  • Elsen, P.
  • Bauwens, W.
Abstract
    Complex environmental models are controlled by a high number of parameters. Accurately estimating the values of all these parameters is almost impossible. Sensitivity analysis (SA) results enable the selection of the parameters to include in a calibration procedure, but can also assist in the identification of the model processes. Additionally, a sensitivity analysis can yield crucial information on the use and meaning of the model parameters.
    This paper presents a Sobol’ sensitivity analysis for flow simulations by a SWAT model of the river Kleine Nete, with the objective to assess the first order, second order and total sensitivity effects. Confidence intervals for the resulting sensitivity indices are inferred by applying bootstrapping. The results indicate that the curve number value (CN2) is the most important parameter of the model and that no more than 9 parameters (out of 26) are needed to have an adequate representation of the model variability. The convergence of the parameter ranking for total sensitivity effects is relatively fast, which is promising for factor fixing purposes. It is also shown that the Sobol’ sensitivity analysis enhances the understanding of the model, by e.g. pointing out 3 significant pairwise interactions.
    In general, it can be concluded that the Sobol’ sensitivity analysis can be successfully applied for factor fixing and factor prioritization with respect to the input parameters of a SWAT model, even with a limited number of model evaluations. The analysis also supports the identification of model processes, parameter values and parameter interaction effects.
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