A global sensitivity analysis of DNDC model using a Bayesian based approach

Abstract

This study was aimed at demonstrate the application of the Bayesian based global sensitivity analysis (GSA) approach to denitrification and decomposition (DNDC) model using the tool of Gaussian emulation machine for sensitivity analysis (GEM-SA), in order to provide information on the relative effect of parameters on major model outputs. To execute the GSA study, twenty-eight input parameters were selected and eighty-six years’ DNDC simulation was run on basis of Three Hill’s spring wheat system. Three interested multi-year’s model outputs were chosen, whose sensitivity to inputs has been tested: yield, annual change in soil organic carbon (dSOC) and N2O flux. We found the effect of input parameters on three mentioned DNDC outputs not vary only with different simulated year but also with specific output variable. Moreover, the influence of inputs on variance of outputs varies with the form of sensitivity indices, i.e. main effects (individual contribution of each input to variance of model output) or total effects (when all inputs’ interactions are considered). Consequently, multi year’s SA is necessary for the nonlinear DNDC model and most sensitive parameters to specific output should be focused on further validation and calibration of that variable.