Sensitivity analysis of model output: variance-based methods make the difference
Proceedings of the 29th conference on Winter simulation
An effective screening design for sensitivity analysis of large models
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Short communication: Sensitivity analysis in fuzzy systems: Integration of SimLab and DANA
Environmental Modelling & Software
Sensitivity analysis for complex ecological models - A new approach
Environmental Modelling & Software
Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis
Environmental Modelling & Software
Environmental Modelling & Software
Component-based development and sensitivity analyses of an air pollutant dry deposition model
Environmental Modelling & Software
Partial order investigation of multiple indicator systems using variance-based sensitivity analysis
Environmental Modelling & Software
Simulation and sensitivity analysis of carbon storage and fluxes in the New Jersey Pinelands
Environmental Modelling & Software
Sobol' sensitivity analysis of a complex environmental model
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Review: Three complementary methods for sensitivity analysis of a water quality model
Environmental Modelling & Software
Sampling strategies in density-based sensitivity analysis
Environmental Modelling & Software
Relative yield decomposition: A method for understanding the behaviour of complex crop models
Environmental Modelling & Software
Global sensitivity analysis of yield output from the water productivity model
Environmental Modelling & Software
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Sensitivity analysis (SA) has become a basic tool for the understanding, application and development of models. However, in the past, little attention has been paid to the effects of the parameter sample size and parameter variation range on the parameter SA and its temporal properties. In this paper, the corn crop planted in 2008 in the Yingke Oasis of northwest China is simulated based on meteorological observation data for the inputs and statistical data for the parameters. Furthermore, using the extended Fourier Amplitude Sensitivity (EFAST) algorithm, SA is performed on the 47 crop parameters of the WOrld FOod STudies (WOFOST) crop growth models. A deep analysis is conducted, including the effects of the parameter sample size and variation range on the parameter SA, the temporal properties and the multivariable output issues of SA. The results show that sample size highly affects the convergence of the sensitivity indices. Two types of parameter variation ranges are used for the analysis, and the results show that the sensitive parameters of the two parameter spaces are distinctly different. In addition, taking the storage organ biomasses at the different growth stages as the objective output, the time-dependent characteristics of the parameter sensitivity are discussed. The results show that several sensitive parameters exist in the grain biomass throughout the entire development stage. In addition, analyzing the twelve sensitive parameters has proven that although certain parameters have no effect on the final yield, they play key roles in certain growth stages, and the importance of these parameters gradually increases. Finally, the sensitivity analyses of different state variable outputs are performed, including the biomass, yield, leaf area index, and transpiration coefficient. The results suggest that the sensitive parameters of various variable processes differ. This study highlights the importance of considering multiple characteristics of the model parameters and the responses of the models in specific phenological stages.