The uncertainty of the input parameters in land surface model can introduce simulation deviation. To improve the capability of the models and reduce the parameter uncertainties, usually the parameter optimization process is necessary. In this study, using the surface layer data observed in Wenjiang station and the particle swarm optimization (PSO) algorithm to optimize soil and vegetation parameters that difficult to obtain by observations in the SHAW (Simultaneous Heat and Water) model. On this basis, the SHAW model was run with the optimized and default parameters. Then the simulations were compared with the corresponding observations to investigate the effect of optimization parameters in land surface process simulation. The following conclusions were drawn:Using the optimized parameters calibrated by PSO algorithm can improve the simulation of the soil moisture and latent heat flux. The biases between simulated soil moisture and latent heat flux with the corresponding observations are decreased, but the net radiation, soil temperature and sensible heat flux simulation are not improved. This study suggests that PSO algorithm can be used for land surface model parameter optimization, but the simulation of all variable cannot be simultaneously improved only by optimization process.
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