Bad data quality and imperfect parameterizations are major sources of uncertainties in the land surface modeling. Gaussian Error Propagation (GEP) principle is used to study the propagation of key plant and soil parameters-burden random errors in the Common Land Model (CoLM), and to quantify the resultant uncertainties in the CoLM modeling. Results show that:  (1) Based on the uncertainties of soil and plant parameters specified in this study, the relative errors of surface soil temperature and moisture as well as plant evapotranspiration (canopy transpiration plus ground evaporation) simulated by the CoLM are 0.11%, 34.07%, and 5.58%, respectively. The highest uncertainties exist in simulations of sandy and sparsely vegetated areas. Compared to the random error of plant parameters, that of soil parameters affects the CoLM's simulation more remarkably; moreover, soil hydraulic parameters (porosity, saturated matrix potential, pore-size distribution index, and saturated hydraulic conductivity) contribute more (saturated soil albedo and volumetric heat capacity) to the modeling uncertainties than thermal parameters. As to all the simulated physical quantities in this study, the pore-size distribution index is always the most critical, which is probably associated with the function describing the relationship between matrix potential and volumetric water content. Porosity of sand and saturated hydraulic conductivity of clay is secondly important. The standard deviations of root distribution on the underlying surface of mixed forest and aerodynamic roughness length on the underlying surface of tundra make an appreciable contribution to evapotranspiration. The empirical parameters with higher relative errors are not necessarily greater contributors to the standard deviation of the predicted physical quantities.  (2) Under dry soil conditions (the surface soil liquid water saturation degree is below 0.1), the standard deviation of soil temperature is typically the highest. The soil moisture uncertainties are higher in the soil experiencing phase changes (the surface soil temperature is near 0 ℃ and the surface soil liquid water content is above 0). The stochastic error of evapotranspiration grows with increasing of the absolute value of flux itself, and is much more significant in relatively warm and dry environments (the surface soil temperature is above 280 K and the soil liquid water saturation degree is below 0.3).  The research verifies that GEP is able to identify the critical parameters and parameterizations of the CoLM, thus is significant for parameter determination, uncertainty analysis as well as model improvement.