This study compares sequential Gaussian simulation (sGs), and collocated cokriging simulation (CCS) algorithms with respect to their success in modeling prediction uncertainty, and their accuracy in making point predictions of water content (w) in the soil cores of a 10 ha area located in the Picardie region (Northern of France). The ability of sGs, and CCS in modeling uncertainty, and making point predictions was confronted with results achieved by ordinary kriging (OK), and collocated cokriging (CC) interpolation methods. A set of 81w samples was collected at the first 0.6 m of soil. A first set of 51 measurements achieved through stratified random sampling was used for simulations, and interpolations. Thus, the remainder set of 30 measurements was kept for the validation. Electrical resistivity (ER1) of the first depth (0,5 m) of investigation, which is linearly related to w and exhaustively sampled over the whole study area, was used as exhaustively sampled secondary information in the predictions, and the modeling of local, and spatial uncertainties of the target variable w using CCS and CC algorithms. In terms of the accuracy in making point predictions by simulation, and interpolation approaches, the results have shown that the approaches accounting for secondary exhaustive information (CCS and CC) are the more accurate. However, the difference between CCS and CC was not statistically significant stressing thus the convergence between a mean realization of a simulation algorithm, when the number of realizations is large enough, and the predicted map of an interpolation algorithm. As regards the modeling local uncertainty using accuracy plots, and goodness statistic (G), results have shown that CCS performed better the modeling prediction uncertainty than sGs that ignores the secondary exhaustive information in modeling uncertainty and an improvement of local certainty on w was observed, through small values of standard deviations of the whole realizations at validation sites, for CCS compared to sGs. Regarding the spatial uncertainty, results revealed that the assessment of spatial uncertainty using simulation algorithms (sGs or CCS) were more revealing and more realistic than spatial uncertainty assessment using interpolation algorithms (OK or CC). The standard deviations varied much less across the study area for CK and CC compared to standard deviations across the study area for sGs and CCS highlighting that for interpolation algorithms, the variance of the errors is independent of the actual data values, and depends only on the data configurations.