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Modelling spatial variability and uncertainty is a highly challenging subject in soil- and geosciences. Regression kriging (RK) has several advantages; nevertheless it is not able to model the spatial uncertainty of the target variable. The main aim of this study is to present and test a sequential stochastic simulation approach based on regression kriging (SSSRK), which can be used to generate alternative and equally probable realizations in order to model and assess the spatial variability and uncertainty of the target variable, meanwhile the advantages of the RK technique are retained. The SSSRK method was tested in a catchment area, in Southern Hungary for the modelling of spatial variability and uncertainty of soil organic matter (SOM) content. In the first step, the auxiliary information was derived according to the soil forming factors; then the RK system was built up, which provides the base of SSSRK. 100 realizations were generated, which reproduced the model statistics and honored the input dataset. These realizations provide 100 simulated values for each grid node, which number is appropriate to calculate the cumulative distributions. Using these distributions the following maps were derived: map of expected values, the corresponding 95% confidence interval’s width, furthermore the probability of the event of {SOM < 1.5%}, since this threshold value is highly informative in soil protection and management planning. The resulted maps showed that, SSSRK is a valuable technique to model and assess the spatial variability and uncertainty of the target variable. Furthermore, the comparison of RK and SSSRK showed that, the SSSRK’s E-type estimation and the RK estimation gave almost the same results due to the fairly high R2 value of the regression model (R2=0.809), which decreased the smoothing effect

Testing a sequential stochastic simulation method based on regression kriging in a catchment area in Southern Hungary