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

Modelling spatial variability and uncertainty is a highly challenging subject in soiland 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 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 sub-catchment area of the Lajvér stream, in Southern Hungary for the high resolution modelling (i.e. 10 metre grid spacing) of the spatial distribution of soil organic matter (SOM). In the first step, secondary 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 honoured the input dataset. These realizations provide 100 simulated values for each grid node, which is an appropriate number for calculating the cumulative distributions for each grid node. Using these cumulative distributions the following maps were derived: the map of the E-type estimation, the corresponding 95% confidence interval width’s map and the map of the probability of the event of {SOM < 1.5%}. The latter map is highly informative in soil protection and management planning. The resulting model and 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.