Stochastic simulation and uncertainty assessment of spatial variation in soil salinity in coastal reclamation regions

A stochastic simulation technique was introduced for mapping spatial variation in soil salinity in typical coastal reclaimed regions in north Jiangsu Province. Ordinary Kriging and sequential Gaussian simulation (SGS) were used to simulate spatial distributions of soil salinity. Then the stochastic simulation result was compared with that of Kriging. Uncertainty in the spatial distribution of soil salinity was also assessed using sequential indicator simulation (SIS). The results revealed that ordinary Kriging yielded a continuous and smooth spatial distribution of soil salinity. The quality of spatial variability from Kriging reduced with change in spatial structure. The SGS technique gave a relatively discrete and fluctuating spatial distribution of soil salinity. Based on SIS analysis, uncertainties in non-saline soils, light-saline soils, medium-saline soils and heavy-saline soils suggested that the probability of soil salinization decreased significantly after reclamation. In high probability regions of light-saline soils, agro-biological amelioration was urgently needed. In the high probability regions of medium-saline soils, field irrigation and drainage installations should be improved. Furthermore, soil borrowing seemed to efficiently harness high probability regions of heavy-saline soils.