One of the key parameters influencing sprinkler irrigation performance is water quality. In this study, the spatial variability of groundwater quality parameters (EC, SAR, Na⁺, Cl⁻, HCO₃ ⁻ and pH) was investigated by geostatistical methods and the most suitable areas for implementation of sprinkler irrigation systems in terms of water quality are determined. The study was performed in Fasa county of Fars province using 91 water samples. Results indicated that all parameters are moderately to strongly spatially correlated over the study area. The spatial distribution of pH and HCO₃ ⁻ was mapped using ordinary kriging. The probability of concentrations of EC, SAR, Na⁺ and Cl⁻ exceeding a threshold limit in groundwater was obtained using indicator kriging (IK). The experimental indicator semivariograms were often fitted well by a spherical model for SAR, EC, Na⁺ and Cl⁻. For HCO₃ ⁻ and pH, an exponential model was fitted to the experimental semivariograms. Probability maps showed that the risk of EC, SAR, Na⁺ and Cl⁻ exceeding the given critical threshold is higher in lower half of the study area. The most proper agricultural lands for sprinkler irrigation implementation were identified by evaluating all probability maps. The suitable areas for sprinkler irrigation design were determined to be 25,240 hectares, which is about 34 percent of total agricultural lands and are located in northern and eastern parts. Overall the results of this study showed that IK is an appropriate approach for risk assessment of groundwater pollution, which is useful for a proper groundwater resources management.

Soil organic carbon (SOC) affects many processes in soil. The main objective of this study was the prediction and uncertainty assessment of the spatial patterns of SOC through stochastic simulation using 2 simulation algorithms, sequential Gaussian simulation (sGs) and sequential indicator simulation (sis). The dataset consisted of 158 point measurements of surface SOC taken from an 18-ha field in Lower Austria. Conditional stochastic simulation algorithms were used to generate 100 maps of equiprobable spatial distribution for SOC. In general the simulated maps represented spatial distribution of SOC more realistically than the kriged map, i.e. overcoming the smoothing effect of kriging. Unlike sGs, sis was able to preserve the connectivity of extreme values in generated maps. The SOC simulated maps generated through sGs reproduced the sample statistics well. The reproduction of class-specific patterns of spatial continuity of SOC for the simulated model produced through sis was also reasonably good. The results highlight that when the class-specific patterns of spatial continuity of the attribute must be preserved, sis is preferred to sGs. For local uncertainty, standard deviations obtained using kriging varied much less across the study area than those obtained using simulations. This shows that the conditional standard deviations achieved through simulations depend on data values in addition to data configuration for greater reliability in reporting the estimation precision. Further, according to accuracy plots and goodness statistic, G, sis performs the modelling uncertainty better than sGs. The simulated models can provide useful information in risk assessment of SOC management in Lower Austria.