calculation of local and spatial uncertainty of precipitation using geostatistical sgs and co-sgs simulation algorithms

among the usual interpolation methods, kriging and co-kriging are frequently used in the interpolation of precipitation data as one the best linear unbiased estimators, despite these advantages, there models show smoothness representation and because they are based on regional averages of the data, they predict maximum and minimum values   lower and higher than real values respectively. therefore, using these models alone is not sufficient in cases where the target is assessment of risk and study of variability. variability of phenomenon could be measured by uncertainty index. in the study in order to calculation of local and spatial uncertainty of precipitation, geostatistical simulation algorithms co-sgs and sgs were used. the main result of the study showed that, in simulation sample sgs and co-sgs algorithms would be able generate the max and min probable value making variance as close as to the main data. the difference simulation variance is very low with main samples, in contrast, the difference of variance between main samples and interpolation method is very high. the result also showed that the mentioned algorithms could be able to compute the local and spatial uncertainty of the precipitation by different simulation.