Drought monitoring is a fundamental component of drought risk management. It is normally performed using various drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables. In many instances, drought indices are used for monitoring purposes. Geostatistical methods allow the interpolation of spatially referenced data and the prediction of values for arbitrary points in the area of interest. In this research, several geostatistical methods,

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... ing ordinary kriging (OK), indicator kriging (IK), residual kriging (RK), probability kriging (Pk), simple kriging (SK), universal kriging (UK), and inverse distance weighted (IDW) methods were assessed for the derivation of maps of drought indices at 12 climatic stations in southern Iran. Data regarding monthly rainfall, temperature, wind, relative humidity, and sunshine of three periods (1985, 1995, and 2005) were taken from 12 meteorological synoptic stations and distributed areas. Based on the used error criteria, kriging methods were used for spatial analysis of the drought indexes and were selected as the best method. Results also showed that the lowest error (RMSE) is related to the kriging method. The results indicated that IK with tree frequency is more appropriate for the spatial analysis of the RDI index, and the Pk and SK methods are more appropriate for the spatial analysis of the SPI index. The kriging methods mean errors (RMSE) selected years for RDI and SPI index respectively are 0.85 and 0.84. In several cases, the "moderately dry" class received a more critical value by RDI. The results showed that by utilizing the ET0, the RDI can be very sensitive to climatic variability.