Assessment of Severity of Droughts Using Geostatistics Method(Case Study: Southern Iran)

Abstract

Drought monitoring is a fundamental component of drought risk management. It is normally performed using<br />various drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables.<br />In many instances, drought indices are used for monitoring purposes. Geostatistical methods allow the interpolation<br />of spatially referenced data and the prediction of values for arbitrary points in the area of interest. In this research,<br />several geostatistical methods, including ordinary kriging (OK), indicator kriging (IK), residual kriging (RK),<br />probability kriging (Pk), simple kriging (SK), universal kriging (UK), and inverse distance weighted (IDW) methods<br />were assessed for the derivation of maps of drought indices at 12 climatic stations in southern Iran. Data regarding<br />monthly rainfall, temperature, wind, relative humidity, and sunshine of three periods (1985, 1995, and 2005) were<br />taken from 12 meteorological synoptic stations and distributed areas. Based on the used error criteria, kriging<br />methods were used for spatial analysis of the drought indexes and were selected as the best method. Results also<br />showed that the lowest error (RMSE) is related to the kriging method. The results indicated that IK with tree<br />frequency is more appropriate for the spatial analysis of the RDI index, and the Pk and SK methods are more<br />appropriate for the spatial analysis of the SPI index. The kriging methods mean errors (RMSE) selected years for RDI<br />and SPI index respectively are 0.85 and 0.84. In several cases, the “moderately dry" class received a more critical<br />value by RDI. The results showed that by utilizing the ET0, the RDI can be very sensitive to climatic variability.