Geographically Weighted Regression Hybridized with Kriging Model for Delineation of Drought-Prone Areas

Assessing spatial variability of drought-prone areas is important for disaster preparedness and impact management. This study applied state-of-the-art geographically weighted regression hybridized with kriging method (GWRKrig) to map the spatial variability of drought-prone areas in the northwest of Iran based on the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Rainfall and evapotranspiration data from 47 synoptic stations over 20 years were used to generate SPI and SPEI, and topographical (altitude) data were used for GWRKrig. The results obtained using GWRKrig were compared with that of standalone GWR, regression kriging (RegKrig), and ordinary kriging (Krig) methods. The GWRKrig method emerged as a promising tool for spatial interpolation of drought indices based on performance evaluation measures, namely, the root mean squared error (RMSE) and coefficient of determination (R2). The SPEI-based drought intensity interpolated via GWRKrig revealed relatively precise spatial variability of drought zones. The method proposed in this study would assist in the accurate delineation of drought-prone areas, which is the foremost venture in the planning hierarchy of drought management schemes and their implementation.