Risk associated with drought requires information from drought monitoring system as the monitoring system provide onset, progress, severity and spatial extent of drought. Such information, when available, assists in drought contingency plans. Drought monitoring is normally performed using drought indices that are continuous function of precipitation and other meteorological variables (Younes et al., 2011). Drought is considered as one of the most complex and least understood of all natural hazard. Due to large scale spatiotemporal variability in timing and duration of drought impact, it is hard to find the definition of drought that fits for all circumstances because drought is not an absolute physical phenomenon that is only characterized …show more content…
Spatial interpolation techniques are widely used for rainfall e.g. (di Piazza et al., 2011), Potential evapotranspiration (PET) e.g. (Tait and Woods, 2007), geophysical data (Mariani and Basu, 2015), temperature e.g. (Jarvis and Stuart, 2001), Ozone e.g. (Hooyberghs et al., 2006; Tranchant and Vincent, 2000), drought indices, PDSI (Rhee et al., 2008), SPI(Rhee et al., 2008; Stagge et al., 2015; Vicente-Serrano et al., 2003) and SPEI (Stagge et al., 2015). Since SPEI is sensitive to both precipitation and the atmospheric evaporative demand, which can be computed for different time scales, and results improvements over SPI, this study utilizes the spatiotemporal distribution of …show more content…
Technique used in interpolation of ground-based measurement of climatic variables can be broadly classified into two main groups: deterministic and geostatistical. The most frequently used deterministic methods in spatial interpolation are the Thiessen polygon, one of the oldest and often used (Thiessen, 1911) and Inverse Distance Weighting (IDW). The geostatistical method constitutes a discipline involving mathematics and earth sciences. Kriging is most often used geostatistical method in interpolation. The term ‘kriging’ and the formalism of this method was done by Matheron (1971). Many researchers have found kriging, spline, and IDW as best instruments in spatial interpolation of climatic data (e.g.(Plouffe et al., 2015; Rhee et al., 2008; Younes et al.,