Assessing agricultural drought intensification and spatiotemporal pattern in the central Rift Valley using multi-source indices

Drought is a complex and multifaceted phenomenon that includes meteorological, agricultural, hydrological, and socio-economic components. Both single and composite drought indices were used in this study to assess agricultural drought in the East Shewa Zone of Central Ethiopia. The Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Soil Moisture Condition Index (SMCI) were used as single indices. To improve drought characterization, three composite indices—the Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI), and Optimized Vegetation Dryness Index (OVDI)—were created using empirical weighting and limited optimization techniques. The association of both single and composite indices with the multi-timescale Standardized Precipitation Index (SPI), a widely used meteorological drought measure, was evaluated using in-sample correlation analysis. The results showed that the composite indices (OVDI, VHI, and SDCI) exhibited stronger associations with the multi-timescale SPI than the individual indices, although the strength of these associations varied across seasons and SPI timescales. Among the single indices, the PCI showed the strongest in-sample correlation with the short-term SPI ( r = 0.764 with SPI-1). In contrast, the VCI and SMCI were more strongly correlated with longer-term SPI scales, with SMCI showing in-sample correlations of ( r = 0.577 with SPI-3) and ( r = 0.467 with SPI-6). Spatiotemporal analysis indicated an increase in drought severity, with the area classified as severely dry expanding from 1% to 11% between 2002 and 2015. The findings provide insights into the use of remote sensing indices for drought monitoring and may contribute to the development of improved drought monitoring and early warning approaches in the region.