Agricultural and meteorological drought variability assessment over the Rift Valley Lake Basin of Ethiopia

Understanding the spatiotemporal drought variability is essential for effective climate resilience and resource management in drought-prone areas. Therefore, this study aimed to assess the dynamics of meteorological and agricultural droughts in the Rift Valley Lake Basin (RVLB) of Ethiopia by using the Standardized Precipitation Index (SPI-1, SPI-4) derived from CHIRPS data (1985–2024) and remote sensing-based vegetation indices (NDVI, VCI, TCI, VHI) from MODIS data (2001–2024). The data were processed using Google Earth Engine and spatial statistical tools, including Spearman correlation, ordinary least squares (OLS), and geographically weighted regression (GWR), to examine the relationships between drought indices and root zone soil moisture. The metrics demonstrated that CHIRPS accurately captures spatial and temporal rainfall variability. The findings revealed significant temporal and spatial variability in drought severity, with major events occurring in 1985, 1987, 1990, 2002, 2009, 2016, and 2020. The study highlighted the compounded effects of thermal and moisture stress on vegetation. Strong negative correlations were identified between land surface temperature (LST) and vegetation indices, emphasizing the role of high temperatures in exacerbating drought stress. VHI identified a larger drought-affected area (severe: 10%, moderate: 30.26%) than VCI (severe: 1.42%, moderate: 15.37%). The GWR model outperformed OLS (Adjusted R2 = 0.95) with randomly distributed residuals (Moran’s I), capturing strong spatial relationships between TCI (3.19 in central and north; − 4.45 in south), SPI-1, and root zone soil moisture (~ 1.20). Predicted drought risk ranged from 0.05 to 5.78, peaking in the central basin where short-term rainfall deficits and high temperatures intensified severity. In conclusion, a multi-index, spatially explicit drought monitoring framework is crucial for early warning, targeted interventions, and adaptive agricultural planning. Integrating real-time remote sensing, climate-smart practices, and localized water management can strengthen resilience and support sustainable development under a changing climate.