Ethiopia - Palmer Drought Severity Index (PDSI)

Aridity index (AI) is an effective estimator of drought status, and spatiotemporally continuous long-term AI dataset is critical for drought assessment and applications.This dataset offers valuable support for research on dryland ecosystems, agriculture, and climate change, offering critical insights to address global environmental and sustainability challenges. Resolution ;0.05 × 0.05   in the resolution  ( referenced year 2022)

Rainfall anomaly refers to the difference between the observed rainfall and the long-term average for a specific location and time period. It indicates whether rainfall conditions are above or below normal. Positive anomalies represent higher-than-average rainfall, while negative anomalies indicate below-average rainfall. These anomalies are essential for monitoring climate variability, including droughts and floods, and for assessing their impacts on agriculture, water resources, and other sectors. For this analysis, CHIRPS daily rainfall data for Ethiopia were utilized, covering the month of March 2024, 2025 and 2025. Rainfall anomalies for month of March were calculated by subtracting the long-term average (2000–2020) from the observed rainfall values for the same period.

These datasets provide Ethiopia’s Vegetation Condition Index (VCI) values for the past five years, specifically covering the June to September growing season. The VCI is a satellite-derived indicator used to evaluate vegetation health and drought conditions. It distinguishes drought-related stress from other factors affecting vegetation growth. The VCI values range from 0 to 100, where 0% indicates the worst vegetation condition—approaching the historical minimum and signifying severe drought—and 100% indicates optimal conditions, near the historical maximum, reflecting healthy and lush vegetation. The VCI data is obtained from the MODIS satellite. The long-term dataset used for seasonal analysis spans from the year 2000 to the present, focusing on the months of June through September, aligning with Ethiopia’s main growing season. These time series datasets enable the comparison of current vegetation conditions with those of recent growing seasons, supporting drought monitoring and vegetation health assessments.

These datasets provide Ethiopia’s Temperature Condition Index (TCI) values for the past five years, focusing specifically on the growing season (June–September). The Temperature Condition Index is a remote sensing-based indicator used to evaluate vegetation stress resulting from temperature extremes, primarily heat stress. TCI is calculated using Land Surface Temperature (LST) data sourced from the MODIS platform. Higher TCI values reflect cooler and more favorable conditions for plant growth, while lower values are associated with increased heat stress and potential vegetation damage. For long-term seasonal analysis, MODIS LST data from 2000 to the present were utilized, with an emphasis on the June–September period. These time series datasets highlight TCI values from the recent five growing seasons, enabling direct comparison with current year conditions.

NDVI Anomaly is a geospatial metric used to measure how current vegetation conditions deviate from what is considered "normal" or average for a specific location and time period. It helps identify abnormal vegetation growth, whether better or worse than expected, and is especially valuable in agriculture, drought monitoring, food security, and environmental management. The dataset used for this analysis is the MOD13Q1 V6.1 product, which provides a Vegetation Index (VI) value on a per-pixel basis. For this study, we used MODIS data from July for the past three years, along with historical data spanning from 2010 to 2020.

These datasets provide Ethiopia’s Vegetation Health Index (VHI) values for the past five years, focusing specifically on the growing season from June to September. The Vegetation Health Index (VHI) is a satellite-derived indicator used to monitor the overall health and stress conditions of vegetation, particularly in relation to drought, heat, and moisture availability. It offers an integrated measure by combining data on vegetation greenness and thermal conditions. VHI is a vital remote sensing index for assessing vegetation condition, drought impacts, and agricultural stress. It combines two indices: the Vegetation Condition Index (VCI), which reflects vegetation greenness, biomass, and vigor; and the Temperature Condition Index (TCI), which indicates thermal conditions including heat and water stress. The long-term dataset analyzed for seasonal VHI spans from 2000 to the present, concentrating on the months of June, July, August, and September, corresponding to the Ethiopian growing season. These time series datasets highlight VHI values for the most recent five growing seasons, enabling direct comparison with current year conditions.