Ethiopia: Rainfall Anomaly

Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. It is a gridded rainfall time series for trend analysis and seasonal drought monitoring, spans 50°S-50°N (and all longitudes) and ranges from 1981 to near-present. The anomaly refers to the difference between current rainfall and the average rainfall that occurred between 1981 and 2010 in millimeters. For more information visit the CHIRPS site. This dataset contains the latest available CHIRPS anomaly data. The full list of data available is available from USGS for Mar-May data, Oct-Dec data, and others. Additionally, subnational statistics (mean, min, max) have been calculated for Ethiopia, Kenya, and Somalia and are available in the csv resource.

Seasonal rainfall accumulation anomaly for East Africa derived from CHIRPS data, showing deviation from the 1981-2010 historical average in millimetres. Use this layer for drought early warning, seasonal forecast validation, agricultural planning, and humanitarian response in the Horn of Africa.

This dataset provides Palmer Drought Severity Index (PDSI) values for Ethiopia from 2020 to 2024, offering insights into drought conditions across the country. The PDSI is a widely used drought index that measures deviations in soil moisture conditions from the long-term average, helping to identify both drought and wet conditions. Unlike traditional PDSI calculations that rely on the Thornthwaite method for estimating potential evapotranspiration (ET), this dataset utilizes a more physically based Penman-Monteith reference ET, which accounts for: * Solar radiation * Temperature * Humidity * Windspeed ## Interpretation of PDSI Values: * PDSI between -0.5 and 0.5 → Near-normal soil moisture conditions * PDSI 4 → Very wet 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.

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)

Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a quasi-global rainfall dataset spanning 50S to 50N from 1981 to near present. It blends 0.05 degree satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.