Africa - Monthly and dekadal Soil Moisture Anomaly (SMA)

The Combined Drought Indicator (CDI) dataset developed at ACMAD is designed as an operational drought-monitoring product that synthesizes multiple biophysical signals into a single, coherent assessment of drought conditions across Africa. Rather than relying on a single indicator, the ACMAD CDI combines information from rainfall-based drought indices and satellite-derived vegetation condition metrics, applying rule-based logic to identify areas where these independent indicators consistently point to drought stress. This convergence approach reduces the risk of false alarms that can arise from short-term rainfall variability or vegetation anomalies unrelated to drought. The data exist in two forms one based on GPCC while another based on Era5 precipitation. The dataset is particularly useful in the African context, where ground observations are sparse and climatic gradients are strong across regions. By using harmonized, gridded datasets, the CDI provides a comparable drought signal across national boundaries, supporting regional coordination and continental-scale analysis. Its categorical outputs are intentionally simple and policy-ready, making them suitable for drought bulletins, early-warning communications, and high-level decision support. Importantly, the CDI is not intended to replace detailed drought diagnostics or impact assessments. Instead, it serves as a convergence and confirmation tool that highlights areas requiring closer analysis and expert interpretation. In this role, the CDI acts as a bridge between complex climate data and practical drought-risk management, enabling timely, consistent, and defensible drought monitoring at regional and continental scales.

This version of the Combined Drought Indicator (CDI) utilizes ERA5 reanalysis data as the primary input for the precipitation component (SPI), rather than gauge-only or other satellite-blended products. ERA5, produced by ECMWF, offers comprehensive spatial coverage and temporal consistency, making it valuable for regions where gauge networks are failing or sparse. Like the standard CDI, this dataset synthesizes rainfall deficits (via ERA5), soil moisture anomalies, and vegetation stress into a single classification system to monitor the evolution of drought episodes.

The Standardized Precipitation Index (SPI) is the globally recommended standard for characterizing meteorological drought. It quantifies observed precipitation as a standardized deviation from the long-term probability distribution function for a specific location and time scale. This dataset typically includes multiple time-scales (e.g., 1-month, 3-month, 6-month, 12-month) to capture different types of drought impacts, from immediate soil moisture deficits to long-term hydrological stress. Because SPI is normalized, it allows for safe comparisons between different climatic zones—allowing a wet region and a dry region to be compared on the same scale of drought severity.

The fAPAR Anomaly dataset maps deviations in vegetation health and photosynthetic activity across Africa. fAPAR measures the proportion of solar radiation absorbed by plants for photosynthesis, making it a robust biophysical indicator of vegetation status. The anomaly product highlights areas where vegetation activity is significantly below or above the long-term average, serving as a primary proxy for agricultural production and biomass availability. Unlike simple greenness indices (like NDVI), fAPAR is physically related to the energy and mass exchange processes of the canopy. Negative anomalies in this dataset are strong indicators of vegetation stress caused by drought, pests, or disease, making it a critical input for yield forecasting and food security analysis.

This dataset contains the absolute values of the Fraction of Absorbed Photosynthetically Active Radiation (fAPAR). While anomalies show deviations from the norm, these absolute values provide a direct measure of the current state of vegetation density and health. It represents the fraction of incoming solar radiation in the photosynthetically active spectrum that is absorbed by the canopy, ranging theoretically from 0 (bare soil) to 1 (dense, healthy vegetation). Absolute fAPAR is crucial for initializing crop growth models, estimating net primary productivity (NPP), and monitoring the phenological development of crops and natural vegetation throughout the growing season.

The Global Precipitation Climatology Centre (GPCC) Monthly Precipitation dataset provides gridded gauge-analysis products focusing on land-surface precipitation. This dataset serves as a high-quality observational baseline for climate monitoring across Africa. It is constructed from quality-controlled data from thousands of rain gauges worldwide. At ACMAD, this data often serves as the "truth" or reference layer for calibrating satellite estimates and for calculating long-term indices like SPI. It is critical for retrospective analysis, model verification, and understanding the climatological context of current weather events.