Africa - Standardized Precipitation Index (SPI)

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.

Standardized Precipitation Index (SPI) for Mozambiqueshowing rainfall anomalies relative to historical averages. Negative values indicate drought conditions, positive values indicate above-normal rainfall. Use this layer for drought early warning, food security analysis, and climate monitoring.

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.

The Soil Moisture Anomaly (SMA) dataset provides a measure of deviation in root-zone soil moisture conditions relative to the long-term climatological mean. Generated typically from hydrological models or satellite assimilation systems, this product highlights deficits or surpluses in soil water availability that are critical for agriculture and pastoralism. Because soil moisture integrates rainfall over time and accounts for evapotranspiration losses, SMA offers a more direct indicator of agricultural drought stress than precipitation deficits alone. This dataset is essential for monitoring the onset of agricultural drought, as soil moisture deficits often precede visible vegetation stress. It is widely used to assess potential yield reductions in rainfed agriculture and to monitor fodder availability in pastoral regions.

The Indicator for Meteorological Drought Tracking provides a clear spatio-temporal identification of persistent low-precipitation conditions at the global scale and at near real-time. It provides the outline and the duration and total spatial extent of drought clusters. The indicator is based on the Standardized Precipitation Index (SPI) for 3-months accumulation periods (i.e. SPI-3). The SPI indicator is derived from the ERA5 fifth generation reanalysis for the global climate and weather of the ECMWF, with baseline 1991-2020. Drought events are identified by means of a three dimensional density-based clustering algorithm (DBSCAN).

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.