Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

摘要

Vast parts of Africa rely on the rainy season for livestock and agriculture.Droughts can have a severe impact in these areas, which often have a very lowresilience and limited capabilities to mitigate drought impacts. This paperassesses the predictive capabilities of an integrated drought monitoring andseasonal forecasting system (up to 5 months lead time) based on theStandardized Precipitation Index (SPI). The system is constructed byextending near-real-time monthly precipitation fields (ECMWF ERA-Interimreanalysis and the Climate Anomaly Monitoring System–Outgoing LongwaveRadiation Precipitation Index, CAMS-OPI) with monthly forecasted fields asprovided by the ECMWF seasonal forecasting system. The forecasts were thenevaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger,and Upper Zambezi. There are significant differences in the quality of theprecipitation between the datasets depending on the catchments, and ageneral statement regarding the best product is difficult to make. Thegenerally low number of rain gauges and their decrease in the recent yearslimits the verification and monitoring of droughts in the different basins,reinforcing the need for a strong investment on climate monitoring. All thedatasets show similar spatial and temporal patterns in southern andnorth-western Africa, while there is a low correlation in the equatorialarea, which makes it difficult to define ground truth and choose an adequateproduct for monitoring. The seasonal forecasts have a higher reliability andskill in the Blue Nile, Limpopo and Upper Niger in comparison with theZambezi. This skill and reliability depend strongly on the SPI timescale,and longer timescales have more skill. The ECMWF seasonal forecasts havepredictive skill which is higher than using climatology for most regions. Inregions where no reliable near-real-time data is available, the seasonalforecast can be used for monitoring (first month of forecast). Furthermore,poor-quality precipitation monitoring products can reduce the potentialskill of SPI seasonal forecasts in 2 to 4 months lead time.