Abstract
Abstract. 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 low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.
Highlights
Most of Africa relies on the rainy season for water supply for livestock and agriculture (IWMI, 2010)
In this work we focus on the meteorological drought using the Standardized Precipitation Index (SPI) initially developed by McKee et al (1993) and recommended by the World Meteorological Organization as a standard to characterize meteorological droughts (Hayes et al, 2011; WMO press release in 2009)
Two freely available products that partially fulfil the requirements are the reanalysis produced by the dynamical European Centre for MediumRange Weather Forecasts (ECMWF) model ERA-Interim (Dee et al, 2011) and the observationally based product Climate Anomaly Monitoring System– Outgoing Longwave Radiation Precipitation Index (CAMSOPI; Janowiak and Xie, 1999)
Summary
Most of Africa relies on the rainy season for water supply for livestock and agriculture (IWMI, 2010). The SPI calculation relies only on monthly means of precipitation, which are usually available in near real time from observations (in situ and/or satellite) and from seasonal forecasts (in both cases generally associated with large uncertainties). Despite the recent model improvements, predicted fields such as temperature, and to a higher extent precipitation, can be biased and in some areas have little or no skill This is the case in some regions in Africa, where in situ observations are scarce and models often show persistent systematic errors. In this paper an integrated monitoring and forecasting drought system for four African river basins has been designed to explore the current capability of ECMWF products to provide drought information over the African continent This has been done by combining globally available monthly precipitation monitoring products with the forecast from S4.
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