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Ensemble hydrological predictions are normally obtained by forcinghydrological models with ensembles of atmospheric forecasts produced bynumerical weather prediction models. To be of practical value to waterusers, such forecasts should not only be sufficiently skilful, they shouldalso provide information that is relevant to the decisions end users make.The semi-arid Limpopo Basin in southern Africa has experienced severedroughts in the past, resulting in crop failure, economic losses and theneed for humanitarian aid. In this paper we address the seasonal predictionof hydrological drought in the Limpopo River basin by testing three proposedforecasting systems (FS) that can provide operational guidance to reservoiroperators and water managers at the seasonal timescale. All three FSinclude a distributed hydrological model of the basin, which is forced witheither  (i) a global atmospheric model forecast (ECMWF seasonal forecastsystem – S4), (ii) the commonly applied ensemble streamflow predictionapproach (ESP) using resampled historical data, or (iii) a conditional ESPapproach (ESPcond) that is conditional on the ENSO (El Niño–Southern Oscillation) signal. We determine theskill of the three systems in predicting streamflow and commonly useddrought indices. We also assess the skill in predicting indicators that aremeaningful to local end users in the basin. FS_S4 showsmoderate skill for all lead times (3, 4, and 5 months) and aggregationperiods. FS_ESP also performs better than climatology for theshorter lead times, but with lower skill than FS_S4.FS_ESPcond shows intermediate skill compared to the other twoFS, though its skill is shown to be more robust. The skill ofFS_ESP and FS_ESPcond is found to decreaserapidly with increasing lead time when compared to FS_S4. Theresults show that both FS_S4 and FS_ESPcondhave good potential for seasonal hydrological drought forecasting in theLimpopo River basin, which is encouraging in the context of providing betteroperational guidance to water users

Hydrological drought forecasting and skill assessment for the Limpopo River basin, southern Africa