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

Abstract

Ensemble hydrological predictions are normally obtained by forcing hydrological models with ensembles of atmospheric forecasts produced by numerical weather prediction models. To be of practical value to water users, such forecasts should not only be sufficiently skilful, they should also provide information that is relevant to the decisions end users make. The semi-arid Limpopo Basin in southern Africa has experienced severe droughts in the past, resulting in crop failure, economic losses and the need for humanitarian aid. In this paper we address the seasonal prediction of hydrological drought in the Limpopo River basin by testing three proposed forecasting systems (FS) that can provide operational guidance to reservoir operators and water managers at the seasonal timescale. All three FS include a distributed hydrological model of the basin, which is forced with either (i) a global atmospheric model forecast (ECMWF seasonal forecast system - S4), (ii) the commonly applied ensemble streamflow prediction approach (ESP) using resampled historical data, or (iii) a conditional ESP approach (ESPcond) that is conditional on the ENSO (El Niño-Southern Oscillation) signal. We determine the skill of the three systems in predicting streamflow and commonly used drought indices. We also assess the skill in predicting indicators that are meaningful to local end users in the basin. FS-S4 shows moderate skill for all lead times (3, 4, and 5 months) and aggregation periods. FS-ESP also performs better than climatology for the shorter lead times, but with lower skill than FS-S4. FS-ESPcond shows intermediate skill compared to the other two FS, though its skill is shown to be more robust. The skill of FS-ESP and FS-ESPcond is found to decrease rapidly with increasing lead time when compared to FS-S4. The results show that both FS-S4 and FS-ESPcond have good potential for seasonal hydrological drought forecasting in the Limpopo River basin, which is encouraging in the context of providing better operational guidance to water users.