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

P Trambauer,S Maskey,H C Winsemius,M Werner,S Uhlenbrook,E Dutra

doi:10.5194/hess-19-1695-2015

Abstract

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.

Highlights

Climate change studies show evidence of an intensification of the global water cycle (Huntington, 2006; IPCC, 2007; Hansen et al, 2012; Trenberth, 2012; Coumou and Rahmstorf, 2012), with extreme events including floods and droughts expected to become more frequent
The UNISDR (United Nations Office for Disaster Risk Reduction) Hyogo Framework of Action 2005–2015 (UNISDR, 2005) describes early warning systems and action plans triggered on the issuing of a warning as one of the most effective strategies to mitigate the impacts of natural hazards
In these stations the performance of the hydrological model is found to be satisfactory based on the evaluation measures and ranges proposed by Moriasi et al (2007), which comprise the Nash–Sutcliffe efficiency (NSE), and the ratio of the root mean square error to the standard deviation of the measured data (RSR)

Summary

Introduction

Climate change studies show evidence of an intensification of the global water cycle (Huntington, 2006; IPCC, 2007; Hansen et al, 2012; Trenberth, 2012; Coumou and Rahmstorf, 2012), with extreme events including floods and droughts expected to become more frequent. Operational forecasting of streamflow to inform early warning is already commonplace in several parts of the world, but the main focus is often on flood prediction. Operational forecasting of streamflow for drought prediction has to date not been applied as widely, despite the widespread recognition of the relevance and importance of drought forecasting in the research community. P. Trambauer et al.: Hydrological drought forecasting and skill assessment than many flood early warning systems. Grasso (2009) reports that only three institutions provide information on the occurrence of major droughts at the global scale: FAO’s Global Information and Early Warning System on Food and Agriculture (GIEWS), the Humanitarian Early Warning Service (HEWS) operated by the World Food Programme (WFP), and the Benfield Hazard Research Centre at University College London

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Discussion

Conclusion