Articles | Volume 21, issue 3
Hydrol. Earth Syst. Sci., 21, 1611–1629, 2017

Special issue: Sub-seasonal to seasonal hydrological forecasting

Hydrol. Earth Syst. Sci., 21, 1611–1629, 2017

Research article 17 Mar 2017

Research article | 17 Mar 2017

Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods

Mathias Seibert1, Bruno Merz1,2, and Heiko Apel1 Mathias Seibert et al.
  • 1GFZ – German Centre for Geosciences, Section 5.4: Hydrology, Potsdam, Germany
  • 2University of Potsdam, Institute of Earth and Environmental Science, Potsdam, Germany

Abstract. The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of people in South Africa, Botswana, Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed using statistical approaches. Three methods (multiple linear models, artificial neural networks, random forest regression trees) are compared in terms of their ability to forecast streamflow with up to 12 months of lead time. The following four main findings result from the study.

1. There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high inter-station differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated).

2. A large range of potential predictors is considered in this study, comprising well-established climate indices, customised teleconnection indices derived from sea surface temperatures and antecedent streamflow as a proxy of catchment conditions. El Niño and customised indices, representing sea surface temperature in the Atlantic and Indian oceans, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42 % explained variance), whereas teleconnections exert a stronger influence in large catchments.

3. Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and random forest regression trees, despite their capabilities to represent nonlinear relationships.

4. Employed in early warning, the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics (ROCs). Seasonal statistical forecasts in the Limpopo show promising results, and thus it is recommended to employ them as complementary to existing forecasts in order to strengthen preparedness for droughts.

Short summary
Seasonal early warning is vital for drought management in arid regions like the Limpopo Basin in southern Africa. This study shows that skilled seasonal forecasts can be achieved with statistical methods built upon driving factors for drought occurrence. These are the hydrological factors for current streamflow and meteorological drivers represented by anomalies in sea surface temperatures of the surrounding oceans, which combine to form unique combinations in the drought forecast models.