Abstract
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.
Highlights
Drought is a slowly progressing phenomenon which is challenging to detect ahead
This study presents the predictability of hydrological droughts in the Limpopo Basin, transferring methodologies predominantly used in meteorology to hydrology
The results show that hydrological drought in the Limpopo can be predicted based on climate indices, sea surface temperatures (SSTs) teleconnections and antecedent streamflow, the predictability varies between catchments and lead times
Summary
Drought is a slowly progressing phenomenon which is challenging to detect ahead. As a result, drought management frequently remains crisis management, which is limited to fighting drought when impacts have already started to unfold. M. Seibert et al.: Statistical seasonal forecasting of hydrological drought in the Limpopo Basin and for end users to include uncertainty information in the decision-making process. Seibert et al.: Statistical seasonal forecasting of hydrological drought in the Limpopo Basin and for end users to include uncertainty information in the decision-making process This can be achieved, for example, by providing probabilistic drought forecast information. Random forest regression trees have only rarely been applied for seasonal drought forecasting (Chen et al, 2012) These datadriven approaches are useful for seasonal forecasting in regions where hydrological observations are available, but additional data characterising the catchments are limited. We present the forecasting skill for hydrological drought during the main rainy season runoff from December to May achieved with the three selected statistical models
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