Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods
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 mai...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-03-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/21/1611/2017/hess-21-1611-2017.pdf |
| Summary: | 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.
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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).
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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.
<br><br>
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.
<br><br>
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. |
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| ISSN: | 1027-5606 1607-7938 |