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| Titel |
Hydrological drought forecasting and skill assessment for the Limpopo River basin, southern Africa |
| VerfasserIn |
P. Trambauer, M. Werner, H. C. Winsemius, S. Maskey, E. Dutra, S. Uhlenbrook |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 4 ; Nr. 19, no. 4 (2015-04-13), S.1695-1711 |
| Datensatznummer |
250120678
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| Publikation (Nr.) |
 copernicus.org/hess-19-1695-2015.pdf |
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| Zusammenfassung |
| 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. |
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