Articles | Volume 22, issue 9
https://doi.org/10.5194/hess-22-4667-2018
https://doi.org/10.5194/hess-22-4667-2018
Research article
 | 
06 Sep 2018
Research article |  | 06 Sep 2018

The potential of global reanalysis datasets in identifying flood events in Southern Africa

Gaby J. Gründemann, Micha Werner, and Ted I. E. Veldkamp

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Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-279,https://doi.org/10.5194/hess-2024-279, 2024
Preprint under review for HESS
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Cited articles

Aich, V., Koné, B., Hattermann, F. F., and Müller, E. N.: Floods in the Niger basin – analysis and attribution, Nat. Hazards Earth Syst. Sci. Discuss., 2, 5171–5212, https://doi.org/10.5194/nhessd-2-5171-2014, 2014a.
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Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013.
Arduini, G., Fink, G., Martinez de la Torre, A., Nikolopoulos, E., Anagnostou, E., Balsamo, G., and Boussetta, S.: End-user-focused improvements and descriptions of the advances introduced between the WRR tier1 and WRR tier2, Tech. rep., eartH2Observe, 2017.
Asante, K. O., Artan, G. a., Pervez, S., and Rowland, J.: A linear geospatial streamflow modeling system for data sparse environments, International Journal of River Basin Management, 6, 233–241, https://doi.org/10.1080/15715124.2008.9635351, 2008.
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Short summary
Flooding in vulnerable and data-sparse regions such as the Limpopo basin in Southern Africa is a key concern. Data available to local flood managers are often limited, inconsistent or asymmetrically distributed. We demonstrate that freely available global datasets are well suited to provide essential information. Despite the poor performance of simulated discharges, these datasets hold potential in identifying damaging flood events, particularly for higher-resolution datasets and larger basins.