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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-164
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 09 Apr 2018

Research article | 09 Apr 2018

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

The potential of global re-analysis datasets in identifying flood events in Southern Africa

Gaby J. Gründemann1, Micha Werner1,2, and Ted I. E. Veldkamp3 Gaby J. Gründemann et al.
  • 1IHE Delft, 2601 DA, Delft, the Netherlands
  • 2Deltares, 2600 MH, Delft, the Netherlands
  • 3Institute for Environmental Studies, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, the Netherlands

Abstract. Sufficient and accurate hydro-meteorological data are essential to manage water resources. Recently developed global re-analysis datasets have significant potential in providing these data, especially in regions such as Southern Africa that are both vulnerable and data poor. These global re-analysis datasets have, however, not yet been exhaustively validated and it is thus unclear to what extent these are able to adequately capture the climatic variability of water resources, in particular for extreme events such as floods. This article critically assesses the potential of a recently developed global Water Resource Re-analysis (WRR) dataset developed in the EU FP7 eartH2Observe project for identifying floods, focussing on the occurrence of floods in the Limpopo River basin in Southern Africa. The discharge outputs of seven global models and ensemble mean of those models as available in the WRR dataset are analysed and compared against two benchmarks of flood events in the Limpopo River basin. The first benchmark is based on observations from the available stations, while the second is developed based on flood events that have led to damages as reported in global databases of damaging flood events. Results show that while the WRR dataset provides useful data for detecting the occurrence of flood events in the Limpopo River basin, variation exists amongst the global models regarding their capability to identify the magnitude of those events. The study also reveals that the models are better able to capture flood events at stations with a large upstream catchment area. Improved performance for most models is found for the 0.25 degrees resolution global model, when compared to the lower resolution 0.5 degrees models, thus underlining the added value of increased resolution global models. The skill of the global hydrological models in identifying the severity of flood events in poorly gauged basins such as the Limpopo can be used to estimate the impacts of those events using the benchmark of reported damaging flood events developed at the basin level, though could be improved if further detail on location and impacts are included in disaster databases. Large-scale models such as those included in the WRR dataset are used by both global and continental forecasting systems, and this study sheds light on the potential these have in providing information useful for local scale flood risk management. In conclusion, this study offers valuable insights in the applicability of global re-analysis data for identifying impacting flood events in data sparse regions.

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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.
Flooding in vulnerable and data-sparse regions such as the Limpopo Basin in Southern Africa is a...
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