Abstract
Abstract. Sufficient and accurate hydro-meteorological data are essential to manage water resources. Recently developed global reanalysis datasets have significant potential in providing these data, especially in regions such as Southern Africa that are both vulnerable and data poor. These global reanalysis 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 Resources Reanalysis (WRR) dataset developed in the European Union's Seventh Framework Programme (EU-FP7) eartH2Observe (E2O) 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° resolution global model, when compared to the lower-resolution 0.5° models, thus underlining the added value of increased-resolution global models. The skill of the global hydrological models (GHMs) 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 this could be improved if further details 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 reanalysis data for identifying impacting flood events in data-sparse regions.
Highlights
Floods are among the most common and destructive natural hazards globally (Jongman et al, 2015)
The relationship between the upstream catchment area of the river gauging stations in the Limpopo River basin and the error statistics for the models in WRR1 and WRR2 is illustrated in Fig. 2 and Table 4
This can be best observed by looking at the Nash–Sutcliffe efficiency (NSE) statistic, from which it is evident that the models are generally able to capture the hydrology for stations with an upstream catchment area that is larger than 2500 km2 for WRR1 (Fig. 2a) and larger than 520 km2 for WRR2 (Fig. 2d)
Summary
Floods are among the most common and destructive natural hazards globally (Jongman et al, 2015). It is generally acknowledged that, due to projected climate and socio-economic changes, extreme events such as floods may further increase in frequency, magnitude and intensity (IPCC, 2012, 2014; UNISDR, 2015, 2016). In order to minimize the negative effects of floods, disaster risk reduction is increasingly important (Trigg et al, 2016). The urgency of mitigating flood risks is recognized by international agreements, such as the Sendai Framework for Disaster Risk Reduction (UNISDR, 2015), which underlines the understanding of disaster risk including the hazard characteristics as a first priority. Developing adequate knowledge of past flood events is essential in order to sufficiently address this global problem (Dottori et al, 2016; Spaliviero et al, 2011) and to further reduce the consequences of future disastrous events
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