The influence of antecedent conditions on flood risk in sub-Saharan Africa

Bischiniotis, Konstantinos; Hurk, Bart van den; Jongman, Brenden; Pérez, Erin Coughlan de; Veldkamp, Ted; Moel, Hans de; Aerts, Jeroen C. J. H.

doi:10.5194/nhess-18-271-2018

Cited by 24 publications

(18 citation statements)

References 59 publications

(62 reference statements)

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“…SPEI accounts for this in a way that rainfall alone does not. The SPEI index is increasingly used to monitor floods as well, especially when calculated at shorter timescales, such as 1 to 3 months [43], [44]. Floods are particularly difficult to forecast and show poor correlation with simple rainfall-based measures [45].…”

Section: Climate Datamentioning

confidence: 99%

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Monsoon weather and early childhood health in India

Dimitrova

Bora

2020

PLoS ONE

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BackgroundIndia is expected to experience an increase in the frequency and intensity of extreme weather events in the coming decades, which poses serious risks to human health and wellbeing in the country. ObjectiveThis paper aims to shed light on the possible detrimental effects of monsoon weather shocks on childhood undernutrition in India using the Demographic and Health Survey 2015-16, in combination with geo-referenced climate data. MethodsUndernutrition is captured through measures of height-for-age, weight-for-height, stunting and wasting among children aged 0-59 months. The standardised precipitation and evapotranspiration index (SPEI) is used to measure climatic conditions during critical periods of child development. OPEN ACCESSCitation: Dimitrova A, Bora JK (2020) Monsoon weather and early childhood health in India. PLoS ONE 15(4): e0231479. https://doi.org/10.

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Section: Climate Datamentioning

confidence: 99%

“…It is important to note that high SPEI values cannot directly be interpreted as floods, as there are other factors contributing to flood developments besides high precipitation, such as soil saturation and river discharge [43], [45]. Nonetheless, heavy rainfall is the single most important factor contributing to flood build-ups.…”

Section: Plos Onementioning

confidence: 99%

Monsoon weather and early childhood health in India

Dimitrova

Bora

2020

PLoS ONE

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“…While the country of South Africa is relatively rich in terms of data, technology and knowledge, many of its neighbouring countries are not (Spaliviero et al, 2011). This lack of spatially consistent datasets is a particular issue in this region, as many of the larger river basins are transboundary, and extreme events are often linked to phenomena on a wider, regional scale, such as cyclones (Biswas, 1999;Patt and Schro, 2008).…”

Section: Introductionmentioning

confidence: 99%

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

Gründemann

Werner

Veldkamp

2018

Hydrol. Earth Syst. Sci.

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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.

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“…It is important to note, though, that likely not all smallscale flood events that occurred between 1980 and 2012 will have been included in the chronology of reported flood events that was developed. As has more often been found to be the case in the Global South (Brakenridge, 2017), the availability of disaster data in the Limpopo River basin is fairly limited. In order to construct a basin-wide timeline of historic damaging floods, events reported in the EM-DAT, GAALFE and NatCatSERVICE databases were collated.…”

Section: Discussionmentioning

confidence: 99%