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Large-Scale Climatic Drivers of Flood Frequency across Sub-Saharan Africa

Authors :
Job Ekolu
Bastien Dieppois
Jonathan Eden
Yves Tramblay
Gabriele Villarini
Simon Moulds
Louise Slater
Gil Mahé
Jean-Emmanuel Paturel
Moussa Sidibe
Pierre Camberlin
Benjamin Pohl
Marco van de Wiel
Publication Year :
2023
Publisher :
Copernicus GmbH, 2023.

Abstract

Sub-Saharan Africa is affected by a high level of temporal and spatial climate variability, with large impacts on water resources, human lives, and economies, notably through hydrological extremes such as floods. Nevertheless, the key climatic factors driving interannual variability in flood frequency remain poorly documented and understood. To address this research gap, we first compile information on large-scale climate drivers that may potential affect sub-Saharan African hydroclimate (e.g., El Niño–Southern Oscillation, Atlantic Multidecadal Variability). Then, using a new 65-year long daily streamflow dataset of over 600 stations in sub-Saharan Africa, a bootstrapped stepwise regression and relative importance analysis is applied to quantify the relative contribution of different ocean basins to interannual variability in flood frequency between 1950 and 2014. Results show that interannual variations in the frequency of flood events are significantly linked to different modes of climate variability in the Pacific, Indian, and Atlantic Oceans. These modes of climate variability together explain around 60% of observed interannual variation in seasonal flood frequency. The relative influence of each ocean basin, however, differs from one region to another. The Indian and Pacific Oceans, for instance, have significant influences on interannual variations in the frequency of floods between December and May across much of southern and eastern Africa. In western Africa, the Mediterranean and Atlantic Oceans appear to have a dominant influence between September and November. In central Africa, the relative influence of different oceans basins is seasonally variable. Using the best combination of Sea-Surface Temperature predictors, we then examine projected future trends using a large ensemble of climate models from the CMIP6 experiments.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........900b1b3ba199bac3f40ed130a5a7f5d0