Identifying drivers of streamflow extremes in West Africa to inform a nonstationary prediction model

West Africa exhibits decadal patterns in the behaviour of droughts and floods, creating challenges for effective water resources management. Proposed drivers prolonged shifts hydrological extremes include impacts land-cover change climate variability region. However, while future land-degradation or land-use are highly unpredictable, recent studies suggest that periods high-flows increasing flood occurrences could be predicted by monitoring sea-surface temperature (SST) anomalies different ocean basins. In this study, we thus examine: i) what basins would most suitable seamless flood-prediction systems; ii) how these affect high-flow (hereafter referred as extreme streamflow); iii) to integrate such nonstationary information risk modelling. We first use relative importance analysis identify main SST modulating conditions at both interannual timescales. At timescales, Pacific Niño (ENSO), tropical Indian Ocean (TIO) eastern Mediterranean (EMED) constitute climatic controls streamflow over Africa, North Atlantic, well variations TIO EMED Using regression analysis, then impact through latitudinal location strength Intertropical Convergence Zone (ITCZ) Walker circulation, impacting African Monsoon, especially zonal meridional atmospheric budget. Finally, a model, with capturing regional circulation patterns, reveals is best predictor extremes, particularly across Sahel. Predictability skill is, however, much higher timescale, Senegal than Niger catchment. This might due stronger (-cover) and/or catchment properties (e.g. Inner Delta) on River flow. Overall, framework floods can also applied drought assessment, contributing regulation plans hazard prevention, potentially other parts world.