In the West African Sahel, two paradoxical hydrological behaviors have occurred during the last five decades. The first paradox was observed during the 1968–1990s ‘Great Drought’ period, during which runoff significantly increased. The second paradox appeared during the subsequent period of rainfall recovery (i.e., since the 1990s), during which the runoff coefficient continued to increase despite the general re-greening of the Sahel. This paper reviews and synthesizes the literature on the drivers of these paradoxical behaviors, focusing on recent works in the West African Sahelo/Sudanian strip, and upscaling the hydrological processes through an analysis of recent data from two representative areas of this region. This paper helps better determine the respective roles played by Land Use/Land Cover Changes (LULCC), the evolution of rainfall intensity and the occurrence of extreme rainfall events in these hydrological paradoxes. Both the literature review and recent data converge in indicating that the first Sahelian hydrological paradox was mostly driven by LULCC, while the second paradox has been caused by both LULCC and climate evolution, mainly the recent increase in rainfall intensity.

Urbanization resulting from sharply increasing demographic pressure and infrastructure development has made the populations of many tropical areas more vulnerable to extreme rainfall hazards. Characterizing extreme rainfall distribution in a coherent way in space and time is thus becoming an overarching need that requires using appropriate models of intensity–duration–frequency (IDF) curves. Using a 14 series of 5 min rainfall records collected in Senegal, a comparison of two generalized extreme value (GEV) and scaling models is carried out, resulting in the selection of the more parsimonious one (four parameters), as the recommended model for use. A bootstrap approach is proposed to compute the uncertainty associated with the estimation of these four parameters and of the related rainfall return levels for durations ranging from 1 to 24 h. This study confirms previous works showing that simple scaling holds for characterizing the temporal scaling of extreme rainfall in tropical regions such as sub-Saharan Africa. It further provides confidence intervals for the parameter estimates and shows that the uncertainty linked to the estimation of the GEV parameters is 3 to 4 times larger than the uncertainty linked to the inference of the scaling parameter. From this model, maps of IDF parameters over Senegal are produced, providing a spatial vision of their organization over the country, with a north to south gradient for the location and scale parameters of the GEV. An influence of the distance from the ocean was found for the scaling parameter. It is acknowledged in conclusion that climate change renders the inference of IDF curves sensitive to increasing non-stationarity effects, which requires warning end-users that such tools should be used with care and discernment.

In Sahelian countries, a vast number of people are still affected every year by flood despite the efforts to prevent or mitigate these catastrophic events. This phenomenon is exacerbated by the incessant population growth and the increase of extreme natural events. Hence, the development of flood management strategies such as flood hazard mapping and Early Warning Systems has become a crucial objective for the affected nations. This study presents a comprehensive hazard assessment of the Nigerien reach of the Sirba River, the main tributary Middle Niger River. Hazard thresholds were defined both on hydrological analysis and field effects, according to national guidelines. Non-stationary analyses were carried out to consider changes in the hydrological behavior of the Sirba basin over time. Data from topographical land surveys and discharge gauges collected during the 2018 dry and wet seasons were used to implement the hydraulic numerical model of the analyzed reach. The use of the proposed hydraulic model allowed the delineation of flood hazard maps as well the calculation of the flood propagation time from the upstream hydrometric station and the validation of the rating curves of the two gauging sites. These significative outcomes will allow the implementation of the Early Warning System for the river flood hazard and risk reduction plans preparation for each settlement.

Urbanization resulting from a sharply increasing demographic pressure and the development of infrastructures have made the populations of many tropical areas more vulnerable to extreme rainfall hazards. Characterizing extreme rainfall distribution in a coherent way in space and time is thus becoming an overarching need that requires using appropriate models of IDF curves. Using 14 series of 5-min rainfall records (aggregated at a basis time-step of 1 h) collected in Senegal, a comparison of two GEV&scaling models is carried out, leading to adopt the most parsimonious one, built around four parameters. A bootstrap approach is proposed to compute the uncertainty associated with the estimation of these 4 parameters and of the related rainfall return levels for durations ranging from 1 h to 24 h. This study confirms previous works showing that simple scaling holds for characterizing the time-space structure of extreme rainfall in tropical regions such as sub-Saharan Africa. It further provides confidence intervals for the parameter estimates, and shows that the uncertainty linked to the estimation of the GEV parameters, is 3 to 4 times larger than the uncertainty linked to the inference of the scaling parameter. From this model, maps of IDF parameters over Senegal are produced, providing a spatial vision of their organization over the country, with a North to South gradient for the location and scale parameters of the GEV. An influence of the distance from the ocean was found for the scaling parameter. It is acknowledged in conclusion that climate change renders the inference of IDF curves sensitive to increasing non stationarity effects, requiring to warn end-users that they should be used with care and discernment.