Contribution of TRMM 3B42 Data to Improve Knowledge on Rainfall in the Kayanga/Geba River Basin (Republic of Guinea, Senegal and GuineaBissau)

The use of Tropical Rainfall Measuring Mission (TRMM) data is an option for counteracting challenge of the lack of ground based observations, particularly in Kayanga/Gêba. This paper undertakes validation of monthly TRMM rainfall estimates before using it to understand the spatial and temporal variability in the Basin. This validation based on application of statistical study, made it possible to obtain interesting results with correlation coefficients varying from 0.92 to 0.96 and Nash indices close to 1. The analysis of the seasonal rainfall pattern shows consistence with ground based observations. The study of the annual cycle reveals that their interannual variability is similar to that of ground based observations. Finally, the interpolation of average monthly rainfall in the basin highlights the NorthSouth rainfall gradient, which shows that the South is wetter than the North, with differences more pronounced in August and September

Intensity-duration-frequency (IDF) rainfall curves in Senegal

International audienceUrbanization 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