A significant decrease in mean river flow as well as shifts in flood regimes have been reported at several locations along the River Niger. These changes are the combined effect of persistent droughts, damming and increased consumption of water. Moreover, it is believed that climate change will impact on the hydrological regime of the river in the next decades and exacerbate existing problems. While decision makers and stakeholders are aware of these issues, it is hard for them to figure out what actions should be taken without a quantitative estimate of future changes. In this paper, a Soil and Water Assessment Tool (SWAT) model of the Niger River watershed at Koulikoro was successfully calibrated, then forced with the climate time series of variable length generated by nine regional climate models (RCMs) from the AMMA-ENSEMBLES experiment. The RCMs were run under the SRES A1B emissions scenario. A combination of quantile-quantile transformation and nearestneighbour search was used to correct biases in the distributions of RCM outputs. Streamflow time series were generated for the 2026-2050 period (all nine RCMs), and for the 2051-2075 and 2076-2100 periods (three out of nine RCMs) based on the availability of RCM simulations. It was found that the quantile-quantile transformation improved the simulation of both precipitation extremes and ratio of monthly dry days/wet days. All RCMs predicted an increase in temperature and solar radiation, and a decrease in average annual relative humidity in all three future periods relative to the 1981-1989 period, but there was no consensus among them about the direction of change of annual average wind speed, precipitation and streamflow. When all model projections were averaged, mean annual precipitation was projected to decrease, while the total precipitation in the flood season (August, September, October) increased, driving the mean annual flow up by 6.9% (2026-2050), 0.9% (2051-2075) and 5.6% (2076-2100). A t-test showed that changes in multi-model annual mean flow and annual maximum monthly flow between all four periods were not statistically significant at the 95% confidence level.

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Temporary water bodies' dynamics play an important role in the epidemiological chain-borne diseases such as Rift Valley fever as they are the main breeding habitats for mosquitoes. During the rainy season, hundreds of these temporary water bodies appear and grow in the Ferlo region (Senegal). The purpose of this research is to generate historical and future time series water levels and areas at three temporary ponds located in the environment and health observatory of Barkedji. A simple lumped hydrological model was developed for that purpose. It describes each pond watershed as three interconnected reservoirs: canopy, surface storage and soil storage and uses a linear relation to describe infiltration, percolation and baseflow (out of the soil reservoir). Given the depth of the water table in the region, percolation out of the soil surface is considered lost. Evapotraspiration was calculated using the Penman equation and withdraws water from the canopy and surface water reservoirs. Excess runoff from the soil storage is turned into runoff using a triangular unit hydrograph. The calibration was done using two years of hydrological and climatic data collected during the 2011 and 2012 rainy seasons. The calibration was successful and water level in the two ponds was simulated with a Root Mean Square Error (RMSE) of 11.2 to 15 cm. Because of the short duration of the observation, no validation could be done. Given the excellent agreement of the simulated and observed water levels during the calibration phase, the modeling exercise was considered to be successful. The developed models were used to generate historical time series of pond areas and correlate these to mosquitoes' infestation in the region. Future time series of pond areas were also generated using downscaled outputs of three regional climate models from the AMMA ENSEMBLES experiment. The generated pond levels and areas are being M. Bop et al. 742 used to assess the evolution of the disease in the next 40 years.

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