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.

The ability of various statistical techniques to forecast the July-August-September (JAS) total rainfall and monthly streamflow in the Sirba watershed (West Africa) was tested. First, multiple linear regression was used to link predictors derived from the Atlantic and Pacific sea-surface temperatures (SST) to JAS rainfall in the watershed up to 18 months ahead; then, daily precipitation was generated using temporal disaggregation; and finally, a rainfall-runoff model was used to generate future hydrographs. Different combinations of lag times and time windows on which SSTs were averaged were considered. Model performance was assessed using the Nash-Sutcliffe coefficient (E f), the coefficient of determination (R 2) and a three-category hit score (H). The best results were achieved using the Pacific Ocean SST averaged over the March-June period of the year, before the rainy season, and led to a performance of R 2 = 0.458, E f = 0.387 and H = 66.67% for JAS total rainfall, and R 2 = 0.552, E f = 0.487 and H = 73.28% for monthly streamflow.

Since the 90s, several studies were conducted to evaluate the predictability of the Sahelian rainy season and propose seasonal rainfall forecasts to help stakeholders to take the adequate decisions to adapt with the predicted situation. Unfortunately, two decades later, the forecasting skills remains low and forecasts have a limited value for decision making while the population is still suffering from rainfall interannual variability: this shows the limit of commonly used predictors and forecast approaches for this region. Thus, this paper developed and tested new predictors and new approaches to predict the upcoming seasonal rainfall amount over the Sirba watershed. Predictors selected through a linear correlation analysis were further processed using combined linear methods to identify those having high predictive power. Seasonal rainfall was forecasted using a set of linear and non-linear models. An average lag time up to eight months was obtained for all models. It is found that the combined linear methods performed better than non-linear, possibly because non-linear models require larger and better datasets for calibration. The R 2 , Nash and Hit rate score are OPEN ACCESSClimate 2015, 3 728 respectively 0.53, 0.52, and 68% for the combined linear approach; and 0.46, 0.45, 61% for non-linear principal component analysis.

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Flood is one of the most important natural disasters that cause huge loss of life and properties every year around the world. Moreover, the International Federation of the Red Cross and Red Crescent Societies pointed out that floods were by far the greatest cause of homelessness. In West Africa, many countries are damaged from flooding almost every season. Thus, this study aimed to set a seasonal flood forecast model and carried out an evaluation of the level of risk associated with each seasonal forecast. HEC-RAS (Hydrologic Engineering Centers River Analysis System) was used to develop a hydro-dynamical model of Niger river on a 160km reach (80km upstream to 80kmdownstream of Niamey), then a simple risk measure was used to calculate the probability of overtopping the flood protection dykes in Niamey. Results show that the hydro-dynamical model reproduced well the rating curve over the period 2009-2014. A subsequent copula analysis demonstrated a dependency between flow on the Niger river and flow on the Sirba River, the main tributary contributing to the seasonal flood at Niamey. The Gumbel copula was found to be the best among the tested 5 copulas to represent the dependency between peak flow on the main channel of the Niger River and concomitant flow on the Sirba river. It is found that for the six dykes the probabilities of being overtopped by the flood range from very high (100%) to relatively low (16.67 %) over the 34 years of simulation.