The main purpose of this study is to assess the climate variability and change through statistical processing tools that able to highlight annual and monthly rainfall behavior between 1970 and 2010 in six strategical raingauges located in northern (Saint-Louis, Bakel), central (Dakar, Kaolack), and southern (Ziguinchor, Tambacounda) part of Senegal. Further, differences in sensitivity of statistical tests are also exhibited by applying several tests rather than a single one to check for one behavior. Dependency of results from statistical tests on studied sequence in time series is also shown comparing results of tests applied on two different periods (1970-2010 and 1960-2010). Therefore, between 1970 and, exploratory data analysis is made to give in a visible manner a first idea on rainfall behavior. Then, Statistical characteristics such as the mean, variance, standard deviation, coefficient of variation, skewness and kurtosis are calculated. Subsequently, statistical tests are applied to all retained time series. Kendall and Spearman rank correlation tests allow verifying whether or not annual rainfall observations are independent. Hubert's procedures of segmentation, Pettitt, Lee Heghinian and Buishand tests allow checking rainfall homogeneity. Trend is undertaken by first employing the annual and seasonal Mann-Kendall trend test, and in case of significance, magnitude of trend is calculated by Sen's slope estimator tests. All statistical tests are applied in the period of 1960-2010. Explanatory analysis data indicates upwards trends for records in northern and central and trend free for southern records. Application of multiple tests shows that the Kendall and spearman ranks correlation tests lead to same conclusion. The difference in tests sensitivity was shown by outcomes of homogene-

Providing useful inflow forecasts of the Manantali dam is critical for zonal consumption and agricultural water supply, power production, flood and drought control and management (Shin et al., Meteorol Appl 27:e1827, 2019). Probabilistic approaches through ensemble forecasting systems are often used to provide more rational and useful hydrological information. This paper aims at implementing an ensemble forecasting system at the Senegal River upper the Manantali dam. Rainfall ensemble is obtained through harmonic analysis and an ARIMA stochastic process. Cyclical errors that are within rainfall cyclical behavior from the stochastic modeling are settled and processed using multivariate statistic tools to dress a rainfall ensemble forecast. The rainfall ensemble is used as input to run the HBV-light to product streamflow ensemble forecasts. A number of 61 forecasted rainfall time series are then used to run already calibrated hydrological model to produce hydrological ensemble forecasts called raw ensemble. In addition, the affine kernel dressing method is applied to the raw ensemble to obtain another ensemble. Both ensembles are evaluated using on the one hand deterministic verifications such the linear correlation, the mean error, the mean absolute error and the root-mean-squared error, and on the other hand, probabilistic scores (Brier score, rank probability score and continuous rank probability score) and diagrams (attribute diagram and relative operating characteristics curve). Results are satisfactory as at deterministic than probabilistic scale, particularly considering reliability, resolution and skill of the systems. For both ensembles, correlation between the averages of the members and corresponding observations is about 0.871. In addition, the dressing method globally improved the performances of ensemble forecasting system. Thus, both schemes system can help decision maker of the Manantali dam in water resources management.

Hydropower is the world’s largest and most widely used renewable energy source. It is expected that climate and land use changes, as well as hydraulic engineering measures, will have profound impacts on future hydropower potential. In this study, the hydropower potential of the Bafing watershed was estimated for the near future (P1: 2035–2065) and the far future (P2: 2065–2095). For this purpose, the moderate scenario ssp 126 and the medium–high scenario ssp 370 were used to explore possible climate impacts. In three management scenarios, we tested the interaction of the existing Manantali Dam with two planned dams (Koukoutamba and Boureya) using an ecohydrological water management model. The results show that, under ssp 126, a 6% increase in annual river flow would result in a 3% increase in hydropower potential in the near future compared with the historical period of 1984–2014. In the far future, the annual river flow would decrease by 6%, resulting in an 8% decrease in hydropower potential. Under ssp 370, the hydropower potential would decrease by 0.7% and 14% in the near and far future, respectively. The investment in the planned dams has benefits, such as an increase in hydropower potential and improved flood protection. However, the dams will be negatively affected by climate change in the future (except in the near future (P1) under ssp 126), and their operation will result in hydropower potential losses of about 11% at the Manantali Dam. Therefore, to mitigate the effects of climate change and adjust the operation of the three dams, it is essential to develop new adaptation measures through an optimization program or an energy mix combining hydro, solar, and wind power.

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