This paper revisits the strength and stationarity of linear statistical relationships between indices of monsoon rainfall derived from rain gauge data for three regions in tropical West Africa [West Sahel (WS), Central Sahel (CS), and Guinea Coast (GC)] and various indices describing remote variations in the ocean-atmosphere climate systems from 1921 to 2009. The results reveal that both the Atlantic Multi-decadal Oscillation (AMO) and the Atlantic Meridional Mode (AMM) show a positive linear correlation to Sahel rainfall. The percent variance explained (PVE) ranges from 10 to 25%. The correlation stems from periods longer than 8 years for AMO, but AMM does show a correlation on interannual time scales for WS that was absent in the 1970s and 1980s. The PVE by El Niño Southern Oscillation (ENSO) indices is, though statistically significant, on the order of 10% and found mainly on the interannual time scale. A strong and stable correlation with PVEs larger than 50% is found between the sea surface temperatures (SSTs) in the Atlantic 3 region (ATL3, 0 • -20 • W, 3 • S-3 • N) and the GC rainfall. On the other hand, the correlation between ATL3 and WS rainfall changed from significantly negative to significantly positive after the 1970s. The Eastern Mediterranean SSTs are found to be significantly related to CS rainfall, especially in recent years with PVEs between 36 and 47%. Multi-linear regression analyses reveal that the relative importance of the Indian Ocean is 42% in the optimal regression model. For the CS, this value is 37% for the Eastern Mediterranean SSTs and 70% in case of the GC using the ATL3 index. However, except for the GC, non-stationarities in the correlation between the climate state indexes and West African monsoon (WAM) rainfall suggest the need of the application of different regression models depending on the active 'teleconnection regime'.

Extreme precipitation is a great concern for West Africa country, as it has serious consequence on key socio-economic activities. We use high resolution data from the Climate Hazards Group InfraRed Precipitation Stations (CHIRPS) to determine the spatial variability, trend of 8 extreme precipitation indices in West Africa and their relationship to remote indices. Spatial variability of extreme is characterized by maximum precipitation over the orographic regions, and in southern Sahel. The trend analysis shows a decrease of dry condition in Sahel and Sahara, and an increase tendency of wet indices over western Sahel and southern Sahel. The correlation analysis reveals that extreme precipitation in Sahel is strongly teleconnected to the Eastern Mediterranean Sea (EMS), whereas western and western-north Sahel is associated with both Atlantic Meridional Mode (AMM), Maiden Julian Oscillation phase 8 (MJO8), El Niño 3.4 index (NINO.3.4), and Trans-Atlantic-Pacific Ocean Dipole Index (TAPODI) but with different characteristics or directions. Guinean coast extreme precipitation is highly associated with Atlantic zone 3 SST anomaly (ATL3), Northern Cold Tongue Index (NCTI), TAPODI but also with an opposite sign with NINO.3.4 and in somewhat with the MJO8.

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.

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