The Upper Niger Basins (UNB) and the Inner Niger Delta (IND) are integral parts of the Niger River Basin, which flows through 10 countries and constitutes the third longest river in Africa. Natural climate variability and human interventions are two major factors affecting the hydrological regime in the UNB and IND. This study focuses on the later factor, by assessing the hydrological impacts of key existing and planned manmade structures and irrigation schemes in the UNB: the Sélingué (existing dam in Mali), four variants of the Fomi/Moussako dam (planned in Guinea), and Office du Niger (irrigation scheme located in Mali). The Fomi /Moussako dam will be located in the headwaters of the UNB and therefore, is expected to alter the hydrological regime in large parts of the watershed. Expected impacts include a reduction of the flood peak which will adversely affect critical ecosystems in the IND, and higher flows directly downstream of the dams in the dry season to sustain irrigation. These higher flows will, however, be consumed by Office du Niger irrigation scheme, leading to possible severe water shortages downstream of the irrigation scheme and in the IND. This is likely to affect the Malian economy and the poorest parts of its population, as the IND is crucial for the socio-economic and ecological preservation and development of the population surrounding it. The hydrological impacts of the dams and the irrigation scheme were evaluated in this study by developing a model of the IND and UNB using SWAT (Soil and Water Assessment Tool). After the model was calibrated, the effects of the dams and the irrigation scheme on selected flow statistics (mean and standard deviation) were determined at fourteen hydrological stations. In general, the results have shown that (1) the Fomi/Moussako dam will noticeably reduce the downstream high flows, and reduce the average flow; (2) if the Fomi/Moussako dam was to be built, the alternatives with the least storage volume (Moussako 388.5') will have the least impacts on the downstream flows. To assist in related decision making for various users, a Decision Support System (DSS) was also developed. The goal of the DSS is to help users analyze the effects of dams and irrigation on the flow regime by performing a comparative analysis (presence and absence of dams and irrigation in the river). A number of potential adaptation measures were also proposed.

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Dams and reservoirs with multiple purposes require effective management to fully realize their purposes and maximize efficiency. For instance, a reservoir intended mainly for the purposes of flood control and hydropower generation may result in a system with primary objectives that conflict with each other. This is because higher hydraulic heads are required to achieve the hydropower generation objective while relatively lower reservoir levels are required to fulfill flood control objectives. Protracted imbalances between these two could increase the susceptibility of the system to risks of water shortage or flood, depending on inflow volumes and operational policy effectiveness. The magnitudes of these risks can become even more pronounced when upstream use of the river is unregulated and uncoordinated so that upstream consumptions and releases are arbitrary. As a result, safe operational practices and risk management alternatives must be structured after an improved understanding of historical and anticipated inflows, actual and speculative upstream uses, and the overall hydrology of catchments upstream of the reservoir. One of such systems with an almost yearly occurrence of floods and shortages due to both natural and anthropogenic factors is the dual reservoir system of Kainji and Jebba in Nigeria. To analyze and manage these risks, a methodology that combines a stochastic and deterministic approach was employed. Using methods outlined by Box and Jenkins (1976), autoregressive integrated moving average (ARIMA) models were developed for forecasting Niger river inflows at Kainji reservoir based on twenty-seven-year-long historical inflow data (1970-1996). These were then validated using seven-year inflow records (1997-2003). The model with the best correlation was a seasonal multiplicative ARIMA (2,1,1)x(2,1,2)12 model. Supplementary iv validation of this model was done with discharge rating curves developed for the inlet of the reservoir using in situ inflows and satellite altimetry data. By comparing net inflow volumes with storage deficit, flood and shortage risk factors at the reservoir were determined based on (a) actual inflows, (b) forecasted inflows (up to 2015), and (c) simulated scenarios depicting undefined competitive upstream consumption. Calculated highrisk years matched actual flood years again suggesting the reliability of the model. Monte Carlo simulations were then used to prescribe safe outflows and storage allocations in order to reduce futuristic risk factors. The theoretical safety levels achieved indicated risk factors below threshold values and showed that this methodology is a powerful tool for estimating and managing flood and shortage risks in reservoirs with undefined competitive upstream consumption