This paper proposes a multi-stage stochastic programming formulation for the reservoir management problem. Our problem specifically consists in minimizing the risk of floods over a fixed time horizon for a multi-dimensional hydro-electrical complex. We consider well-studied linear time series model and enhance the approach to consider heteroscedasticity. Using these stochastic processes under very general distributional assumptions, we efficiently model the support of the joint conditional distribution of the random inflows and update these sets as new data is assimilated. Using robust optimization techniques and affine decision rules, we embed these time series in a tractable convex program. This allows us to obtain good quality solutions rapidly and test our model in a realistic simulation framework using a rolling horizon approach. Finally, we study a real river system in Western Québec and perform various numerical experiments based on different inflow generators.