Multi-stage stochastic hydrothermal planning stands as one of the most critical problems in the power systems industry, mostly due to its vast implication in the system operation. The multi-stage stochastic hydrothermal scheduling refers to determining the economic dispatch of the power plants that minimize the global operation cost under the system s physical constraints. One of the main difficulties of the problem lies in the representations of uncertainty, as the dispatch decision must consider the different possible scenarios of water inflow, renewable generation, and the demand. More recently, we have seen a worldwide speed up in the integration of variable renewable sources. Nonetheless, these sources have a greater uncertainty in the short-term than the world has ever experienced. Therefore, to support the dispatch scheduling, the models must accurately represent the uncertainties without increasing computational time. In this work it is proposed a novel formulation for a multistage stochastic week-ahead economic dispatch with unit-commitment. The model uses affine decision rules to be computationally tractable. The relationship between the decision rules and the scenario approach is explored, and by building the uncertainty set with the scenario approach, both the feasibility of the decision rule policy and the chance-constraint on the load balance are respected.