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<p>Hydropower operations are commonly prescribed as part of a grid-wide coordination process by an Independent System Operator (ISO). The scheduling problem is usually divided into two coupled problems: short- and medium-term scheduling. The medium term problem, usually within a planning horizon of a few years, takes into account uncertain inflows to every hydropower plant in the grid. This uncertainty is often represented by a scenario tree constructed from historical records. The result of this stochastic optimization problem is a set of Future Value Functions (FVF) of water in the reservoirs. These functions represent the carryover storage value, as avoided future thermal costs, for each week within the planning horizon. These FVFs are then used as a boundary condition for short-term scheduling within each week.</p><p>Chile has suffered a 10-year severe drought since 2010. Moreover, climate projections for Chile suggest an intensification of droughts in the future, in terms of both frequency and magnitude. From the water-energy nexus perspective, this phenomenon would rise energy costs and prices, and at the same time, push the electric coordinator to feed the system with less clean sources of electricity.</p><p>This work proposes and tests alternative ways to introduce plausible mega-droughts in Chile as part of the power scheduling process. We develop series representing plausible future conditions of drought and severe drought, preserving the time and spatial correlation structure of inflows. These scenarios are then used, along with historical information, to develop FVFs that take into account those severe drought scenarios. The method is tested in Chile’s main grid, represented by 624 power plants, 103 inflow points, 13 reservoirs, and 58 demand nodes.</p><p>The FVFs obtained from each alternative approach are then simulated under a wide range of futures. Results show that the introducing very severe droughts is not the best course of action, as the corresponding FVFs perform very poorly under moderately dry futures. In contrast, introducing scenarios with a moderate dry bias performs better over a wide range of future conditions, except for extremely severe droughts.   </p>