The value of seasonal streamflow forecasts for the hydropower industry has long been assessed by considering metrics related to hydropower availability. However, this approach overlooks the role played by hydropower dams within the power grid, therefore providing a myopic view of how forecasts could improve the operations of large-scale power systems. With the aim of understanding how the value of streamflow forecasts penetrates through the power grid, we developed a coupled-water energy model that is subject to reservoir inflow forecasts with different levels of accuracy. We implement the modelling framework on a real-world case study based on the Cambodian grid, which relies on hydropower, coal, oil, and imports from neighboring countries. In particular, we evaluate the performance in terms of metrics selected from both the reservoir and power systems, including available and dispatched hydropower, power production costs, CO2 emissions, and transmission line congestion. Through this framework, we demonstrate that streamflow forecasts can positively impact the operations of hydro-dominated power systems, especially during the transition from wet to dry seasons. Moreover, we show that the value largely varies with the specific metric of performance at hand as well as the level of operational integration between water and power systems.