The increasing demand for uncertainty assessment in streamflow forecasts has drawn the hydrological community's interest toward ensemble forecasting techniques. The widespread deterministic hydrological forecasting point of view focuses to a great extent on the search for a hydrological model that would come as close as possible to “perfection” (i.e. the aim is to implement a model that produces a point forecast that is as close as possible as the observed outcome). On the other hand, ensemble forecasting departs from the deterministic point of view by avoiding the assumption that the “perfect” model exists and instead focuses on issuing a type of forecast that accounts explicitly for the uncertainty inherent to the forecasting process as a whole. In this paper, one-day-ahead hydrological ensemble forecasts obtained by stacked neural networks are presented and analysed. To do so, three simple performance assessment criteria are presented. Those criteria were originally developed in the meteorological and statistical communities to accommodate the need for a quality assessment methodology that is coherent with the probabilistic nature of ensemble weather forecasts. It will be shown that, even though the ensemble forecasts suffer from underdispersion, they outperform point forecasts.

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