Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall-and snowmelt-driven contributions to flood events. Few operational flood-forecast models are available in the region.Here, we integrated a number of proven technologies in a novel way to produce a super-ensemble forecast system for the Englishman River, a flood-prone stream on Vancouver Island. This three-day-ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network-based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high-flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013-2014 storm season suggest that the prediction system is operationally useful and robust.(KEY TERMS: flooding; meteorology; runoff; computational methods; surface water hydrology; artificial neural networks; forecasting; uncertainty assessment; probabilistic; NAEFS; numerical weather prediction; multimodel ensemble; machine learning.)