There is growing interest in the application of "continuous simulation'' conceptual rainfall-runoff models for flood frequency estimation as an adjunct to event-based or statistical design methodology. The approach has advantages that stem from the use of models with continuous water balance accounting. Conceptual rainfall-runoff models usually require calibration, which in turn requires gauged rainfall and flow data. One of the key challenges is therefore to develop ways of generalizing models for use at ungauged sites. Recent work has produced a prototype scheme for achieving this aim in Great Britain for two catchment models by relating model parameters to spatial catchment properties, such as soils, topography, and geology. In this paper we present an analysis of the uncertainty associated with one of the generalized models ( the "probability distributed model'') in terms of confidence intervals for simulations at test sites that are treated as if they were ungauged. This is done by fitting regression relationships between hydrological model parameters and catchment properties so as to estimate the parameters as distribution functions for the ungauged site case. Flood flow outputs are then simulated from the parameter distributions and used to construct approximate confidence intervals. Comparison with gauged data suggests that the generalized model may be tentatively accepted. Uncertainty in the modeled flood flows is often of a similar order to the uncertainty surrounding a more conventional statistical model, in this case a single-site generalized Pareto distribution fitted to the gauged data.