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Quantifying the uncertainty of flood forecasts by ensemble methods is becoming increasingly important for operational purposes. The aim of this paper is to ex- amine how the ensemble distribution of precipitation fore- casts propagates in the catchment system, and to interpret the flood forecast probabilities relative to the forecast errors. We use the 622 km 2 Kamp catchment in Austria as an exam- ple where a comprehensive data set, including a 500 yr and a 1000 yr flood, is available. A spatially-distributed contin- uous rainfall-runoff model is used along with ensemble and deterministic precipitation forecasts that combine rain gauge data, radar data and the forecast fields of the ALADIN and ECMWF numerical weather prediction models. The analy- ses indicate that, for long lead times, the variability of the precipitation ensemble is amplified as it propagates through the catchment system as a result of non-linear catchment re- sponse. In contrast, for lead times shorter than the catch- ment lag time (e.g. 12 h and less), the variability of the pre- cipitation ensemble is decreased as the forecasts are mainly controlled by observed upstream runoff and observed pre- cipitation. Assuming that all ensemble members are equally likely, the statistical analyses for five flood events at the Kamp showed that the ensemble spread of the flood forecasts is always narrower than the distribution of the forecast errors. This is because the ensemble forecasts focus on the uncer- tainty in forecast precipitation as the dominant source of un- certainty, and other sources of uncertainty are not accounted for. However, a number of analyses, including Relative Op- erating Characteristic diagrams, indicate that the ensemble spread is a useful indicator to assess potential forecast errors for lead times larger than 12 h.

Ensemble prediction of floods – catchment non-linearity and forecast probabilities