A dynamic‐stochastic model of flood generation consisting of a distributed physically based model of snowmelt runoff genesis and a stochastic weather generator has been used for the assessment of extreme flood risk. Coupling this model with Monte‐Carlo simulations of meteorological series allows one to calculate long series of runoff hydrographs and the exceedance probabilities of flood peak discharges and volumes. The implementation of such a dynamic‐stochastic methodology may provide an improvement in extreme flood risk assessment in comparison with the traditional flood frequency analysis of the hydrological series. However, for very rare events, the uncertainty in estimating flood risk may increase significantly. To decrease this uncertainty, it has been suggested to combine the peak discharge series obtained by dynamic‐stochastic simulations with the probable maximum discharge (PMD) calculated through the physically based model of snowmelt runoff generation. This combination is achieved by fitting the estimated exceedance probabilities of simulated peak discharges by the Johnson distribution with the PMD as the parameter. The sensitivity of the fitted Johnson distribution to the errors of the PMD estimations is analysed. A case study in Russia is carried out for the Vyatka River basin (the catchment area is 124 000 km 2 ).
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Kuchment et al. (2011) studied this question.