Abstract :
A dynamic-stochastic approach, which combines a deterministic model of snowmelt runoff formation with a stochastic weather generator, has been proposed. The model describes snow accumulation and melt, vertical heat and moisture transfer in a soil, detention of melt water by the depressions at the catchment surface, overland and channel flow. The weather generator includes stochastic models that produce daily values of precipitation, air temperature, and air humidity during a whole year. Daily weather variables have been simulated by Monte Carlo procedure and transposed to snowmelt flood hydrographs on the basis of continuous simulation by the model of runoff generation. A specific censoring procedure has been developed to select among the generated weather scenarios the ones that can lead to generation of the extremely high floods. The developed procedure makes simulations more efficient and computationally fast. The proposed approach has been used to generate extreme snowmelt floods exceeding the maximum observed flood in the Seim River basin of central European Russia (catchment area, 7460 km2), to estimate their frequencies and, importantly, to assess characteristic conditions of the genesis of such extreme floods.
