Analysis of peaks over threshold within daily data for flood frequency curve assessment

We report an attempt to relate basic properties of stochastic processes of basin intense rainfalls and floods, in order to improve the prediction of flood risk by moving information from rainfalls to floods. This approach is based on a stochastic model of the extreme precipitation in which a wet and a dry season can be distinguished. During the wet period, the process of rainfall occurrences can be assumed as Poissonian and the distribution of marks can be derived trough the analysis of the peak over the threshold (POT) process. The basin operates on the rainfall process as a stochastic filter, constituted by a stochastic threshold S on precipitation, that reduces the number of runoff events. Theoretical analysis of the filter mechanism provides the relationships between stochastic parameters of maximum rainfall and flood. An application to data from a Southern Italy basin showed that magnitude of S is higher at low probability level of X and Y than at high probabilities, and the decay is rather steep. This finding seems to suggest a peculiar two-state behaviour for the basin antecedent moisture conditions: a wet state, in correspondence of annual maximum events, and a dry state for base process, both during the wet season (October-May).