Modelling annual maximum daily rainfall with the STORAGE (STOchastic RAinfall GEnerator) model | Zendy

Andrea Petroselli‬ | Zendy; Davide Luciano De Luca | Zendy; Dariusz Młyński | Zendy; Andrzej Wałęga | Zendy

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Modelling annual maximum daily rainfall with the STORAGE (STOchastic RAinfall GEnerator) model

Author(s) -

Andrea Petroselli‬,

Davide Luciano De Luca,

Dariusz Młyński,

Andrzej Wałęga

Publication year - 2022

Publication title -

hydrology research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.665

H-Index - 48

eISSN - 1996-9694

pISSN - 0029-1277

DOI - 10.2166/nh.2022.100

Subject(s) - environmental science , hydrology (agriculture) , stochastic modelling , climatology , meteorology , atmospheric sciences , geography , mathematics , statistics , geology , geotechnical engineering

In this work, the capability of STORAGE (STOchastic RAinfall GEnerator) model for generating long and continuous rainfall series for the upper Vistula basin (southern Poland) is tested. Specifically, in the selected area, only parameters of depth–duration–frequency curves are known for sub-daily rainfall heights (which are usually estimated in an indirect way by using Lambor's equations from daily data), while continuous daily series with a sufficient sample size are available. Attention is focused on modelling the sample frequency distributions of daily annual maximum rainfall. The obtained results are promising for further elaborations, concerning the use of STORAGE synthetic continuous rainfall data as input for a continuous rainfall-runoff approach, to be preferred with respect to classical event-based modelling.

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