Calibration of NSRP models from extreme value distributions

Abstract

In this work, the authors investigated the feasibility of calibrating a model which is suitable for the generation of continuous high-resolution rainfall series, by using only data from annual maximum rainfall (AMR) series, which are usually longer than continuous high-resolution data, or they are the unique available data set for many locations. In detail, the basic version of the Neyman-Scott Rectangular Pulses (NSRP) model was considered, and numerical experiments were carried out, in order to analyze which parameters can mostly influence the extreme value frequency distributions, and whether heavy rainfall reproduction can be improved with respect to the usual calibration with continuous data. The obtained results were highly promising, as the authors found acceptable relationships among extreme value distributions and statistical properties of intensity and duration for the pulses. Moreover, the proposed procedure is flexible, and it is clearly applicable for a generic rainfall generator, in which probability distributions and shape of the pulses, and extreme value distributions can assume any mathematical expression.