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.

Comparative Evaluation of the Rainfall Erosivity in the Rieti Province, Central Italy, Using Empirical Formulas and a Stochastic Rainfall Generator

“…Results of numerical tests carried out for the Colli sul Velino rain gauge station were presented and discussed. In particular, the observed rainfall data were compared with numerical results obtained using a synthetic rainfall generator model developed by De Luca et al [44][45][46][47][48]. Some peculiar features were observed.…”

“…However, continuous high-resolution data sets are very short or absent in many locations, with respect to Annual Maximum Rainfall (AMR) with different durations that usually are available with an adequate sample size. For this reason, the possibility to calibrate the model using only AMR data [43][44][45] and basic statistics, such as cumulate values for monthly or annual rainfall, has been adopted here. Such choice allows for a better reconstruction of extreme events, which are of main interest in many hydrological studies, for instance in the case of soil erosion caused by intense rainfall events.…”

STORAGE (STOchastic RAinfall GEnerator): A User-Friendly Software for Generating Long and High-Resolution Rainfall Time Series

“…Recent works [6,7,47] investigated the possibility to calibrate an SRG (i.e., to carry out the parametric estimation, on the basis of available sample data, in order to better reproduce some features of interest) by using only sample series at coarser time scales, which are usually longer than continuous data with a high resolution. In this framework, a modified version of the Neymann-Scott Rectangular Pulse (NSRP) model was implemented with Visual Basic for Application (VBA) macros in MS Excel, and the realized software, named STORAGE, is discussed in the present work.…”