In this work, a comprehensive methodology for trend investigation in rainfall time series, in a climate-change context, is proposed. The crucial role played by a Stochastic Rainfall Generator (SRG) is highlighted. Indeed, SRG application is particularly suitable to obtain rainfall series that are representative of future rainfall series at hydrological scales. Moreover, the methodology investigates the climate change effects on several timescales, considering the well-known Mann–Kendall test and analyzing the variation of probability distributions of extremes and hazard. The hypothesis is that the effects of climate changes could be more evident only for specific time resolutions, and only for some considered aspects. Applications regarded the rainfall time series of the Viterbo rain gauge in Central Italy.

This study tests stationary and non-stationary approaches for modelling data series of hydro-meteorological variables. Specifically, the authors considered annual maximum rainfall accumulations observed in the Calabria region (southern Italy), and attention was focused on time series characterized by heavy rainfall events which occurred from 1 January 2000 in the study area. This choice is justified by the need to check if the recent rainfall events in the new century can be considered as very different or not from the events occurred in the past. In detail, the whole data set of each considered time series (characterized by a sample size N > 40 data) was analyzed, in order to compare recent and past rainfall accumulations, which occurred in a specific site. All the proposed models were based on the Two-Component Extreme Value (TCEV) probability distribution, which is frequently applied for annual maximum time series in Calabria. The authors discussed the possible sources of uncertainty related to each framework and remarked on the crucial role played by ergodicity. In fact, if the process is assumed to be non-stationary, then ergodicity cannot hold, and thus possible trends should be derived from external sources, different from the time series of interest: in this work, Regional Climate Models’ (RCMs) outputs were considered in order to assess possible trends of TCEV parameters. From the obtained results, it does not seem essential to adopt non-stationary models, as significant trends do not appear from the observed data, due to a relevant number of heavy events which also occurred in the central part of the last century.

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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.