One of the most relevant and debated topics related to the effects of the climate change is whether intense rainfall events have become more frequent over the last decades. It is a crucial aspect, since an increase in the magnitude and frequency of occurrence of heavy rainfall events could result in a dramatic growth of floods and, in turn, human lives losses and economic damages.Because of its central position in the Mediterranean area, Sicily has been often screened with the aim to capture some trends in precipitation, potentially related to climate change. While Mann-Kendall test has been largely used for the rainfall trend detection, in this work a different procedure is considered. Precipitation trends are here investigated by processing the whole rainfall time-series, provided by the regional agency SIAS at a 10-min resolution, through the quantile regression method by aggregating precipitation across a wide spectrum of durations and considering different quantiles. Results show that many rain gauges are characterized by an increasing trend in sub-hourly precipitation intensity, especially at the highest quantiles, thus suggesting that, from 2002 to 2019, sub-hourly events have become more intense in most of the island. Moreover, by analysing some spatial patterns, it has been revealed that the south and the east of Sicily are more interested in significant increasing rainfall trends, especially at the 10-min duration. Finally, the comparison between the two procedures revealed a stronger reliability of the quantile regression in the trend analysis detection, mainly due to the possibility of investigating the temporal variation of the tails of precipitation distribution.

Extreme rainfall events have been more frequent in recent decades, potentially as a climate change effect. This has been leading to a higher risk of the failure of existing hydraulic infrastructures, and to a higher awareness regarding the unreliability of design rainfall calculated with reference to historical data recorded in the last century. With this in mind, the present study questions the stationary assumption of the rainfall Depth–Duration–Frequency curves commonly used in Sicily, the biggest island of the Mediterranean Sea. Quantiles derived from the most up-to-date regional method, regarding Sicily, based on observations in the period 1928–2010, have been compared with those extracted from a high-resolution dataset related to the period 2002–2022, provided by the SIAS agency. The results showed a remarkable underestimation of the rainfall quantiles calculated with the regional approach, especially at the shortest durations and low return periods. This means that new hydraulic works should be designed with reference to longer return periods than in the recent past, and those that currently exist may experience a higher risk of failure. Future investigation of this aspect is crucial for enhancing the effectiveness of water management and detecting hydrological risks under a changing climate.

Climate change affects all the components of the hydrological cycle. Starting from precipitation distribution, climate alterations have direct effects on both surface water and groundwater in terms of their quantity and quality. These effects lead to modifications in water availability for agriculture, ecology and other social uses. Change in rainfall patterns also affects the runoff of natural rivers. For this reason, studying runoff data according to classical hydrological approaches, i.e., statistical inference methods that exploit stationary probability distributions, might result in missing important information relevant to climate change. From this point of view, a new approach has to be found in the study of this type of data that allows for non-stationary analysis. In this study, the statistical framework known as Generalized Additive Models for Location, Scale and Shape (GAMLSS), which can be used to carry out non-stationary statistical analyses, was applied in a non-stationary frequency analysis of runoff data collected by four gauges widely distributed across Sicily (Italy) in the period 1916–1998. A classical stationary frequency analysis of these runoff data was followed by a different non-stationary frequency analysis; while the first was made using annual rainfall as a covariate, with the aim of understanding how certain statistical parameters of runoff distribution vary with changes in rainfall, the second derived information about the temporal variability of runoff frequencies by considering time as a covariate. A comparison between stationary and non-stationary approaches was carried out using the Akaike information criterion as a performance metric. After analyzing four different probability distributions, the non-stationary model with annual rainfall as a covariate was found to be the best among all those examined, and the three-parameter lognormal the most frequently preferred distribution.

<p>Nowadays, studying heavy rainfall events, characterized by significant rainfall depth concentrated in short durations, and by the presence of lightning, downbursts, and hail, is extremely important. The increasing attention to these phenomena is due to the fact that they may determine serious impacts on the population, economic activities, and the environment. Among heavy rainfall events, high-intensity and short-duration ones, are usually associated with the occurrence of convective cells.</p> <p>Since these events have been occurring in a more frequent way over the last two decades as a climate change effect and the Mediterranean area is considered one of the most prone areas to this type of event, this study focuses on the identification of heavy rainfall over Sicily, i.e., the biggest island of the Mediterranean Sea. The high-resolution rainfall time series (i.e., 10 minutes) here analyzed have been collected by the rain gauge network of the <em>Servizio Informativo Agrometeorologico Siciliano </em>(SIAS) within the period 2002 - 2021.</p> <p>Given that convective cells are usually characterized by high lightning activity, their detection has been carried out by means of a lightning dataset of <em>Blitzortung</em>, providing the location and time of lightning strikes for all of Europe on a daily scale since 2015. To reach this goal, different searching radii centred on the rain gauges and some conditions to weigh the distance between lightning strikes to the gauge, have been considered. This allowed exploring how far the lightning activity developed from the rain gauge, where rainfall is recorded.</p> <p>The detection of convective precipitation through lightning data has been then improved by using some reanalysis data, such as the Convective Available Potential Energy (CAPE), and the K-Index, from the ERA-5 database of the European Centre for Medium-Range Weather Forecasts (ECMWF). </p>

<p>Physical modelling of atmospheric processes, such as rainfall events, is often difficult due to the complexity of the atmosphere and the large number of variables involved. At the same time, it is necessary to know, as carefully as possible, some characteristics of precipitation processes, such as rainfall magnitude and frequency, in order to better understand their impacts on the territory. For this reason, statistical frameworks able to use external covariates to explain the physical process could be a central point in research.</p><p>Starting from the rainfall events identified from continuous data series from about 40 rain gauges located in Sicily, this paper aims at assessing the occurrence of rainfall events in a fixed interval of time according to a temporal contagion model (branching process) with external covariates, within a regression-like framework (Adelfio and Chiodi, 2021).</p><p>In detail, we extend the model formulation proposed by Meyer et al. (2012) in the context of infectious disease transmission, suggesting the use of a specific branching-type model, born in seismic context (the ETAS model, (Ogata 1988, 1998)), in a regression-oriented version modelling. In the temporal ETAS model, the expected frequency of events in a time unit can be defined as the sum of a term that describes the long-term variation and a term that describes the short-term variation.</p><p>Accounting for further potential covariates in the model specification of the short-term variation component, may both explain some of the overall variability of the studied phenomenon (i.e., for decreasing the unpredictable variability) and provide a more realistic description of the observed activity. The Forward Likelihood for prediction (FLP) method (Chiodi and Adelfio 2011) is used for estimating the ETAS model components with the covariates.</p><p>In this application the mean rainfall intensity, the duration and the anomalies in temperature and relative humidity of the events have been considered as external covariates of the model in order to explain the events frequency. The first results of the model appear to be interesting, and special attention will be paid to the sample of convective precipitation events identified using the same dataset (Sottile et al. 2021).</p>