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The feared global climate change could have important effects on various environmental variables including rainfall in many countries around the world. Changes in precipitation regime directly affect water resources management, agriculture, hydrology and ecosystems. For this reason it is important to investigate the changes in the spatial and temporal rainfall pattern in order to improve water management strategies. In this study a non-parametric statistical method (Mann–Kendall rank correlation method) is employed in order to verify the existence of trend in annual, seasonal and monthly rainfall and the distribution of the rainfall during the year. This test is applied to about 250 rain gauge stations in Sicily (Italy) after a series of procedures finalized to the estimation of missing records and to the verification of data consistency. In order to understand the regional pattern of precipitation in Sicily, the detected trends are spatially interpolated using spatial analysis techniques in a GIS environment. The results show the existence of a generalized negative trend for the entire region.

This paper compares two generators of yearly water availabilities from sources located at multiple sites with regard to their ability to reproduce the characteristics of historical critical periods and to provide reliable results in terms of the return period of critical sequences of different length. The two models are a novel multi-site Markov mixture model explicitly accounting for drought occurrences and a multivariate ARMA. In the case of the multisite Markov mixture model parameter estimation is limited to a search in the parameter space guided by the value of parameter λ to show the sensitivity of the model to this parameter. Application to two of the longest time series of streamflows available in Sicily (Italy) shows that the models can provide quite different results in terms of estimated return periods of historic droughts, although they seem to perform more uniformly when it comes to simulate drought-related statistics such as drought length, severity and intensity. The role of parameter selection for the multisite Markov mixture model and of the marginal probability of generated flows in providing results consistent with the characteristics of the observed series is discussed. Both models are applied to the system of sources supplying the city of Palermo (Sicily) and its environs showing the applicability of the newly developed multisite Markov mixture model to medium-to-large scale water resources systems.

River discharge observations are usually affected by uncertainty, which is due to many concurrent causes and strongly affects the response of rainfall-runoff models. The present paper is aimed at studying the influence of imperfect rating curve knowledge on the uncertainty of the response of a daily conceptual linear-nonlinear rainfall-runoff model. To describe the impact of imperfect rating curve knowledge, simulations have been conducted using a conceptual rainfall-runoff model and continuous daily series of rainfall, air temperature and discharges recorded in a Sicilian catchment. The GLUE procedure was used to introduce the uncertainty of the rating curve in a classical rainfall-runoff model uncertainty analysis by randomly modifying the original rating curve. The final results show an increase of uncertainty with respect to the original rating curves for the majority of model predictions, but more significantly for overestimated rating curves than underestimated ones.

The aim of this study is to investigate and quantify changes in the rainfall regime of the metropolitan area of Palermo characterised by increasingly strong urbanisation. The rainfall data, considered in this study, were collected on a yearly basis from eight rain gauges within and outside the metropolitan area of Palermo, Sicily, Italy. A preliminary analysis made on the annual total rainfall depths showed a global reduction of total annual rainfall, with two different trends: more regular for the series observed in the rain gauges within the urbanised area and more variable for the series observed in the rain gauges outside the area. A further analysis has been performed using the series of maximum intensity for fixed duration (1, 3, 6, 12, 24 hrs) and annual daily maxima. The analysis of the trend in the extreme rainfall series has been performed by estimating the maximum rainfall depth corresponding to a fixed return period using the EV1 distribution with parameters estimated using L-moments. The analysis of all series indicates a global reduction of rainfall intensities, both for internal and external series, in disagreement with the results obtained by other authors.

In the original publication of the article, the first and last names of the authors are interchanged and published incorrectly. The correct author names are given below: Paola Pennisi, Laura Giallongo, Giusy Milintenda, Michela Cannarozzo., (© 2021. Marta Olivetti Belardinelli and Springer-Verlag GmbH Germany, part of Springer Nature.)