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Precipitation is one of the major variables for many applications and disciplines related to water resources and the geophysical Earth system. Satellite retrieval systems, rain-gauge networks, and radar systems are complementary to each other in terms of their coverage and capability of monitoring precipitation. Satellite-rainfall estimate systems produce data with global coverage that can provide information in areas for which data from other sources are unavailable. Without referring to ground measurements, satellite-based estimates can be biased and, although some gauge-adjusted satellite-precipitation products have been already\ud developed, an effective way of integrating multi-sources of precipitation information is still a challenge. In this study, a specific area, the Sicilia Island (Italy), has been selected for the evaluation of satellite-precipitation products based on rain-gauge data. This island is located in the Mediterranean Sea, with a particular climatology and morphology, which can be considered an interesting test site for satellite-precipitation products in the European mid-latitude area. Four satellite products (CMORPH, PERSIANN, PERSIANN-CCS, and TMPA-RT) and two GPCP-adjusted products (TMPA and PERSIANN Adjusted) have been selected. Evaluation and comparison of selected products is performed with reference to data provided by the rain-gauge network of the Island Sicilia and by using statistical and graphical tools. Particular\ud attention is paid to bias issues shown both by only-satellite and adjusted products. In order to investigate the current and potential possibilities of improving estimates by means of adjustment procedures using GPCC ground precipitation, the data have been retrieved separately and compared directly with the reference rain-gauge network data set of the study area. Results show that bias is still considerable for all satellite products, then some considerations about larger area climatology, PMW-retrieval algorithms, and GPCC data are discussed to address this issue, along with the spatial and seasonal characterization of results

Extreme rainfall events have large impacts on society and are likely to increase in intensity under climate change. For design and management decisions, particularly regarding hydraulic works, accurate estimates of precipitation magnitudes are needed at different durations. In this article, an objective approach of the regional frequency analysis (RFA) has been applied to precipitation data for the island of Sicily, Italy. Annual maximum series for rainfall with durations of 1, 3, 6, 12, and 24 h from about 130 rain gauges were used. The RFA has been implemented using principal component analysis (PCA) followed by a clustering analysis, through the k‐means algorithm, to identify statistically homogeneous groups of stations for the derivation of regional growth curves. Three regional probability distributions were identified as appropriate from an initial wider selection of distributions and were compared – the three‐parameter log‐normal distribution (LN3), the generalized extreme value (GEV) distribution, and the two component extreme value (TCEV) distribution. The regional parameters of these distributions were estimated using L‐moments and considering a hierarchical approach. Finally, assessment of the accuracy of the growth curves was achieved by means of the relative bias and relative root‐mean‐square error (RMSE) using a simulation analysis of regional L‐moments. Results highlight that for the lower return periods, all distributions showed the same accuracy while for higher return periods the LN3 distribution provided the best result. The study provides an updated resource for the estimation of extreme precipitation quantiles for Sicily through the derivation of growth curves needed to obtain depth–duration–frequency (DDF) curves.

Climate change resulting from the enhanced greenhouse effect is expected to have great impacts on hydrological cycle and consequently on ecosystems. The effects of climate variability have direct implications on water management, as water availability is related to changes in temperature and precipitation regimes. At the same time, this kind of alterations drives ecological impacts on flora and fauna. For these reasons, many studies have been carried out to investigate the existence of some tendency in temperature and/or precipitation series in different geographic domains. In order to verify the hypothesis of temperature increase in Sicily (Italy), temperature data from about 80 spatially distributed weather stations have been deeply analysed. In this study, trend of annual, seasonal and monthly temperature time series have been examined for the period 1924–2006 to investigate possible evidences of climate changes in this region. In addition, also a long series (more than 200 years) has been analysed in order to individuate possible anomalies in the 20th century and to verify the presence, in the last decades, of a temperature increase larger than in the past. The Mann–Kendall non-parametric statistical test has been used to identify trends in temperature time series data. The test has been applied at local and regional scale for three different confidence level, considering the influence of serial correlation as well. The field significance of the regional results has been evaluated using a bootstrap technique of resampling that allows to eliminate the influence of data spatial correlation on Mann–Kendall test. The application of Mann–Kendall test on temperature data provides the evidence of a general warming in Sicily during the analysed period. The analysis of the long series demonstrates that the temperature trend is mainly due to the strong rising observed in the last years of the past century. In order to determine the spatial patterns of temperature trends and identify areas with a similar temperature evolution, the detected trends have been first subjected to the spatial auto correlation analysis and then interpolated using spatial analysis techniques in a GIS framework. Temperature trend maps have allowed to argue on the risk of aridity increase, in particular in the central and western part of the island. Copyright © 2013 Royal Meteorological Societ

An exhaustive comparison among different spatial interpolation algorithms was carried out in order to derive annual and monthly air temperature maps for Sicily (Italy). Deterministic, data-driven and geostatistics algorithms were used, in some cases adding the elevation information and other physiographic variables to improve the performance of interpolation techniques and the reconstruction of the air temperature field. The dataset is given by air temperature data coming from 84 stations spread around the island of Sicily. The interpolation algorithms were optimized by using a subset of the available dataset, while the remaining subset was used to validate the results in terms of the accuracy and bias of the estimates. Validation results indicate that univariate methods, which neglect the information from physiographic variables, significantly entail the largest errors, while performances improve when such parameters are taken into account. The best results at the annual scale have been obtained using the the ordinary kriging of residuals from linear regression and from the artificial neural network algorithm, while, at the monthly scale, a Fourier-series Water 2015, 7 1867 algorithm has been used to downscale mean annual temperature to reproduce monthly values in the annual cycle.