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In this study, a spatiotemporal analysis of drought occurrence and trends across Sicily using ERA50-Land continuous gridded data is carried out. We first use the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) to evaluate drought conditions at various time scales from 1950 to 2023. Then, the Modified Mann–Kendall test was employed to detect trends and Sen's slope estimator was used to quantify their magnitude. An analysis of the historical series confirms that 2002 was the most severe drought year, impacting all time scales from short-term to long-term. The spatial analysis revealed that the western regions of Sicily experienced the highest severity and frequency of drought events. In contrast, the northeastern regions were less severely affected compared with the other parts of the island. The analysis detects significant increasing trends in SPI values in the eastern coastal areas of the island, which are related to a possible historical increase in precipitation. On the other hand, the analysis of the SPEI indicates significant decreasing trends in the western part of the island, which are mainly related to increased evapotranspiration rates. These results are partially consistent with previous analyses of future climate change scenarios, where changes in the SPEI values in the island are projected to be way clearer than changes in SPI values. [ABSTRACT FROM AUTHOR]

Drought is often monitored through standardized indices. However, while enabling comparisons across different climatic regions, standardization poses an issue when using indices to assess future climate change impacts on drought, since they have a null average by definition. To address this issue, in this study we introduce a dynamic approach where future changes are assessed by computing climate normals using moving time windows. The approach is applied to Sicily and Calabria (Southern Italy) using the standardized precipitation index (SPI) and the standardized precipitation–evapotranspiration index (SPEI), and considering climate change scenarios RCP4.5 and RCP8.5. An optimized ensemble weighted average (OEWA) of climate models is introduced to reduce model biases. The results indicate that the region is likely to experience an increase in drought events due to climate change. The findings highlight the need for revised drought identification strategies that account for non-stationarity in climate. [ABSTRACT FROM AUTHOR]

Climate change and the related temperature rise can cause an increase in evapotranspiration. Thus, the assessment of potential evapotranspiration (PET) trends is important to identify possible ongoing signals of climate change, in order to develop adaptation measures for water resource management and improve irrigation efficiency. In this study, we capitalize on the data available from a network of 46 complete meteorological stations in Sicily that cover a period of about 21 years (2002–2022) to estimate PET by the Food and Agriculture Organization (FAO) using the Penman–Monteith method at the daily time scale in Sicily (southern Italy). We then analyse the trends of PET and assess their significance by Sen's Slope and the Mann–Kendall test at multiple temporal scales (monthly, seasonal, and annual). Most of the locations do not show significant trends. For instance, at the annual timescale, only five locations have a significantly increasing trend. However, there are many locations where the monthly trend is statistically significant. The number of locations where monthly trend is significant is maximum for August, where 18 out of these 46 stations have an increasing trend. In contrast, in March, there are no locations with a significant trend. The location with the highest increasing trend of PET indicates trend slopes of 1.73, 3.42, and 10.68 mm/year at monthly (August), seasonal (summer), and annual timescales, respectively. In contrast, decreasing PET trends are present only at the monthly and seasonal scales, with a maximum of, respectively, −1.82 (July) and −3.28 (summer) mm/year. Overall, the findings of this study are useful for climate change adaptation strategies to be pursued in the region. [ABSTRACT FROM AUTHOR]

In recent times, several efforts have been addressed to understand the extent to which soil moisture estimations may improve the performance of landslide early warning systems (LEWSs). These systems have been traditionally based on rainfall intensity-duration thresholds. Still a limited number of studies explore the possible enhancement of the performance of LEWSs through the identification of hydro-meteorological thresholds. In this study, we propose a methodology for developing regional hydro-meteorological landslide triggering thresholds coupling mean rainfall intensity and soil moisture information. To test the potential improvements in prediction we use ERA5-Land reanalysis soil moisture data, available at four depth levels and hourly resolution. Two different instances are investigated, namely the identification of triggering thresholds using rainfall intensity and the soil moisture at each of four depth levels, and the identification of triggering thresholds using rainfall intensity and a combination of soil moisture at the four depths as obtained by principal component analysis (PCA). We propose thresholds in the form of a piece-wise linear equation. The equation's parameters are optimized in order to maximize the ROC True Skill Statistic (TSS) prediction performance metric. The proposed hydro-meteorological thresholds are tested on the case of Sicily Island (south Italy) and the performance is compared with those obtained through the traditional rainfall intensity-duration (ID) power-law thresholds. Overall, the results show that the soil moisture information adds a considerable value to the improved thresholds' performance since the ROC True Skill Statistic increases from 0.50 to 0.71. A similar performance is obtained when the first principal component derived from the PCA is used, proving PCA to be a valuable support tool for the identification of the proposed hydro-meteorological thresholds, as it allows to take into account the multi-layer information while keeping the thresholds two-dimensional. [ABSTRACT FROM AUTHOR]

To mark the 20th anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences, we highlight 11 key publications covering major subject areas of NHESS that stood out within the past 20 years. The papers cover all the topics contemplated in the European Geosciences Union (EGU) Division on Natural Hazards including dissemination, education, outreach and teaching. The selected articles thus represent excellent scientific contributions in the major areas of natural hazards and risks and helped NHESS to become an exceptionally strong journal representing interdisciplinary areas of natural hazards and risks. At its 20th anniversary, we are proud that NHESS is not only used by scientists to disseminate research results and novel ideas but also by practitioners and decision-makers to present effective solutions and strategies for sustainable disaster risk reduction. [ABSTRACT FROM AUTHOR]

To mark the twentieth anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences, we highlight eleven key publications covering major subject areas of NHESS that stood out within the past 20 years. The selected articles represent excellent scientific contributions in the major areas of natural hazards and risks and helped NHESS to become an exceptionally strong journal representing interdisciplinary areas of natural hazards and risks. At its 20th anniversary, we are proud that NHESS is not only used by scientists to disseminate research results and innovative novel ideas but also by practitioners and decision-makers to present effective solutions and strategies for sustainable disaster risk reduction. [ABSTRACT FROM AUTHOR]

In this communication we show how the use of artificial neural networks (ANNs) can improve the performance of the rainfall thresholds for landslide early warning. Results for Sicily (Italy), show how performance of a traditional rainfall event duration and depth power law threshold, yielding a true skill statistic (TSS) of 0.50, can be improved by ANNs (TSS = 0.59). Then we show how ANNs allow to easily add other variables, like peak rainfall intensity, with a further performance improvement (TSS = 0.64). This may stimulate more research on the use of this powerful tool for deriving landslide early warning thresholds. [ABSTRACT FROM AUTHOR]

Many recent studies indicate climate change as a phenomenon that significantly alters the water cycle in different regions worldwide, also implying new challenges in water resources management and drought risk assessment. To this end, it is of key importance to ascertain the quality of Regional Climate Models (RCMs), which are commonly used for assessing at proper spatial resolutions future impacts of climate change on hydrological events. In this study, we propose a statistical methodological framework to assess the quality of the EURO- CORDEX RCMs concerning their ability to simulate historic climate (temperature and precipitation) and drought characteristics (duration, accumulated deficit, and intensity) determined by the theory of runs, at seasonal and annual time scales, by comparison with high-density and high-quality ground-based observational datasets. In particular, the proposed methodology is applied to Sicily and Calabria regions (Southern Italy), where long historical precipitation and temperature series were recorded by the ground-based monitoring networks operated by the formerly Regional Hydrographic Offices, whose density is considerably greater than observational gridded datasets available at the European level, such as E-OBS. Results show that the more skilful models, able to reproduce, overall, precipitation and temperature variability, as well as drought characteristics, are based on the COSMO-CLM RCM, with the significant exception of the combination based on the HadGEM2-ES GCM and the RACMO RCM. Nevertheless, the choice of the most appropriate model depends on the specific variable analysed, as well as the temporal and spatial scale of interest. From this point of view, the proposed methodology highlights the skills and weaknesses of the different configurations, supporting a proper model selection for climate projections depending on the examined hydrologic processes. [ABSTRACT FROM AUTHOR]