Search

Landslide prediction is key for the development of early warning systems. In this work, we develop artificial neural networks (ANNs) that can identify landslide triggering conditions using soil moisture data in addition to precipitation. In particular, we use observed precipitation and ERA5-Land reanalysis soil moisture data at four different depth layers at the beginning and end of the precipitation events. Two different case studies, Sicily region (Italy), and a group of catchments in the Bergen area (Norway), are used to test the proposed approach against different climatic and geomorphological conditions. As a first step, traditional power law thresholds based on cumulative precipitation and duration (E-D) are derived by maximizing the true skill statistic (TSS) as a benchmark. For both study areas, ANNs using 87 different input combinations of precipitation characteristics and soil moisture data at multiple depth layers are analyzed. The developed ANN classifiers using soil moisture information in addition to precipitation outperform those using precipitation data only. Specifically, while power law E-D thresholds lead to a TSS maximum of 0.50 for both areas, the use of single-layer soil moisture yields a maximum TSS of 0.76 (0.78) for Sicily (Bergen area), while the use of multilayer soil moisture taken at both the start and the end of precipitation events yields a TSS = 0.79 (0.89). These results demonstrate that the proposed methodology is particularly promising for improving landslide prediction. [ABSTRACT FROM AUTHOR]

Assessing and monitoring the spatial extent of drought is of key importance to forecasting the future evolution of drought conditions and taking timely preventive and mitigation measures. A commonly used approach in regional drought analysis involves spatially interpolating meteorological variables (e.g., rainfall depth during specific time intervals, deviation from long-term average rainfall) or drought indices (e.g., Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index) computed at specific locations. While plotting a drought descriptor against the corresponding percentage of affected areas helps visualize the historical extent of a drought, this approach falls short of providing a probabilistic characterization of the severity of spatial drought conditions. That can be overcome by identifying drought Severity-Area-Frequency (SAF) curves over a region, which establishes a link between drought features with a chosen probability of recurrence (or return period) and the corresponding proportion of the area experiencing those drought conditions. While inferential analyses can be used to estimate these curves, analytical approaches offer a better understanding of the main statistical features that drive the spatial evolution of droughts. In this research, a technique is introduced to mathematically describe the Severity-Area-Frequency (SAF) curves, aiming to probabilistically understand the correlation between drought severity, measured through the SPEI index, and the proportion of the affected region. This approach enables the determination of the area's extent where SPEI values fall below a specific threshold, thus calculating the likelihood of observing SAF curves that exceed the observed one. The methodology is tested using data from the ERA5-Land reanalysis project, specifically studying the drought occurrences on Sicily Island, Italy, from 1950 to the present. Overall, findings highlight the improvements of incorporating the spatial interdependence of the assessed drought severity variable, offering a significant enhancement compared to the traditional approach for SAF curve derivation. Moreover, they validate the suitability of reanalysis data for regional drought analysis. [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]

Investigating the trends of reference evapotranspiration (ETo) is fundamental importance for water resource management in agriculture, climate variability analysis, and other hydroclimate-related projects. Moreover, it would be useful for understanding the sensitivity of such trends to basic meteorological variables, as the modifications of these variables due to climate change are more easily predictable. This study aims to analyze ETo trends and sensitivity in relation to different explanatory meteorological factors. The study used a 17 year-long dataset of meteorological variables from a station located in Piazza Armerina, Sicily, a region characterized by a Mediterranean climate. First, the FAO-Penman-Monteith method was applied for estimation of ETo. Next, the Mann-Kendall test with serial autocorrelation removal by Trend-free pre-whitening (TFPW) was applied to analyze ETo trends and the basic meteorological variables on which they depend. Sen's slope was also used to examine the magnitude of the trend of monthly ETo and its related meteorological variables. According to the obtained results, ETo only showed a downward trend of 0.790 mm per year in November, while no trend is shown in other months or on seasonal and annual time scales. Solar radiation (November and Autumn) and rainfall (Autumn) showed a downward trend. The other meteorological variables (minimum temperature, maximum temperature, mean temperature, wind speed, and relative humidity) showed an upward trend both at monthly and seasonally scale in the study area. The highest and lowest sensitivity coefficients of ETo in the study area are obtained for specific humidity and wind speed, respectively. Specific humidity and wind speed give the highest (44.59%) and lowest (0.9%) contribution to ETo trends in the study area. These results contribute to understanding the potential and possible future footprint of climate change on evapotranspiration in the study area. [ABSTRACT FROM AUTHOR]

A key component for landslide early warning systems (LEWSs) is constituted by thresholds providing the conditions above which a landslide can be triggered. Traditionally, thresholds based on rainfall characteristics have been proposed, but recently, the hydrometeorological approach, combining rainfall with soil moisture or catchment storage information, is becoming widespread. Most of the hydrometeorological thresholds proposed in the literature use the soil moisture from a single layer (i.e., depth or depth range). On the other hand, multi-layered soil moisture information can be measured or can be available from reanalysis projects as well as from hydrological models. Approaches using this multi-layered information are lacking, perhaps because of the need to keep the thresholds simple and two-dimensional. In this paper, we propose principal component analysis (PCA) as an approach for deriving two-dimensional hydrometeorological thresholds that use multi-layered soil moisture information. To perform a more objective assessment we also propose a piecewise linear equation for the identification of the threshold's shape, which is more flexible than traditional choices (e.g., power law or bilinear). Comparison of the receiver operating characteristic (ROC) (true skill statistic, TSS) of thresholds based on single- and multi-layered soil moisture information also provides a novel tool for identifying the significance of multi-layered information on landslide triggering in a given region. Results for Sicily island, considering the ERA5-Land reanalysis soil moisture data (available at four different depth layers), corroborate the advantages of the hydrometeorological approach gained in spite of the coarse spatial resolution and the limited accuracy of reanalysis data. Specifically, the TSS of traditional precipitation intensity–duration thresholds is equal to 0.5, while those of the proposed hydrometeorological thresholds is significantly higher (TSS=0.71). For the analyzed region, however, multi-layered information seems not to be relevant, as performances in terms of TSS are similar to those obtained with single-layer soil moisture at the upper depths, namely 0–7 and 7–28 cm, which can imply that in Sicily landslide phenomena are mainly influenced by soil moisture in most shallow soil layers. [ABSTRACT FROM AUTHOR]

Large-scale photovoltaic (PV) power plants may affect the hydrological cycle in all its components. Among the various components, evapotranspiration is one of the most important. As a preliminary step for assessing the impacts of PV plants on evapotranspiration, in this study, we performed an evaluation study of methods for estimating reference evapotranspiration (ETo). FAO and ASCE recommend the Penman–Monteith (PM) method for the estimation of ETo when the data for all involved variables are available. However, this is often not the case, and different empirical methods to estimate ETo, requiring mainly temperature data, need to be used. This study aimed at assessing the performance of different temperature- and radiation-based empirical ETo estimation methods against the standardized PM ETo method in an experimental photovoltaic power plant in Piazza Armerina, Sicily, Italy, where a meteorological station and a set of sensors for soil moisture were installed. The meteorological data were obtained from the Lab from July 2019 to end of January 2022. By taking the ETo estimations from the PM method as a benchmark, the study assessed the performance of various empirical methods. In particular, the following methods were considered: Hargreaves and Samani (HS), Baier and Robertson (BR), Priestley and Taylor (PT), Makkink (MKK), Turc (TUR), Thornthwaite (THN), Blaney and Criddle (BG), Ritchie (RT), and Jensen and Haise (JH) methods, using several performance metrics. The result showed that the PT is the best method, with a Nash–Sutcliffe efficiency (NSE) of 0.91. The second method in order of performance is HS, which, however, performs significantly worse than PT (NSE = 0.51); nevertheless, this is the best among methods using only temperature data. BG, TUR, and THN underestimate ETo, while MKK, BG, RT, and JH showed overestimation of ETo against the PM ETo estimation method. The PT and HS methods are thus the most reliable in the studied site. [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]

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 other variables to be easily added, like peak rainfall intensity, with a further performance improvement (TSS = 0.66). This may stimulate more research on the use of this powerful tool for deriving landslide early warning thresholds. [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 resource 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, the basic variables that determine meteorological drought). We then specifically focus on drought characteristics (duration, accumulated deficit, intensity, and return period) determined by the theory of runs at seasonal and annual timescales by comparison with high-density and high-quality ground-based observational datasets. In particular, the proposed methodology is applied to the Sicily and Calabria regions (southern Italy), where long historical precipitation and temperature series were recorded by the ground-based monitoring networks operated by the former Regional Hydrographic Offices, whose density is considerably greater than observational gridded datasets available at the European level, such as E-OBS or CRU-TS. Results show that among the more skilful models able to reproduce, overall, precipitation and temperature variability as well as drought characteristics, many are based on the CLM-Community RCM, particularly in combination with the HadGEM2 global circulation model (GCM). Nevertheless, the ranking of the models may slightly change depending 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 and can serve as an aid for selecting the most suitable climate model for assessing climate change impacts on drought processes and the underlying variables. [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]

Summary Since the mid-90s the Standardized Precipitation Index (SPI) has found widespread use to monitor drought periods at different time scales. Recently, some efforts have been made to analyze the role of SPI for drought forecasting, as well as to estimate transition probabilities between SPI drought classes. In the present paper probabilistic models for short and middle term forecasting of SPI drought class transition probabilities are presented and extended in order to include information provided by an exogenous variable, such as an index of large scale atmospheric circulation pattern like, for instance, the North Atlantic Oscillation index (NAO). In particular, the proposed models result from evaluating conditional probability of future SPI classes with respect to current SPI (and NAO) classes or current SPI (and NAO) values, under the hypothesis of multivariate normal distribution of the underlying joint variables. SPI series are computed on average areal precipitation in Sicily region (Italy). As a significant negative correlation exists between NAO and SPI series in Sicily during recent decades, the proposed models are calibrated on the period from 1979 to 2008. Both SPI and NAO values are categorized in four classes. Transition probabilities to future SPI classes are evaluated based on SPI and NAO current classes or values and compared to the corresponding probabilities when NAO is neglected. Results indicate that drought transition probabilities in Sicily are generally affected by NAO index. In particular, transition probabilities related to persisting or worsening drought conditions significantly increase as NAO index tends toward extremely positive values. On the other hand transition probabilities to a less severe drought class decrease as NAO values increase. Furthermore, application of a simple score approach to quantitatively assess the skill in forecasting of the proposed models shows that assessing transition probabilities to future SPI classes from current SPI and NAO values leads to better results than considering current classes. [ABSTRACT FROM AUTHOR]

Drought is a natural phenomenon that presents spatial and temporal features whose knowledge is fundamental for an appropriate water resources management. In particular, the assessment of probabilities and return periods of areal extent of droughts of different severities over a region can provide useful information for planning drought management. In this study, an analytical methodology to characterize probabilistically the relationship between meteorological drought severity (computed in terms of Standardized Precipitation Index, SPI) and areal extent, expressed as Drought severity-Area-Frequency (SAF) curves, is proposed. In particular, analytical expressions of SAF curves describing the proportion of the total area of the region of interest where the SPI values are below a fixed threshold are derived. The developed curves enable to characterize a given drought event in a region, by computing the probability of occurrence of SAF curves exceeding the one observed. The proposed methodology is validated through the investigation of the spatio-temporal features of drought occurrences over Sicily, Italy, for the period 1921-2005. [ABSTRACT FROM AUTHOR]

In recent years, several drought monitoring indexes have found application to describe and compare droughts among different time periods and regions as well as to forecast the evolution of ongoing droughts, in order to select appropriate mitigation measures and policies for water resources management under shortage risk conditions. However, limited efforts have been made to investigate the possibilities of using information conveyed by large-scale climatic indexes to improve the forecasting ability of drought indexes, provided they exert some influence on the climatic variability in a region. The aim of the paper is to develop models for forecasting Palmer Hydrological Drought Index series in Sicily (Italy) based on artificial neural networks, and to extend such models in order to include information from large-scale climatic indexes. First, the influence of North Atlantic Oscillation (NAO) and European Blocking (EB) indexes on Palmer index series, computed on areal monthly precipitation from 1955 until 1999 in Sicily, has been investigated by means of a correlation analysis. Results indicate that NAO and EB series are significantly correlated with Palmer index series for winter and autumn months, with special reference to the last decades. Then, forecasting models based on neural networks have been developed, using different approaches. The comparison between the prediction for winter and autumn months obtained by either including or not including the NAO and EB indexes within the forecasting model indicates some improvements in terms of R2 when NAO and especially EB are considered. A different model behavior has been observed for the spring and summer predictions for which no significant improvements in terms of model predictive capability, due to the introduction of the climatic indexes as input variables, have been observed. [ABSTRACT FROM AUTHOR]

The assessment of water resources in a region usually must cope with a general lack of data, both in time (short observed series) as well as in space (ungauged basins). Such a lack of data is generally overcome by combining rainfall-runoff models with regionalization techniques in order to transfer information to sites without or with short available observed series. The present paper aims to analyze applicability and limitations of two regionalization procedures for estimating the parameters of simple rainfall-runoff models respectively based on a "two-step" and on a "one-step" approach, for the estimation of monthly streamflow series in ungauged basins. In particular, an application to a Sicilian river basin of multiple regression equations according to a "two-step" and a "one-step" approaches and of a "one-step" approach based on neural networks is reported. For the investigated region, results indicate that models based on the "one-step" approach appear to be robust and adequate for estimating the streamflows in ungauged basins. [ABSTRACT FROM AUTHOR]

The proactive approach to drought management is based on measures devised and implemented before, during, and after the drought event, according to a planning strategy rather than within an emergency framework. The measures taken before the initiation of a drought event consist of long-term measures oriented to improve the reliability of the water supply system to meet future demands under drought conditions. The measures taken after a drought is forecasted or starts are short-term measures that try to mitigate the impacts of the particular drought on the basis of a contingency plan. Selection of the preferable mix of long-term and short-term measures can be accomplished through multicriteria decision analysis (MCDA), which enables the comparison of alternatives on the basis of appropriate quantitative and/or qualitative assessment criteria. In this paper, a methodology for the assessment of drought mitigation measures based on a combined use of simulation models and MCDA is applied to a water supply system located in eastern Sicily, Italy. The system comprises two reservoirs and several diversion dams and hydropower plants, and its main uses are irrigation and municipal water supply. First, a simulation model is applied to evaluate the effects of several drought mitigation alternatives consisting of a mix of long- and short-term measures. Then, MCDA is applied to rank the different alternatives on the basis of economic, environmental, and social criteria, taking into account the process of coalition formation among stakeholders on the basis of their different point of view. The results confirm the applicability of the proposed multicriteria methodology for a transparent comparison of drought mitigation measures to be adopted as a support for the decision making process. [ABSTRACT FROM AUTHOR]

The objective of the study is to assess the presence of linear and non linear trends in annual maximum rainfall series of different durations observed in Sicily. In particular, annual maximum rainfall series with at least 50 years of records starting from the 1920's are selected, and for each duration (1, 3, 6, 12 and 24 h) the Student's t test and the Mann-Kendall test, respectively, for linear and non linear trend detection, are applied also by means of bootstrap techniques. The effect of trend on the assessment of the return period of a critical event is also analysed. In particular, return periods related to a storm, recently occurred along the East Coast of Sicily, are computed by estimating parameters based on several sub-series extracted from the whole observation period. Such return period estimates are also compared with confidence intervals computed by bootstrap. Results indicate that for shorter durations, the investigated series generally exhibit increasing trends while as longer durations are considered, more and more series exhibit decreasing trends. [ABSTRACT FROM AUTHOR]

An analysis of drought in Sicily from 1926 to 1996 is presented. In identifying drought over the region, both the NCEP/NCAR reanalysis precipitation data and those observed in 43 gauges, located quite uniformly over the territory of the Island, are used. Drought occurrence is estimated by means of the Standardized Precipitation Index (SPI). To study long-term drought variability, a Principal Component Analysis was applied to the SPI field. Results suggest that the entire Island is characterised by a drought variability with a multi-year fluctuations and a tendency towards drier periods from the seventies onward. A preliminary comparison between results obtained using the meteorological large-scale analysis and that derived from actual observations on the ground shows a general good agreement, although further efforts are needed to get a better downscaling of the large-scale precipitation fields. Furthermore, by applying orthogonal rotations to the principal component patterns, it has been found that three distinct areas having coherent climatic variability may be identified. Finally, the sensitivity of the SPI values on the calibration period is also investigated. [ABSTRACT FROM AUTHOR]

Intensive urbanization and related increase of impervious surfaces, causes negative impacts on the hydrological cycle, amplifying the risk of urban floods. These impacts can get even worse due to potential climate change impacts. The urban areas of the Simeto River Valley (SRV), the largest river valley in Sicily (Italy), have been repeatedly hit by intense rainfall events in the last decades that lead to urban flooding, causing several damages and, in some instances, threats to population. In this paper, we present the results of a 10-question survey on climate change and risk perception in 11 municipalities of the SRV carried out within the activities of the LIFE project SimetoRES, which allowed to collect 1143 feedbacks from the residents. The survey investigated: (a) the level of worry about climate change in relation to extreme storms, (b) elements of urban flooding risk preparedness: the direct experience of the residents during heavy rain events, their trust in a civil protection regional alert system, and their knowledge of the correct behavior in case of flood, and (c) the willingness of citizens to implement sustainable drainage actions for climate change adaptation in their own municipality and real estates. The results show that more than 52% of citizens has inadequate knowledge of the correct behavior during flooding events and only 30% of them feel responsible for mitigation of flooding risk. There is a modest willingness by the population to support the construction of sustainable urban drainage infrastructures. A statistical cross-analysis of the answers to the different questions, based on contingency matrices and conditional frequencies, has shown that a greater worry about climate change has no significant impact either on the behavior of people in dangerous situations occurring during flooding events or on the willingness to support financially sustainable solutions. These results suggest that to build a higher worry about climate change and related urban flooding risk is not sufficient to have better preparedness, and that more direct educative actions are necessary in the area. [ABSTRACT FROM AUTHOR]

Climate change induced by greenhouse gas emissions is expected to alter the natural availability of water, affecting domestic, agricultural and industrial uses. This work aims at assessing the possible future impacts of climate change on precipitation, temperature and runoff, and to simulate the effects on reservoir demand–performance curves. To this aim, a modeling chain is set up, based on the combined use of regional climate models (RCMs) and water supply system simulation models. The methodology is applied to the Pozzillo reservoir, located in Sicily (Italy), which has experienced several droughts in the past. We use an RCM model that, based on a previous study, has proved to be the most reliable in the area, among those of the EURO-CORDEX initiative. RCM precipitation and temperature monthly time series are used to generate future reservoir inflow data, according to two representative concentration pathways, RCP4.5 (intermediate emissions scenario) and RCP8.5 (high emissions scenario) and a two-step bias correction procedure. Simulation of the reservoir indicated that, due to reservoir inflow reduction induced by climate change, performances of the Pozzillo reservoir are predicted to decrease significantly in the future, with impacts of RCP8.5 generally higher than RCP4.5. [ABSTRACT FROM AUTHOR]

Abstract: This paper shows an application of copulas to the probabilistic analysis of drought characteristics. Drought occurrences are analyzed by the Standardized Precipitation Index (SPI) computed on the mean areal precipitation, aggregated at 6months, observed in Sicily between 1921 and 2003. Assuming a drought period as a consecutive number of intervals where SPI values are less than −1, several characteristics are determined, namely: drought length, mean and minimum SPI values, and drought mean areal extent. Results of a preliminary analysis based on Kendall’s correlation and upper tail dependence coefficient, computed on observed and resampled data, show significant dependence properties between almost all the considered pairs. The four-dimensional joint distribution required to correctly model the stochastic structure of variables is determined by resorting to copula approach. This allows flexibility in choosing suitable marginals and dependence structure, and in simplifying the inference procedure as well. Drought return periods are then computed as mean interarrival time, taking into account two drought characteristics at a time by means of the corresponding bivariate marginals of the fitted four-dimensional distribution. Application of the proposed methodology to Sicilian precipitation series shows a good correspondence between empirical and theoretical joint return periods, thus indicating that copulas are adequate to jointly model drought characteristics and to compute exceedance probabilities of drought events. [Copyright &y& Elsevier]

Summary: A procedure for quality control of daily rainfall, designed to automatically detect erroneous data to be submitted for further manual controls, is herein described. Quality control of daily rainfall data is based on confidence intervals derived by means of neural networks on the basis of contemporaneous data observed at reference stations, since the presence of zero values in the series and the strong variability of precipitation at daily time scale do not allow reliable confidence intervals to be estimated from historical data from the same station. Application of the proposed procedure to automatic stations in Sicily (Italy), enables validation of more than 80% of the data. The accuracy of the procedures is verified by introducing known errors into the available datasets, supposed as correct, and by computing the probabilities of correctly classifying data as validated or not validated. [Copyright &y& Elsevier]

Many recent studies indicate climate change as a phenomenon that can potentially alter the water cycle, also implying new challenges in drought risk management. Impacts of drought are difficult to study, as this phenomenon is usually characterized by multiannual evolution scales, meaning that few occurrences may take place in a climatologic period of 30-years. Hence, it is of key importance to assess the quality of Regional climate models (RCMs), which are commonly used for assessing future impacts of climate change on hydrological events. To this end, a common approach consists in evaluating models' capability to simulate historic climate by comparing models' simulations to observational gridded datasets, available at global or continental spatial scales. Regardless of the specific methods used to assess the differences between simulated and observed data, a main limitation in this approach is that the considered spatial resolution is too coarse for reliable climate change impact studies at hydrologically relevant scales, especially in areas of complex topography. From this point of view, large scale observational gridded datasets are of poor applicability, since they are built upon low-density hydro-meteorological networks.The Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative provides climate data simulated by RCMs developed by several research centers all over the world. In the present study we use climate data for the Euro-Mediterranean area, known as EURO-CORDEX (www.euro-cordex.net). In particular, simulations for an historic reference period (baseline) and for future projections up to 2100, with a 12.5 km grid resolution, are available for four different greenhouse gas concentration trajectories (Representative Concentration Pathways). We propose a statistical methodological framework to assess the quality of the EURO-CORDEX RCMs with respect to their ability to simulate historic climate by a comparison with high-density and high-quality ground-based observational datasets. In particular, the focus of this study is on observed thermometric and pluviometric regimes in Southern Italy at time scales from monthly to annual, which are particularly useful for drought analyses. To this aim, performances indicators relevant for assessing the accuracy of assessments are also considered. The proposed methodology is applied to Sicily and Calabria regions, where long historical precipitation and temperature series have been recorded by the ground-based networks operated by the formerly Regional Hydrographic Offices, whose density is considerably greater than observational gridded datasets available at European level, such as E-OBS. Results show that the proposed methodology is capable to highlight clearly skills and weaknesses of different RCMs and can help to properly select the models for climate projections in the investigated areas, with respect to the examined hydrologic processes. [ABSTRACT FROM AUTHOR]