Search

In order to correctly detect climate signals and discard possible instrumentation errors, establishing coherent data records has become increasingly relevant. However, since real measurements can be inhomogeneous, their use for assessing homogenization techniques is not directly possible, and the study of their performance must be done on homogeneous datasets subjected to controlled, artificial inhomogeneities. In this paper, considering two European temperature networks over the 1950–2005 period, up to 7 artificial breaks and an average of 107 missing data per station were introduced, in order to determine that mean square error, absolute bias and factor of exceedance can be meaningfully used to validate the best-performing homogenization technique. Three techniques were used, ACMANT and two versions of HOMER: the standard, automated setup mode and a manual setup. Results showed that the HOMER techniques performed better regarding the factor of exceedance, while ACMANT was best with regard to absolute error and root mean square error. Regardless of the technique used, it was also established that homogenization quality anti-correlated meaningfully to the number of breaks. On the other hand, as missing data are almost always replaced in the two HOMER techniques, only ACMANT performance is significantly, negatively affected by the amount of missing data. [ABSTRACT FROM AUTHOR]

In this study, the problem of determining, with an adequate statistical significance, the relationship between teleconnection pattern indices and precipitation at climatological time scales has been addressed through a novel technique based on a rainfall zonation, by means of creating a regional precipitation database for a whole‐region correlation approach. Pearson correlation was performed to evaluate the mesoscale influence on precipitation in the Calabria region over the 1951–2010 time period, by means of a database of 79 rain gauges, divided in five Rainfall Zones (RZs) and seven teleconnection pattern indices relevant to the Mediterranean region, searching where results were significant (significance level < 0.05) and with an absolute correlation value higher than a prefixed threshold equal to 0.2. The Calabria region was chosen as it is located in the centre of the Mediterranean area, which constitutes a hot spot for climate change, and because it is equipped with a high‐density, long‐time series of precipitation gauge network, recently validated and homogenized. Correlation analysis between seasonal teleconnection indices and seasonal cumulated precipitation showed that the Western Mediterranean Oscillation and the East Atlantic/West Russian patterns were the most relevant teleconnections over all Calabria. Correlations of 3‐month averaged teleconnection indices versus monthly precipitation showed that the Mediterranean and Western Mediterranean Oscillations produce most significant results with correlation values higher than 0.2, with East Atlantic pattern a third close. Comparison between monthly teleconnection indices and monthly cumulated precipitation indicate that all modes of variability taken into account share a similarly weak correlation. Comparing the rainfall zone‐based study and the technique of averaging individual stations results post‐correlation, it was shown that on average only 43% of the stations would produce useful correlations, while the novel technique used all the valid and available station data, resulting in statistically more robust findings. [ABSTRACT FROM AUTHOR]

Recent studies evidenced that extreme daily rainfall often increases in spite of a decrease of the total rainfall. For this reason, the analysis of daily precipitation concentration is an important issue in climate research and can be useful to evaluate risks linked to extreme rainfall events. In this paper, an analysis of the spatial and temporal variability of the daily precipitation concentration in the Sardinia region, the second largest island in the Mediterranean Sea, has been performed for the period 1922–2011 by applying the Concentration Index. First, the CI has been evaluated at annual and seasonal scale. Then, the coefficient of variation, and the 5th and 95th percentiles of the annual CI values were calculated and mapped as a set of descriptors of variability and extreme characterization of precipitation concentration across the study area. Finally, in order to evaluate the temporal evolution of the CI, a trend analysis has been also performed using the Mann–Kendall test. Results show CI values ranging from a minimum of 0.477 to a maximum of 0.723. Spatially, the region can be divided in two parts with opposite behaviours: the western side characterized by a more uniform temporal distribution of rainfall intensity and the eastern side with the most critical intensity and aggressiveness of rainfall. From a seasonal point of view, this spatial gradient was evidenced in winter, spring and autumn while summer showed a more homogeneous spatial distribution of CI. The spatial distribution of the coefficient of variation did not show any spatial gradient between the two sides of the region, both at annual and seasonal scale. As regards the trend analysis, a marked positive trend of the CI has been detected, thus indicating a tendency towards more intense and aggressive rainfall. Copyright © 2019 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Calabria (Southern Italy) is one of the Italian regions most affected by desertification phenomena. This study presents a detailed analysis of the sensitivity to desertification of an economically important agricultural area in the province of Crotone. The Environmentally Sensitive Areas methodology, developed during the European Union MEDALUS project, has been applied by means of a consistent set of pedological, vegetation, climatic and management data of the study area. Analysis of 15 biophysical and social-economic indicators and an evaluation of 4 Quality Indices allowed the classification of the study area into potential, fragile and critical areas in relation to desertification. The main result is a pronounced sensitivity to desertification of the area: about 46% of the land emerges as being already affected by degradation phenomena, falling within the worst class (‘critical’), while about 39% of the area belongs to the ‘fragile’ class. [ABSTRACT FROM PUBLISHER]

In order to investigate the behaviour of climatic and hydrological variables, several statistical and stochastic techniques are currently applied to time series. In the present study a statistical analysis of annual and seasonal precipitation has been performed over 109 cumulated rainfall series with more than 50 years of data observed in a region of Southern Italy (Calabria). Trend analyses have been made by using both nonparametric (Mann-Kendall test) and parametric (linear regression analysis) procedures. The long historical series of monthly rainfall data employed in this work have been previously processed through a pre-whitening (PW) technique in order to reduce the autocorrelation of rainfall series and its effects on outcomes of trend detection. The application of the above mentioned procedures has shown a decreasing trend for annual and winter-autumn rainfall and an increasing trend for summer precipitation. Moreover the Mann-Whitney test has been used to evidence the possible change points in the data. The higher percentages of rainfall series show possible year changes during decade 1960-1970 for almost all of the temporal aggregation rainfall. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The spatial distribution of precipitation in Xinjiang, China, is extremely heterogeneous due to its complex subsurface, and microphysical studies of cloud precipitation over Xinjiang are severely constrained by observational means. The Global Precipitation Measurement (GPM) dual‐frequency precipitation radar (DPR) provides improved three‐dimensional observation data of strong and weak precipitation and performs effective observations in alpine mountain regions. Therefore, this study used GPM/DPR Level‐3 data from January 2015 to December 2022 to analyze the convective and stratiform precipitation characteristics over Xinjiang. The results showed that (1) the range of maximum precipitation reflectivity factors (PRF_max) near the ground and at different heights were larger in summer and maximum PRF_max values appeared in areas along the Altai, Tianshan, and Kunlun Mountains and plains on the respective mountain slopes. In summer, the PRF_max of stratiform (convective) precipitation nearsurface ranged from 24 to 34 dBZ (28–40 dBZ). (2) The two types of precipitation exhibited the highest storm top heights in summer, with convective precipitation having higher values than stratiform precipitation and mountainous areas having higher values than the surrounding basins due to the effects of topographic uplift. (3) The value domains of the PRF, particle mass‐weighted diameter (Dm), and particle number concentration for stratiform (convective) precipitation were more concentrated (wider), that is, PRF: 14–30 dBZ (12–40 dBZ), Dm: 0.9–1.4 mm (0.7–2.1 mm), and the particle concentration: 29–35 (24–39). The height of the center of the probability density of each variable was higher in the three major mountain ranges than in the two major basins owing to the influence of topography. PRF increased and then decreased with height. Convective raindrops were in an active state of high concentration and exhibited a large scale in the lower layers, whereas the particle spectral parameters of stratiform precipitation showed a more concentrated and symmetric distribution and insignificant changes with height. [ABSTRACT FROM AUTHOR]

Skills in reproducing monthly rainfall over Calabria (southern Italy) have been validated for the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) satellite data, the E-OBS dataset and 13 Global Climate Model-Regional Climate Model (GCM-RCM) combinations, belonging to the ENSEMBLES project output set. To this aim, 73 rainfall series for the period 1951–1980 and 79 series for the period 1981–2010 have been selected from the database managed by Multi-Risk Functional Centre of the Regional Agency for Environmental Protection (Regione Calabria). The relative mean and standard deviation errors, and the Pearson correlation coefficient have been used as validation metrics. Results showed that CHIRPS satellite data (available only for the 1981–2010 validation period) and RCMs based on the ECHAM5 Global Climate performed better both in mean error and standard deviation error compared to other datasets. Moreover, a slight appreciable improvement in performance for all ECHAM5-based models and for the E-OBS dataset has been observed in the 1981–2010 time-period. The whole validation-and-assessment procedure applied in this work is general and easily applicable where ground data and gridded data are available. This procedure might help scientists and policy makers to select among available datasets those best suited for further applications, even in regions with complex orography and an inadequate amount of representative stations. [ABSTRACT FROM AUTHOR]

The use of validated gridded data in assessment procedures is of fundamental importance, as it allows to calculate trends and changes not only at locations equipped with meteorological stations, but to whole regions. Using validated data can help in developing a more precise risk assessment especially in regions with a complex orography and sub-regional scale microclimates. In this study, we have used the CHIRPS dataset, an observation-validated satellite dataset, to assess extreme precipitation in the eight alert areas of Calabria (southern Italy). CHIRPS is a quasi-global gridded rainfall dataset, spanning 50°S-50°N at all longitudes, incorporating 0.05° resolution satellite imagery with in-situ station data starting in 1981 (Funk et al., 2015).The Calabria region has been selected for this assessment study, as it is a challenging region with complex orography, and with a high number of rain stations, used in previous gridded data validation studies. The CHIRPS dataset was validated for Calabria for the 1981-2010 time period by means of the registrations of 79 rain gauges of the Multi-Risk Functional Centre of the Regional Agency for Environmental Protection. Metrics used were the mean error and standard deviation (Caroletti and Coscarelli, 2019). The alert areas were defined according to the Directive "Sistema di Allertamento regionale per il rischio meteoidrogeologico ed idraulico in Calabria" (Resolution of Regione Calabria Government n. 535 of November15th, 2017). Four areas are located on the western side of the region, and four on the eastern side.We investigated the changes in the number of extreme events and the values of extreme precipitation in each area in the 1981-2017 time period. We considered extreme all precipitation events that occurred over the 99th percentile threshold for the study period.By using 5-years running means of precipitation at each satellite data cell, CHIRPS showed an increase in the magnitude of extreme events for seven of the eight alert areas. The increase is especially strong in four areas (the three southernmost eastern alert areas and the Lamezia Terme area in the west).The number of extreme precipitation events, which also shows a growing trend, in contrast with the decrease of precipitation days overall, can explain the slight growing trend in yearly precipitation for these same areas over the 1981-2017 time period. [ABSTRACT FROM AUTHOR]

Validation studies are of major importance in science, and climate data are no exception to the rule. Metrics are the common tools used to assess data accuracy; it is generally not possible to define generalized metrics, but they must be tailored on the specific use of the evaluated dataset, on the variable used and on the regions of interest.Calabria (southern Italy) is a challenging area for rainfall studies, because of its complex orography and high vulnerability to climate-change due to its position at the southernmost tip of Italy and in the center of the Mediterranean basin. In this study, we have validated the skills in reproducing rainfall over Calabria of the CHIRPS satellite data, of the E-OBS dataset and of 14 GCM-RCM combined models belonging to the ENSEMBLES project set.Rainfall data have been validated for the 1951-2010 time period by means of the registrations of 79 rain gauges of the Multi-Risk Functional Centre of the Regional Agency for Environmental Protection (Regione Calabria). Metrics used were the mean error and standard deviation. The results have shown that the skills of E-OBS, of most ENSEMBLES models, and of CHIRPS (whose data are avilable only for the 1981-2010 validation period) are very similar. RCMs based on the ECHAM5 Global Climate Model showed slightly better results than both reanalysis and satellite data, both in mean error and standard deviation error. There was no appreciable change in performance for any of the evaluated datasets between the 1951-1980 and the 1981-2010 time periods.The whole validation-and-assessment procedure, used in this work, is general and easily applicable to any other region where ground data and gridded data are available. The procedure could be applied to develop a more precise risk assessment in those regions with a complex orography and where there is an inadequate amount of representative stations. This validation procedure could be a useful tool in support of policy makers and scientists in decisions related to precipitation risks. [ABSTRACT FROM AUTHOR]

Several studies evidenced the importance of the knowledge of the bioclimatic comfort for improving people's quality of life. Temperature and relative humidity are the main variables related to climatic comfort/discomfort, influencing the environmental stress in the human body. In this study, a stochastic approach is proposed for characterizing the bioclimatic conditions through the Humidex values in six sites of Calabria (southern Italy), a region often hit by heat waves in summer months. The stochastic approach is essential, because the available time series of temperature and relative humidity are not long enough and present several missing values. The model allowed the characterization of sequences of extreme values of the Humidex. Results showed different behaviours between inner and coastal stations. For example, a sequence of 20 consecutive days with maximum daily Humidex values greater than 35 has a return period ranging from 10 to 20 years for the inner stations, while it exceeds 100 years for the coastal ones. The maximum yearly Humidex values for the inner stations have a larger range (40–50) than the coastal ones (38–45), reaching higher occurrence probabilities of serious danger conditions. Besides, the different influence of temperature and relative humidity on the Humidex behaviour has been evidenced. [ABSTRACT FROM AUTHOR]

Earth observation data are useful to analyze the impact of climate-related variables on geomorphological processes. This work aims at evaluating the impact of rainfall on slow-moving landslides, by means of a quantitative procedure for identifying satellite-based displacement clusters, comparing them with rainfall series, and applying statistical tests to evaluate their relationships at the regional scale. The chosen study area is the Basento catchment in the Basilicata region (southern Italy). Rainfall series are gathered from rain gauges and are analyzed to evaluate the presence of temporal trends. Ground displacements are obtained by applying the P-SBAS (Parallel Small BAseline Subset) to three datasets of Sentinel-1 images: T146 ascending orbit, and T51 and T124 descending orbits, for the period 2015–2020. The displacement series of the pixels located in areas mapped as landslides by the Italian Landslide Inventory and sited within rain gauge influence regions (defined as 10 km circular buffers) are studied. Those displacement series are analyzed and compared to the rainfall series to search for correlations, by employing statistical and non-parametric tests. In particular, two landslides are selected and investigated in detail. Significant results were obtained for the T124 descending orbit for both landslides, for a 3-day cumulative rainfall and a 7-day delay of the slope response. Challenges in the whole procedure are highlighted and possible solutions to overcome the raised problems are proposed. Given the replicability of the proposed quantitative procedure it might be applied to any study area. [ABSTRACT FROM AUTHOR]

This paper describes the activities conducted for the development of a thematic map of shallow landslides (and other landforms) resulting from by consecutive rainfall events during the fall-winter seasons of 2008/2009 and 2009/2010 in the Catanzaro Strait (Central Calabria, South Italy). The methodology is based on the interpretation of Google Earth high-resolution satellite images (post-event images are dated 19th March 2010), followed by processing and management of the collected data through a geographic information system. The inventory covers a wide area (692 km2) and includes 3483 shallow landslides and many other geomorphological features (zones of incipient shallow landslides and areas affected by soil erosion) related to the 2008–2010 rainfall events. The map produced may provide useful data to define shallow landslide events, develop landscape evolution models and characterise active geomorphic processes. Furthermore, the map may also be used as a basic tool to produce landslide or erosion susceptibility maps, which can be used for land-use planning strategies and landslide hazard analyses. [ABSTRACT FROM AUTHOR]

In the last decades, building damage caused by landslides in urban areas has increased due to the rapid growth of urbanization even on landslide-prone slopes. This work presents the analysis results of landslide damage evolution by means of a macrodamage index in relation to the characteristics of landslides. The study area is located in southern Italy, and is affected by slow-moving landslides, which, over time, have caused relevant effects on the buildings. A series of geological and geomorphological studies and field surveys, supported by monitoring via conventional geotechnical techniques, allowed us to classify four categories of landslides based on the following criteria: landslide movement, type of material and estimated depth. Detailed field surveys aimed at evaluating distribution and evolution of the damage to buildings located within and near the unstable area were carried out in 2005 and 2011. The evaluation of the macrodamage index in two different years enabled us to directly correlate the features of the landslide categories and their effects on the urban fabric. [ABSTRACT FROM AUTHOR]

The present study aims at improving data availability and quality for the last 80-90 years for daily precipitation in the Calabria region (southern Italy). First, the original database was homogenised and the gaps filled in for 129 daily rain gauges for the 1916-2006 period. Then, precipitation variability and change were evaluated at an adequate spatial resolution. Monthly and annual total precipitation (P), number of wet days (WDs), and precipitation intensity (PI) were calculated for each series. With regard to the monthly total precipitation a general negative trend, albeit not everywhere significant, was detected, in particular for the autumn-winter period, while in summer the tendency was toward an increase in total precipitation. The monthly behaviour of WDs was not very different from that observed for P: a diffuse negative trend was detected in most months, particularly evident and significant in January, with the exception of April and the summer months, for which the tendency was toward an increase. Regarding the PI, a general negative and often significant trend was found for the entire region and for almost all the months, except summer. Attention was also focused on tendencies in the different PI categories, revealing negative trends in high-intensity categories, especially coming from the winter season. Finally, running trend analysis revealed that the previously discussed tendencies were not persistent throughout the series length, but depended on the period examined. This important aspect should be taken into account when different results based on different time windows are compared. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Mean annual precipitation variability at decadal time scale over a southern Italy area (Calabria) was investigated to quantify the spatial and temporal patterns. A multivariate approach has been applied to analyse spatial-temporal (1921-2000) data of annual precipitation. A nested isotropic linear model of coregionalization was fitted to the experimental variograms including three basic structures: a nugget effect, an exponential model and a spherical model. The correlation structure between the decades was analysed by applying principal component analysis at each spatial scale, and specific factors at each characteristic scale were cokriged and mapped. Two components were identified: the first is mainly linked to local factors, which could be identified by the orographic characteristics of the region, while the long range component could be related to large-scale factors, like for example the processes of the global atmospheric circulation. In addition, in order to obtain a better insight into the pattern of relatively dry or wet zones, a standardized relative difference was calculated. The results showed that the annual precipitation decreased during the period 1921-2000 and the spatial distribution changed with an increase of dry areas. [ABSTRACT FROM AUTHOR]

In the framework of the project "Integrated approach for the development across Europe of user oriented climate indicators for GFCS high-priority sectors: agriculture, disaster risk reduction, energy, health, water and tourism" (INDECIS 2017–2020), around 610K climate station-based observations were rescued over European regions for the main climate variables (maximum and minimum temperature, rainfall, sunshine duration and snow depth) along the 20th century at daily scale. Rescued data will constitute, together with other gathered regional datasets, the INDECIS-Raw-Dataset, which will expand current European data coverage contained in the European Climate Assessment & Dataset (ECA&D). An extensive examination of the ECA&D dataset was conducted to find spatial-temporal data gaps or stations with low percentage of daily data as prior candidates for data recovery in European regions. This exercise led us to focus our efforts on the Central European region and the Balkans. Digitizing was carried out by using a rigorous "key as you see" method, meaning that the digitizers type the values provided by data images, rather than using any coding system. Digitizers carefully cross-checked the typed values against original sources for the 10th, 20th and 30th day of each month to make sure that no days were skipped or repeated during the digitizing process. Monthly totals and statistical summaries were computed from transcribed data and were compared with monthly totals and summaries provided by data sources to check accuracy as preliminary quality control. The digitizing method and the quality control of the digitizing process applied in this study ensured an accurate data transcription according to the obtained statistics. [ABSTRACT FROM AUTHOR]

Slope failures are the result of various predisposing factors (geo-structural and morphological conditions, topography, geotechnical characteristics, etc.). In an ordinary phase, a typical slope stability analysis includes the identification of factors that can trigger a slope failure, its mechanisms, the modelling of stability conditions and their assessment during critical situations. To define the predisposing and triggering factors, integrated monitoring represents an essential and powerful tool. In this paper, referring to the case study of a medium-deep landslide that occurred in Calabria (Southern Italy) during the winter of 2009–2010, a method and means of defining an emergency geotechnical model ( smart geotechnical model ) using a geological model are proposed. The definition of both models considers the resources that may be obtained in an emergency and the short time available to achieve the objectives (protection of public and private safety, restoration of normal conditions, etc.). The proposed method allows the orderly and systematic acquisition, under emergency conditions, of data that are useful for the management of a particular situation and for defining an initial cognitive state of the problem. These elements can be particularly effective in both emergency (to manage risk using progressively quantitative knowledge) and ordinary conditions to plan, design, realize and manage definitive measures for risk adaptation, mitigation and reduction. Moreover, the same knowledge can provide useful references to typify landslides that occur in similar geo-environmental contexts. [ABSTRACT FROM AUTHOR]

The INDECIS project (see www.indecis.eu) includes 16 institutions from 12 countries and intends to maximize the benefits achievable from the use of observational data across Europe to develop climate indicators and climate services useful to assess the effects of climate variability, including extreme events, and climate change over socioeconomic systems. INDECIS is starting its second year of work and has already produced its first results. The project contributed to the ECA&D dataset by rescuing around 610K meteorological station-based daily observations from the Balkans and Central Europe for the different essential variables (maximum and minimum temperature, rainfall, sunshine duration and snow depth) along the 20th century at daily scale. (https://doi.pangaea.de/10.1594/PANGAEA.896957). In addition, 339 climate series (rainfall and temperature) were gathered from Italy and a Global Dataset with 311 wind tall mast data was also recovered. Next to DARE activities, the focus moved to ensure data quality and homogeneity. First, three Quality Control (QC) software suites have been created and are available to use as beta versions (http://www.indecis.eu/software.php): the INQC software and the MetQC software, for the quality control of ECA&D daily data; and the QCSS4TT software built specifically for tall masts wind data. INDECIS has also created a set of daily benchmarks for testing quality control and homogenization methods and software. Two benchmarks are available from the project's website (http://www.indecis.eu/benchmarking.php). One of this Benchmark is based on KNMI Climate Regional model (RACMO) and a Real-Data based Benchmark is created by combining close by stations to simulate real relocations. INDECIS has also produced a list of 136 climate indices and a software suite to compute them(http://www.indecis.eu/indices.php). A list of indicators related to circulation variability have been catalogued to inspect their relations with the computed indices and a new software suite for datasets inter-comparison is under development to examine the strengths and weaknesses of different gridded, reanalyzed and modeled datasets. INDECIS is now engaging with potential users to promote a series of workshops to present and co-develop products and services.In the presentation, an overview of INDECIS activities and results are given.The Project INDECIS started in September 2017 and will last 36 months. It is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). [ABSTRACT FROM AUTHOR]