Tommaso Caloiero

Influence of the North Atlantic Oscillation on winter rainfall in Calabria (southern Italy)

Seasonal rainfall amounts, directly responsible for availability of water resources on a specified area, are strongly dependent on the climate system. In order to highlight some features of such dependence, generally circulation indexes based on the difference in the sea level pressure between two geographic areas are taken into account. In the present study, the relationships between winter rainfall series observed in the Calabria region (southern Italy) and the North Atlantic Oscillation Index (NAOI) have been analysed. Firstly, a correlation analysis between precipitation and the NAOI was performed. Subsequently, the influence of the different phases of the NAO on the winter precipitation has been detected by a composite analysis, and by identifying changes in the behaviour of the probability density functions (gamma distribution) fitted on monthly rainfall. The results evidence a clear link existing between the phases of the climatic index and the amount of winter rainfall.

Adaptation to flood risk - results of international paired flood event studies

As flood impacts are increasing in large parts of the world, understanding the primary drivers of changes in risk is essential for effective adaptation. To gain more knowledge on the basis of empirical case studies, we analyze eight paired floods, that is, consecutive flood events that occurred in the same region, with the second flood causing significantly lower damage. These success stories of risk reduction were selected across different socioeconomic and hydro-climatic contexts. The potential of societies to adapt is uncovered by describing triggered societal changes, as well as formal measures and spontaneous processes that reduced flood risk. This novel approach has the potential to build the basis for an international data collection and analysis effort to better understand and attribute changes in risk due to hydrological extremes in the framework of the IAHSs Panta Rhei initiative. Across all case studies, we find that lower damage caused by the second event was mainly due to significant reductions in vulnerability, for example, via raised risk awareness, preparedness, and improvements of organizational emergency management. Thus, vulnerability reduction plays an essential role for successful adaptation. Our work shows that there is a high potential to adapt, but there remains the challenge to stimulate measures that reduce vulnerability and risk in periods in which extreme events do not occur.

Drought events at different timescales in southern Italy (Calabria)

This study reports an analysis of the spatial drought patterns for a region of southern Italy (Calabria) based on a homogenous monthly precipitation data set of 129 rain gauges for the period 1916–2006. Drought was expressed using the Standardized Precipitation Index (SPI), and drought events were analyzed using both the short-time (3 and 6 months) and the long-time (12 and 24 months) SPI. In particular, in order to characterize the SPI spatial pattern, index data of the three most severe drought events were interpolated and mapped using a geostatistical approach. Results show that these heavy drought episodes have widely affected the Calabria region and the drought that occurred in 2002 was the worst in terms of spatial extent both at short- and long-time scales.

A stochastic model for the analysis of the temporal change of dry spells

In the present paper a stochastic approach which considers the arrival of rainfall events as a Poisson process is proposed to analyse the sequences of no rainy days. Particularly, among the different Poisson models, a non-homogeneous Poisson model was selected and then applied to the daily rainfall series registered at the Cosenza rain gauge (Calabria, southern Italy), as test series. The aim was to evaluate the different behaviour of the dry spells observed in two different 30-year periods, i.e. 1951–1980 and 1981–2010. The analyses performed through Monte Carlo simulations assessed the statistical significance of the variation of the mean expected values of dry spells observed at annual scale in the second period with respect to those observed in the first. The model has then been verified by comparing the results of the test series with the ones obtained from other three rain gauges of the same region. Moreover, greater occurrence probabilities for long dry spells in 1981–2010 than in 1951–1980 were detected for the test series. Analogously, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 resulted less than half of the corresponding ones evaluated with the data generated for the previous 30-year period.

Bioclimatic analysis in a region of southern Italy (Calabria)

In this study, an analysis of precipitation and temperature data has been performed over 67 series observed in a region of southern Italy (Calabria). At first, to detect possible trends in the time series, an analysis was performed with the Mann–Kendall non-parametric test applied at monthly and seasonal scale. An additional investigation, useful for checking the climate change effects on vegetation, has also been included analysing bioclimatic indicators. In particular, Emberger, Rivas-Martinez and De Martonne indices were calculated by using monthly temperature and precipitation data in the period 1916–2010. The spatial pattern of the indices has been evaluated and, in order to link the vegetation and the indices, different indices maps have been intersected with the land cover data, given by the Corine Land Cover map. Moreover, the temporal evolution of the indices and of the vegetation has been analysed. Results suggest that climate change may be responsible for the forest cover change, but, given also the good relationship between the various types of bioclimate and forest formations, human activities must be considered.

Trend of monthly temperature and daily extreme temperature during 1951–2012 in New Zealand

Among several variables affecting climate change and climate variability, temperature plays a crucial role in the process because its variations in monthly and extreme values can impact on the global hydrologic cycle and energy balance through thermal forcing. In this study, an analysis of temperature data has been performed over 22 series observed in New Zealand. In particular, to detect possible trends in the time series, the Mann-Kendall non-parametric test was first applied at monthly scale and then to several indices of extreme daily temperatures computed since 1951. The results showed a positive trend in both the maximum and the minimum temperatures, in particular, in the autumn-winter period. This increase has been evaluated faster in maximum temperature than in minimum one. The trend analysis of the temperature indices suggests that there has been an increase in the frequency and intensity of hot extremes, while most of the cold extremes showed a downward tendency.

Stochastic analysis of long dry spells in Calabria (Southern Italy)

A deficit in precipitation may impact greatly on soil moisture, snowpack, streamflow, groundwater and reservoir storage. Among the several approaches available to investigate this phenomenon, one of the most applied is the analysis of dry spells. In this study, a non-homogeneous Poisson model has been applied to a set of high-quality daily rainfall series, recorded in southern Italy (Calabria region) during the period 1981–2010, for the stochastic analysis of dry spells. Firstly, some statistical details of the Poisson models were presented. Then, the proposed model has been applied to the analysis of long dry spells. In particular, a Monte Carlo technique was performed to reproduce the characteristics of the process. As a result, the main characteristics of the long dry spells have shown patterns clearly related to some geographical features of the study area, such as elevation and latitude. The results obtained from the stochastic modelling of the long dry spells proved that the proposed model is useful for the probability evaluation of drought, thus improving environmental planning and management.

Phytoclimatic map of Calabria (Southern Italy)

This study updates Pavaris phytoclimatic map in a region of southern Italy, Calabria (between 37° 50′ and 40° 10′ N and 15° 30′ and 17° 20′ E). A set of homogeneous monthly precipitation and temperature series registered in Calabria and relative to the period 1916–2010 were used. First, temperature data from 28 thermometric stations, over a 30-year period, were homogenized and processed. A multiple regression analysis was carried out between temperature data (annual average temperature, average of minimum temperature and average temperature of the coldest month of the year) as dependent variables and some geographical variables (altitude of the station, continentality and solar radiation) as independent variables. Then, temperature maps of the region were calculated and, finally, temperature and rainfall maps were intersected to define Pavaris phytoclimatic map. The results obtained in this study allow the identification and the delineation of three phytoclimatic belts.

Sensitivity to desertification of a high productivity area in Southern Italy

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.

A combined stochastic analysis of mean daily temperature and diurnal temperature range

In this paper, a stochastic model, previously proposed for the maximum daily temperature, has been improved for the combined analysis of mean daily temperature and diurnal temperature range. In particular, the procedure applied to each variable sequentially performs the deseasonalization, by means of truncated Fourier series expansions, and the normalization of the temperature data, with the use of proper transformation functions. Then, a joint stochastic analysis of both the climatic variables has been performed by means of a FARIMA model, taking into account the stochastic dependency between the variables, namely introducing a cross-correlation between the standardized noises. The model has been applied to five daily temperature series of southern Italy. After the application of a Monte Carlo simulation procedure, the return periods of the joint behavior of the mean daily temperature and the diurnal temperature range have been evaluated. Moreover, the annual maxima of the temperature excursions in consecutive days have been analyzed for the synthetic series. The results obtained showed different behaviors probably linked to the distance from the sea and to the latitude of the station.