Climate change impacts on critical international transportation assets of Caribbean Small Island Developing States (SIDS): the case of Jamaica and Saint Lucia

This contribution presents an assessment of the potential vulnerabilities to climate variability and change (CV & C) of the critical transportation infrastructure of Caribbean Small Island Developing States (SIDS). It focuses on potential operational disruptions and coastal inundation forced by CV & C on four coastal international airports and four seaports in Jamaica and Saint Lucia which are critical facilitators of international connectivity and socioeconomic development. Impact assessments have been carried out under climatic conditions forced by a 1.5 °C specific warming level (SWL) above pre-industrial levels, as well as for different emission scenarios and time periods in the twenty-first century. Disruptions and increasing costs due to, e.g., more frequent exceedance of high temperature thresholds that could impede transport operations are predicted, even under the 1.5 °C SWL, advocated by the Alliance of Small Island States (AOSIS) and reflected as an aspirational goal in the Paris Climate Agreement. Dynamic modeling of the coastal inundation under different return periods of projected extreme sea levels (ESLs) indicates that the examined airports and seaports will face increasing coastal inundation during the century. Inundation is projected for the airport runways of some of the examined international airports and most of the seaports, even from the 100-year extreme sea level under 1.5 °C SWL. In the absence of effective technical adaptation measures, both operational disruptions and coastal inundation are projected to increasingly affect all examined assets over the course of the century

Coastal vulnerability assessment based on video wave run-up observations at a mesotidal, steep-sloped beach

Coastal imagery obtained from a coastal video monitoring station installed at Faro Beach, S. Portugal, was combined with topographic data from 40 surveys to generate a total of 456 timestack images. The timestack images were processed in an open-access, freely available graphical user interface (GUI) software, developed to extract and process time series of the cross-shore position of the swash extrema. The generated dataset of 2% wave run-up exceedence values R 2 was used to form empirical formulas, using as input typical hydrodynamic and coastal morphological parameters, generating a best-fit case RMS error of 0.39 m. The R 2 prediction capacity was improved when the shore-normal wind speed component and/or the tidal elevation η tide were included in the parameterizations, further reducing the RMS errors to 0.364 m. Introducing the tidal level appeared to allow a more accurate representation of the increased wave energy dissipation during low tides, while the negative trend between R 2 and the shore-normal wind speed component is probably related to the wind effect on wave breaking. The ratio of the infragravity-to-incident frequency energy contributions to the total swash spectra was in general lower than the ones reported in the literature E infra/E inci > 0.8, since low-frequency contributions at the steep, reflective Faro Beach become more significant mainly during storm conditions. An additional parameterization for the total run-up elevation was derived considering only 222 measurements for which η total,2 exceeded 2 m above MSL and the best-fit case resulted in RMS error of 0.41 m. The equation was applied to predict overwash along Faro Beach for four extreme storm scenarios and the predicted overwash beach sections, corresponded to a percentage of the total length ranging from 36% to 75%.info:eu-repo/semantics/publishedVersio

Collision-induced dissociation of Nb (x) O (y) (+) (x=1, 2, y=2-12) clusters: crossed molecular beams and collision cell studies

Oxygen-rich niobium oxide clusters are formed by mixing laser-produced Nb plasma with pure oxygen, and their stability is investigated by mass spectrometry and collision-induced dissociation. We use an experimental configuration recently developed by our group, where the cluster ions beam is crossed with a secondary beam of noble gas atoms, and the fragments are rejected by a retarding field energy analyzer. In this way, the relative collision cross sections of Nb (x) O (y) (+) (x = 1, 2, y = 2-12) clusters have been measured and information about their fragmentation channels has been obtained

Autonomous Differential Absorption Laser Device for Remote Sensing of Atmospheric Greenhouse Gases

A ground-based, integrated path, differential absorption (IPDA) light detection device capable of measuring multiple greenhouse gas (GHG) species in the atmosphere is presented. The device was developed to monitor greenhouse gas concentrations in small-scale areas with high emission activities. It is equipped with two low optical power tunable diode lasers in the near-infrared spectral range for the atmospheric detection of carbon dioxide, methane, and water vapors (CO2, CH4 and H2O). The device was tested with measurements of background concentrations of CO2 and CH4 in the atmosphere (Crete, Greece). Accuracies in the measurement retrievals of CO2 and CH4 were estimated at 5 ppm (1.2%) and 50 ppb (2.6%), respectively. A method that exploits the intensity of the recorded H2O absorption line in combination with weather measurements (water vapor pressure, temperature, and atmospheric pressure) to calculate the GHG concentrations is proposed. The method eliminates the requirement for measuring the range of the laser beam propagation. Accuracy in the measurement of CH4 using the H2O absorption line is estimated at 90 ppb (4.8%). The values calculated by the proposed method are in agreement with those obtained from the differential absorption LiDAR equation (DIAL)

Shoreline Extraction from Coastal Images Using Chebyshev Polynomials and RBF Neural Networks

Part 14: Image Video Processing 4International audienceIn this study, we use a specialized coastal monitoring system for the test case of Faro beach (Portugal), and generate a database consisted of variance coastal images. The images are elaborated in terms of an empirical image thresholding procedure and the Chebyshev polynomials. The resulting polynomial coefficients constitute the input data, while the resulting thresholds the output data. We, then, use the above data set to train a radial basis function network structure with the aid of input-output fuzzy clustering and a steepest descent approach. The implementation of the RBF network leads to an effective detection and extraction of the shoreline of the beach under consideration

Application of Ultraviolet-Visible Absorption Spectroscopy with Machine Learning Techniques for the Classification of Cretan Wines

The present study was aimed at the identification, differentiation and characterization of red and white Cretan wines, which are described with Protected Geographical Indication (PGI), using ultraviolet–visible absorption spectroscopy. Specifically, the grape variety, the wine aging process and the role of barrel/container type were investigated. The combination of spectroscopic results with machine learning-based modelling demonstrated the use of absorption spectroscopy as a facile and low-cost technique in wine analysis. In this study, a clear discrimination among grape varieties was revealed. Moreover, a grouping of samples according to aging period and container type of maturation was accomplished, for the first time

Verifying the Geographical Origin and Authenticity of Greek Olive Oils by Means of Optical Spectroscopy and Multivariate Analysis

Olive oil samples from three different Greek regions (Crete, Peloponnese and Lesvos) were examined by optical spectroscopy in a wide spectral region from ultraviolet to near infrared using absorption, fluorescence and Raman spectroscopies. With the aid of machine learning methods, such as multivariate partial least squares discriminant analysis, a clear classification of samples originating from the different Greek geographical regions was revealed. Moreover, samples produced in different subareas of Crete and Peloponnese were also well discriminated. Furthermore, mixtures of olive oils from different geographical origins were studied employing partial least squares as a tool to establish a model between the actual and predicted compositions of the mixtures. The results demonstrated that optical spectroscopy combined with multivariate statistical analysis can be used as an emerging innovative alternative to the classical analytical methods for the identification of the origin and authenticity of olive oils