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

A Chebyshev polynomial radial basis function neural network for automated shoreline extraction from coastal imagery

This paper investigates the potential of using a polynomial radial basis function (RBF) neural network to extract the shoreline position from coastal video images. The basic structure of the proposed network encompasses a standard RBF network module, a module of nodes that use Chebyshev polynomials as activation functions, and an inference module. The experimental setup is an operational coastal video monitoring system deployed in two sites in Southern Europe to generate variance coastal images. The histogram of each image is approximated by non-linear regression, and associated with a manually extracted intensity threshold value that quantifies the shoreline position. The key idea is to use the set of the resulting regression parameters as input data, and the intensity threshold values as output data of the network. In summary, the data set is extracted by quantifying the qualitative image information, and the proposed network takes the advantage of the powerful approximation capabilities of the Chebyshev polynomials by utilizing a small number of coefficients. For comparative reasons, we apply a polynomial RBF network trained by fuzzy clustering, and a feed-forward neural network trained by the back propagation algorithm. The comparison criteria used are the standard mean square error; the data return rates, and the root mean square error of the cross-shore shoreline position, calculated against the shorelines extracted by the aforementioned annotated threshold values. The main conclusions of the simulation study are: (a) the proposed method outperforms the other networks, especially in extracting the shoreline from images used as testing data; (b) for higher polynomial orders it obtains data return rates greater than 84%, and the root mean square error of the cross-shore shoreline position is less than 1.8 meters.JRC.H.7-Climate Risk Managemen

On the systematic implementation of artificial neural networks in the classification of variance images and shoreline extraction

Monitoring of the shoreline position has become an issue of urgency given the high socio-economic impact due to the population density of the coastal zone, the increasing erosion and the projected sea-level rise. In this study, we implement a specialized monitoring system to generate a database consisted of variance images also called SIGMA images. We, then, apply a radial basis function (RBF) neural network trained with the aid of fuzzy clustering. The RBF network is able to elaborate on the image histograms in order to perform efficient image classification and shoreline extraction. The outcoming contributions of the current study can be summarised as follows. We develop a specialized regression strategy to approximate the image histograms, where for each histogram we extract a set of regression parameters. Then, the key idea is to use these parameters as the input data of the RBF network and associate them with a set of intensity thresholds that directly quantify the corresponding shorelines. In addition, the utilization of fuzzy cluster analysis provides the means to effectively estimate all the network’s parameters by taking into consideration any uncertainty hidden in the histograms. The simulation experiments showed that the proposed algorithmic framework exhibits an accurate behaviour, while it also constitutes a fully automated process.JRC.H.7-Climate Risk Managemen

Assessment of the Role of Nearshore Marine Ecosystems to Mitigate Beach Erosion: The Case of Negril (Jamaica)

Coastal and marine ecosystems are supplying a wide range of services. With accelerated Sea Level Rise, intensification of waves and storm surge severity and increasing anthropogenic pressures, these areas are under multiple threats and society may not receive the same level of ecosystems services. This study aims at measuring the trend of beach erosion and at identifying and quantifying the role of some coastal and marine ecosystems in mitigating beach erosion in the region of Negril (Jamaica). In this location, the tourism industry provides the main source of economic revenue. Even at the national level, the two beaches are important assets linked with 5% of the national revenue as 25% of the hotel rooms are located around Negril. In Jamaica, the tourism industry is a significant component of national GDP. 25% of hotel rooms are located around the two beaches of Negril, which have lost an average of 23.4 m of width since 1968. Given the importance of Negril’s beaches to their economy, the Government of Jamaica asked UNEP to conduct a study to identify causes of beach erosion in Negril and potential solutions to address trends of beach erosion, in the context of future sea level rise scenarios induced by climate change. This paper addresses the current beach erosion status and future trends under different climate scenarios. We explain how, by using remote sensing, GIS, wave modelling and multiple regressions analysis associated with national, local and community consultations, we were able to identify and quantify the role of ecosystems for mitigating beach erosion. We show that larger widths of coral and seagrass meadows reduce beach erosion.</p

Aggregate extraction from tidal sandbanks: Is dredging with nature an option? Introduction

Sandbanks are considered as primary targets for the marine aggregate industry, not only because of considerationsrelated to resource quality and operational advantages, but also due to the notion that natural sediment transportprocesses that form and maintain sandbanks are able to counterbalance the loss of sediment due to extraction.This paper introduces: (a) the problems related to the assessment of the impacts of aggregate extraction fromtidal sandbanks; and (b) the multidisciplinary and integrated research that was undertaken on the potential forregeneration of the most intensively exploited area of the Kwinte Bank (Flemish Banks, Belgian Continental Shelf),following the cessation of extraction on this part of the sandbank. We assert that the results of a 30-year monitoringof exploitation effects along the Kwinte Bank have put in doubt the universal notion of ‘dredging with nature’. Theelongated depressions that have been observed in the most heavily exploited areas provide a clear signal that moredetailed information and thorough assessment are required in order to understand and predict the most likelyevolution of the bank’s hydro-sedimentary regime and its natural and anthropogenically-induced dynamics

Assessment of and Adaptation to Beach Erosion in Islands: An Integrated Approach

Island beaches, which form significant natural and economic resources, are under increasing erosion risk due to sea level rise. The present contribution proposes an integrated methodological framework for the evaluation of the socio-economic significance of beaches and their vulnerability to sea level rise and the design of effective adaptation measures. The approach comprises four steps: (i) beach ranking on the basis of their socio-economic significance and vulnerability in order to prioritize adaptation responses; (ii) monitoring of the hydro- and morphodynamic regime of the most highly ranking beaches using field observations and modelling, (iii) assessment of the sediment volumes required for beach nourishment under different scenarios of sea level rise and nourishment designs; (iv) evaluation of the marine aggregate potential of the adjacent areas that can be used for beach nourishment. The framework was applied to the Greek island of Chios, which has many beaches that are already under erosion. The methodology was shown to provide a structured approach for the assessment and response to erosion of the most vulnerable beach