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

SVMT: A MATLAB toolbox for stereo-vision motion tracking of motor reactivity

This article presents a Matlab-based stereo-vision motion tracking system (SVMT) for the detection of human motor reactivity elicited by sensory stimulation. It is a low-cost, non-intrusive system supported by Graphical User Interface (GUI) software, and has been successfully tested and integrated in a broad array of physiological recording devices at the Human Physiology Laboratory in the University of Granada. The SVMT GUI software handles data in Matlab and ASCII formats. Internal functions perform lens distortion correction, camera geometry definition, feature matching, as well as data clustering and filtering to extract 3D motion paths of specific body areas. System validation showed geo-rectification errors below 0.5 mm, while feature matching and motion paths extraction procedures were successfully validated with manual tracking and RMS errors were typically below 2% of the movement range. The application of the system in a psychophysiological experiment designed to elicit a startle motor response by the presentation of intense and unexpected acoustic stimuli, provided reliable data probing dynamical features of motor responses and habituation to repeated stimulus presentations. The stereo-geolocation and motion tracking performance of the SVMT system were successfully validated through comparisons with surface EMG measurements of eyeblink startle, which clearly demonstrate the ability of SVMT to track subtle body movement, such as those induced by the presentation of intense acoustic stimuli. Finally, SVMT provides an efficient solution for the assessment of motor reactivity not only in controlled laboratory settings, but also in more open, ecological environments

Digital technologies can enhance climate resilience of critical infrastructure

Delivering infrastructure, resilient to multiple natural hazards and climate change, is fundamental to continued economic prosperity and social coherence. This is a strategic priority of the United Nations Sustainable Development Goals (SDGs), the World Bank, the Organisation for Economic Co-operation and Development (OECD), public policies and global initiatives. The operability and functionality of critical infrastructure are continuously challenged by multiple stressors, increasing demands and ageing, whilst their interconnectedness and dependencies pose additional challenges. Emerging and disruptive digital technologies have the potential to enhance climate resilience of critical infrastructure, by providing rapid and accurate assessment of asset condition and support decision-making and adaptation. In this pursuit, it is imperative to adopt multidisciplinary roadmaps and deploy computational, communication and other digital technologies, tools and monitoring systems. Nevertheless, the potential of these emerging technologies remains largely unexploited, as there is a lack of consensus, integrated approaches and legislation in support of their use. In this perspective paper, we discuss the main challenges and enablers of climate-resilient infrastructure and we identify how available roadmaps, tools and emerging digital technologies, e.g. Internet of Things, digital twins, point clouds, Artificial Intelligence, Building Information Modelling, can be placed at the service of a safer world. We show how digital technologies will lead to infrastructure of enhanced resilience, by delivering efficient and reliable decision-making, in a proactive and/or reactive manner, prior, during and after hazard occurrences. In this respect, we discuss how emerging technologies significantly reduce the uncertainties in all phases of infrastructure resilience evaluations. Thus, building climate-resilient infrastructure, aided by digital technologies, will underpin critical activities globally, contribute to Net Zero target and hence safeguard our societies and economies. To achieve this we set an agenda, which is aligned with the relevant SDGs and highlights the urgent need to deliver holistic and inclusive standards and legislation, supported by coordinated alliances, to fully utilise emerging digital technologies

Performance of intertidal topography video monitoring of a meso-tidal reflective beach in South Portugal

This study discusses site-specific system optimization efforts related to the capability of a coastal video station to monitor intertidal topography. The system consists of two video cameras connected to a PC, and is operating at the meso-tidal, reflective Faro Beach (Algarve coast, S. Portugal). Measurements from the period February 4, 2009 to May 30, 2010 are discussed in this study. Shoreline detection was based on the processing of variance images, considering pixel intensity thresholds for feature extraction, provided by a specially trained artificial neural network (ANN). The obtained shoreline data return rate was 83%, with an average horizontal cross-shore root mean square error (RMSE) of 1.06 m. Several empirical parameterizations and ANN models were tested to estimate the elevations of shoreline contours, using wave and tidal data. Using a manually validated shoreline set, the lowest RMSE (0.18 m) for the vertical elevation was obtained using an ANN while empirical parameterizations based on the tidal elevation and wave run-up height resulted in an RMSE of 0.26 m. These errors were reduced to 0.22 m after applying 3-D data filtering and interpolation of the topographic information generated for each tidal cycle. Average beach-face slope tan(β) RMSE were around 0.02. Tests for a 5-month period of fully automated operation applying the ANN model resulted in an optimal, average, vertical elevation RMSE of 0.22 m, obtained using a one tidal cycle time window and a time-varying beach-face slope. The findings indicate that the use of an ANN in such systems has considerable potential, especially for sites where long-term field data allow efficient training