Uncertainty and Bias in Global to Regional Scale Assessments of Current and Future Coastal Flood Risk.

This study provides a literature-based comparative assessment of uncertainties and biases in global to world-regional scale assessments of current and future coastal flood risks, considering mean and extreme sea-level hazards, the propagation of these into the floodplain, people and coastal assets exposed, and their vulnerability. Globally, by far the largest bias is introduced by not considering human adaptation, which can lead to an overestimation of coastal flood risk in 2100 by up to factor 1300. But even when considering adaptation, uncertainties in how coastal societies will adapt to sea-level rise dominate with a factor of up to 27 all other uncertainties. Other large uncertainties that have been quantified globally are associated with socio-economic development (factors 2.3-5.8), digital elevation data (factors 1.2-3.8), ice sheet models (factor 1.6-3.8) and greenhouse gas emissions (factors 1.6-2.1). Local uncertainties that stand out but have not been quantified globally, relate to depth-damage functions, defense failure mechanisms, surge and wave heights in areas affected by tropical cyclones (in particular for large return periods), as well as nearshore interactions between mean sea-levels, storm surges, tides and waves. Advancing the state-of-the-art requires analyzing and reporting more comprehensively on underlying uncertainties, including those in data, methods and adaptation scenarios. Epistemic uncertainties in digital elevation, coastal protection levels and depth-damage functions would be best reduced through open community-based efforts, in which many scholars work together in collecting and validating these data.

A Pan-European high resolution storm surge hindcast.

This contribution presents the high-resolution Pan-European storm surge (SSL) dataset, ANYEU-SSL, produced with the SCHISM circulation model. The dataset covers 40 years (1979-2018) of SSL data along the European coastline with 3-hour temporal resolution and has been extensively validated for the period spanning from 1979 to 2016, considering the whole time series, as well as for the extreme SSL values. Validation against tidal gauge data shows an average RMSE of 0.10 m, and RMSE below 0.12 m in 75% of the tidal gauges. Comparisons with satellite altimetry data show average RMSE of 0.07 m. SSL trends are estimated as an example of a potential application case of the dataset. The results indicate an overall latitudinal gradient in the trend of the extreme storm surge magnitude for the period 1979-2016. SSLs appear to increase in areas with latitudes >50 °N and to decrease in the lower latitudes. Additionally, a seasonal variation of the extreme SSL, particularly strong in the northern areas, has been observed. The dataset is publicly available and aspires to provide the scientific community with an important data source for the study of storm surge phenomena and consequential impacts, either on large or local scales.

Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change.

In low-lying coastal areas, the co-occurrence of high sea level and precipitation resulting in large runoff may cause compound flooding (CF). When the two hazards interact, the resulting impact can be worse than when they occur individually. Both storm surges and heavy precipitation, as well as their interplay, are likely to change in response to global warming. Despite the CF relevance, a comprehensive hazard assessment beyond individual locations is missing, and no studies have examined CF in the future. Analyzing co-occurring high sea level and heavy precipitation in Europe, we show that the Mediterranean coasts are experiencing the highest CF probability in the present. However, future climate projections show emerging high CF probability along parts of the northern European coast. In several European regions, CF should be considered as a potential hazard aggravating the risk caused by mean sea level rise in the future.

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.