Application of an autonomous robot for the collection of nearshore topographic and hydrodynamic measurements

Beach topographic and hydrodynamic measurements are essential for coastal geology and engineering studies as well as sustainable coastal management. Standard approaches involve either time-consuming manual data acquisition usually with limited coverage or remote sensing techniques which are usually characterized by low resolution or increased costs. The present contribution reports the results from the application of the autonomous robot RTS-Hanna with a calibrated sensor setup including 3D laser range scanners, a camera, a Differential GPS and an inertial measurement unit which significantly facilitates field data collection. RTS-Hanna was tested at the Wadden Sea Barrier Island Langeoog, Northern Germany, for two days and was proven capable of autonomously collecting topographic scans. 175 GB of dense topographic and water surface elevation data were collected, including RBG images, while RTS-Hanna covered a total of 21 km of coastline in approximately 3 hours. Scans of the surf/swash zone allowed continuous measurements of topographic changes at the beachface, wave propagation velocities and wave breaking heights

A method for regional estimation of climate change exposure of coastal infrastructure: Case of USVI and the influence of digital elevation models on assessments

Objective: This study tests the impacts of Digital Elevation Model (DEM) data on an exposure assessment methodology developed to quantify flooding of coastal infrastructure from storms and sea level rise on a regional scale. The approach is piloted on the United States Virgin Islands (USVI) for a one-hundred-year storm event in 2050 under the IPCC\u27s 8.5 emission scenario (RCP 8,5). Method: Flooding of individual infrastructure was tested against three different digital elevation models using a GIS-based coastal infrastructure database created specifically for the project using aerial images. Inundation for extreme sea levels is based on dynamic simulations using Lisflood-ACC (LFP). Results: The model indicates transport and utility infrastructure in the USVI are considerably exposed to sea level rise and modeled storm impacts from climate change. Prediction of flood extent was improved with a neural network processed SRTM, versus publicly available SRTM (~30 m) seamless C-band DEM but both SRTM based models underestimate flooding compared to LIDAR DEM. The modeled scenario, although conservative, showed significant flood exposure to a large number of access roads to facilities, 113/176 transportation related buildings, and 29/66 electric utility and water treatment buildings including six electric power transformers and six waste water treatment clarifiers. Conclusion: The method bridges a gap between large-scale non-specific flood assessments and single-facility detailed assessments and can be used to efficiently quantify and prioritize parcels and large structures in need of further assessment for regions that lack detailed data to assess climate exposure to sea level rise and flooding caused by waves. The method should prove particularly useful for assessment of Small Island Developing State regions that lack LIDAR data, such as the Caribbean

Brief communication: the role of using precipitation or river discharge data when assessing global coastal compound flooding

Interacting storm surges and high water-runoff can cause compound flooding (CF) in low-lying coasts and river estuaries. The large-scale CF hazard has been typically studied using proxies such as the concurrence of storm surge extremes either with precipitation or with river discharge extremes. Here the impact of the choice of such proxies is addressed employing state-of-the-art global datasets. Although being proxies of diverse physical mechanisms, we find that the two approaches show similar CF spatial patterns. However, deviations increase with the catchment size and our findings indicate that CF in long rivers (catchment > 5-10,000 Km2) is more accurately analysed using river discharge data. The precipitation-based assessment allows for considering local rainfall-driven CF, and CF in small rivers not resolved by large-scale datasets

Database on coastal vulnerability and exposure

In this document, we report progress on the development of European layers on exposure and vulnerability. This involves the collection and cataloguing of relevant exposure factors (e.g., land use, population, settlements, infrastructures) and vulnerability indicators (coastal flood protection, damage functions) as well as the development and application of tools for the logging, spatial interpolation, statistical analysis and validation of the collected information. All data are available through the The Risk Data hub database the aim of which is to improve the accessibility and dissemination of EU-wide curated risk data for fostering Disaster Risk Management (DRM).JRC.E.1-Disaster Risk Managemen

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

The transformed-stationary approach: A generic and simplified methodology for non-stationary extreme value analysis

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MATLAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/ (Mentaschi et al., 2016)

Assessment of global wave models on regular and unstructured grids using the Unresolved Obstacles Source Term

The Unresolved Obstacles Source Term (UOST) is a general methodology for parameterizing the dissipative effects of subscale islands, cliffs, and other unresolved features in ocean wave models. Since it separates the dissipation from the energy advection scheme, it can be applied to any numerical scheme or any type of mesh. UOST is now part of the official release of WAVEWATCH III, and the freely available packagealphaBetaLabautomates the estimation of the parameters needed for the obstructed cells. In this contribution, an assessment of global regular and unstructured (triangular) wave models employing UOST is presented. The results in regular meshes show an improvement in model skill, both in terms of spectrum and of integrated parameters, thanks to the UOST modulation of the dissipation with wave direction, and to considering the cell geometry. The improvement is clear in wide areas characterized by the presence of islands, like the whole central-western Pacific Basin. In unstructured meshes, the use of UOST removes the need of high resolution in proximity of all small features, leading to (a) a simplification in the development process of large scale and global meshes, and (b) a significant decrease of the computational demand of accurate large-scale models

The role of culture for coastal disaster risk reduction measures: empirical evidence from northern and southern Europe

Recent and historic high-impact events have demonstrated significant flood risks to many coastal areas in Europe and across the globe. Understanding the behavior of humans in relation to risk management poses grand challenges for both natural and social sciences and humanities. The study analyzes the cultural aspects of coastal risk management and illustrates path-dependencies of concrete disaster risk reduction measures in relation to local contexts in European coastal regions in Northern and South Western Europe. It adopts a comparative approach by targeting risk perception and risk management related to coastal floods and erosion, induced by storms and sea level rise, in two contrasting coastal areas: German coastal state Schleswig–Holstein at the Baltic Sea (especially the communities Eckernförde and Timmendorfer Strand) and the Portuguese barrier island system of Ria Formosa (especially the community of Faro Beach). Both regions are very low lying with only a few meters above sea level and exposed to similar hazards such as erosion and floods induced by coastal storms, and while they are both attractive touristic destinations, they are culturally, socio-economically and politically very different. The geographical and the socio-cultural contexts of the case study regions are assessed first using an explorative approach, followed by an analysis of the relevance of cultural aspects for the implementation of disaster risk reduction measures. The study addresses both first responders (city authorities, citizens) and scholars. It is found that the choice of risk reduction measures hinges on the values underlying people's perspectives about the desired outcomes of specific measures and that the role of identity and meaning making are still undervalued in decision making processes. It concludes that subjective capacities formed by cultural identities, knowledge, trust coupled with a variety of factors of socio-economic and political texture are important to understand local decision making processes. The authors found that lively ‘culture of risk memory’, ‘trust in scientific information and community’ as well as decision making of coastal authorities coupled with inclusiveness and participation of communities in formulating and implementing disaster risk reduction measures are prerequisites for successful collaboration and in turn execution of disaster risk reduction measures.info:eu-repo/semantics/publishedVersio

Beach erosion and recovery during consecutive storms at a steep-sloping, meso-tidal beach

This study analyses beach morphological change during six consecutive storms acting on the meso-tidal Faro Beach (south Portugal) between 15 December 2009 and 7 January 2010. Morphological change of the sub-aerial beach profile was monitored through frequent topographic surveys across 11 transects. Measurements of the surf/swash zone dimensions, nearshore bar dynamics, and wave run-up were extracted from time averaged and timestack coastal images, and wave and tidal data were obtained from offshore stations. All the information combined suggests that during consecutive storm events, the antecedent morphological state can initially be the dominant controlling factor of beach response; while the hydrodynamic forcing, and especially the tide and surge levels, become more important during the later stages of a storm period. The dataset also reveals the dynamic nature of steep-sloping beaches, since sub-aerial beach volume reductions up to 30m3/m were followed by intertidal area recovery (–2<z 3m) with rates reaching ~10m3/m. However, the observed cumulative dune erosion and profile pivoting imply that storms, even of regular intensity, can have a dramatic impact when they occur in groups. Nearshore bars seemed to respond to temporal scales more related to storm sequences than to individual events. The formation of a prominent crescentic offshore bar at ~200m from the shoreline appeared to reverse the previous offshore migration trend of the inner bar, which was gradually shifted close to the seaward swash zone boundary. The partially understood nearshore bar processes appeared to be critical for storm wave attenuation in the surf zone; and were considered mainly responsible for the poor interpretation of the observed beach behaviour on the grounds of standard, non-dimensional, morphological parameters.202798info:eu-repo/semantics/publishedVersio