A Model-Derived Empirical Formulation for Wave Run-Up on Naturally Sloping Beaches

A new set of empirical formulations has been derived to predict wave run-up at naturally sloping sandy beaches. They are obtained by fitting the results of hundreds of XBeach-NH+ model simulations. The simulations are carried out over a wide range of offshore wave conditions (wave heights ranging from 1 to 12 m and periods from 6 to 16 s), and surf zone (Dean parameters aD ranging from 0.05 to 0.30) and beach geometries (slopes ranging from 1:100 to 1:5). The empirical formulations provide estimates of wave set-up, incident and infragravity wave run-up, and total run-up R2%. Reduction coefficients are included to account for the effects of incident wave angle and directional spreading. The formulations have been validated against the Stockdon dataset and show better skill at predicting R2% run-up than the widely used Stockdon relationships. Unlike most existing run-up predictors, the relations presented here include the effect of the surf zone slope, which is shown to be an important parameter for predicting wave run-up. Additionally, this study shows a clear relationship between infragravity run-up and beach slope, unlike most existing predictors.

Climate-driven Impacts in Ports on a Tectonically Active Coast Under Mid- and End-of-century RCP 8.5 Scenario

Economic costs due to operational downtime and wave overtopping under the RCP 8.5 scenario are evaluated at 7 Chilean ports located on a tectonically active-coast. Wave statistics for a historical period (1985-2004), mid-century (2026-2045) and end-of-century projections (2081-2100) are computed with a Pacific-wide model, forced by wind fields from six General Circulation Models. Offshore waves are then downscaled to each port, where downtime is computed by comparing wave heights with vessel berthing criteria. The difference in downtime between the historical and projected periods is attributed to climate change. While changes in offshore wave climate will be moderate and spatially smooth in the region, some ports will reduce and others increase downtime for mid-century projections due to local effects. By the end-of-century, however, all ports will experience downtime reduction. Additionally, by mid-century, overtopping will increase in northern ports as a combination of extreme waves and sea-level rise (SLR), while in southern ports it will be slightly reduced due to milder waves. By the end-of century, overtopping will increase in the whole region, mainly driven by SLR. Overtopping rates, however, are significantly altered by coseismic uplift/subsidence which may occur during the design-life of coastal works. Adaptation measures are finally proposed.

A Clustering Approach for Predicting Dune Morphodynamic Response to Storms Using Typological Coastal Profiles: A Case Study at the Dutch Coast

Dune erosion driven by extreme marine storms can damage local infrastructure or ecosystems and affect the long-term flood safety of the hinterland. These storms typically affect long stretches (∼100 km) of sandy coastlines with variable topo-bathymetries. The large spatial scale makes it computationally challenging for process-based morphological models to be used for predicting dune erosion in early warning systems or probabilistic assessments. To alleviate this, we take a first step to enable efficient estimation of dune erosion using the Dutch coast as a case study, due to the availability of a large topo-bathymetric dataset. Using clustering techniques, we reduce 1,430 elevation profiles in this dataset to a set of typological coastal profiles (TCPs), that can be employed to represent dune erosion dynamics along the whole coast. To do so, we use the topo-bathymetric profiles and historic offshore wave and water level conditions, along with simulations of dune erosion for a number of representative storms to characterize each profile. First, we identify the most important drivers of dune erosion variability at the Dutch coast, which are identified as the pre-storm beach geometry, nearshore slope, tidal level and profile orientation. Then using clustering methods, we produce various sets of TCPs, and we test how well they represent dune morphodynamics by cross-validation on the basis of a benchmark set of dune erosion simulations. We find good prediction skill (0.83) with 100 TCPs, representing a 93% input and associated computational costs reduction. These TCPs can be used in a probabilistic model forced with a range of offshore storm conditions, enabling national scale coastal risk assessments. Additionally, the presented techniques could be used in a global context, utilizing elevation data from diverse sandy coastlines to obtain a first order prediction of dune erosion around the world.

Tsunami propagation kernel and its applications

Tsunamis rarely occur in a specific area, and their occurrence is highly uncertain. Suddenly generated from their sources in deep water, they occasionally undergo tremendous amplification in shallow water to devastate low-lying coastal areas. Despite the advancement of computational power and simulation algorithms, there is a need for novel and rigorous approaches to efficiently predict coastal amplification of tsunamis during different disaster management phases, such as tsunami risk assessment and real-time forecast. This study presents convolution kernels that can instantly predict onshore waveforms of water surface elevation and flow velocity from observed/simulated wave data away from the shore. Kernel convolution involves isolating an incident-wave component from the offshore wave data and transforming it into the onshore waveform. Moreover, unlike previously derived ones, the present kernels are based on shallow-water equations with a damping term and can account for tsunami attenuation on its path to the shore with a damping parameter. Kernel convolution can be implemented at a low computational cost compared to conventional numerical models that discretise the spatial domain. The prediction capability of the kernel method was demonstrated through application to real-world tsunami cases.

A Method for Identifying Compatible Locations for Wave Energy Exploration With Different WECs

This paper aims to assess the marine space availability for the nearshore and offshore wave energy exploration while avoiding conflict with any technical, environmental, legal or other use restriction. Within the available areas retrieved in a GIS environment, it is presented a method to evaluate the performance of various state-of-the-art Wave Energy Converter technologies in terms of the expected Power Output Capacity Factor and Capture Width, when potentially working in the characteristic sea states found on the selected areas. The method is applied to a case study in the Archipelago of Azores. The data of the wave resource utilized comes from a hindcast model refined down to a spatial resolution of 0.05° that makes possible the detailed analysis and spatial comparison of results at insular local scales. The results intend to provide stakeholders and decision-makers with meaningful information about the suitable locations for the wave energy exploration and about the most efficient converter devices in such locations for the potential deployment of a wave energy exploration facility.

Investigation of long-term changes of coastal wave directionality patterns and their connections with NAO climatic index: UK case study

<p>The effects of climate change are at the spotlight of scientific research. In coastal science the effects of sea-level rise (SLR) on coastal areas, mainly as a result of melting of ice sheets and thermal volume expansion consist an intensive area of research. As well the changing ocean wave field due to greenhouse effect and interactions of atmospheric processes is under investigation. Researchers have placed focus on significant wave height changes and their associated impacts on the coastal environment, with evidence suggesting that the number, intensity and location of storms will change. It is suggested that equal attention should be placed on the mean wave direction changes and the effects that these changes may have on the coastlines and surrounding coastal infrastructure. Following that, this study investigated the changes in wave direction data since 1979 to 2019 covering 40 years’ time period at 11 offshore UK coastal locations. The selected locations lie close to WaveNet, Cefas’ strategic wave monitoring network points for the UK. Stakeholders use the data to provide advice and guidance to all involved parties including responders and communities about coastal flood risk. On a longer timescale the data provide evidence to coastal engineers and scientists of the wave climate change patterns and the implications this may have on coastal structures and flood defences design. Based on this initiative, this study investigated UK offshore wave climate changes by performing a longer timescale analysis of changes of wave direction patterns. The wave direction data were taken from ECMWF ERA5 6-hour hind cast data catalogue which covers 40 years’ time period from 1797-2019 (Copernicus Climate Change Service (C3S), 2017). MATLAB software coding was primarily utilized for data processing and analyses. Following that, inferential statistics were applied to map inter-decadal statistical changes in wave direction patterns, suggesting that wave directionality patterns have presented changes at 11 offshore locations tested.  The connections of wave directions with North Atlantic Oscillation (NAO) Climatic Index are currently investigated through use of machine learning approaches. The results of this study can be confidently used in wave transformation computational models coupled with hydro-morphodynamic models to downscale offshore wave direction changes to UK coastal areas. This can help identify susceptible coasts to offshore wave climate change. Susceptibility is regarded in form of coastal erosion and accretion rates changes as a result of altered offshore wave conditions, which might affect coastal flood risk with potential impacts on critical infrastructure.  </p>

Extreme waves induced by cyclone Nargis at Myanmar coast: numerical modeling versus satellite observations

The deltaic coast of Myanmar was severely hit by tropical cyclone Nargis in May 2008. In the present study, a top-down numerical simulation approach using the Weather Research and Forecasting (WRF) and Simulating WAves Nearshore (SWAN) models was conducted to study the meteorological and offshore wave characteristics of cyclone Nargis near the coast of Myanmar. The WRF simulation results agree well with the observed data from the India Meteorological Department. SWAN simulation results were compared with the WaveWatch 3 model by National Oceanic and Atmospheric Administration and validated against available measurement data from satellites. The model results show relatively good agreement, and hindcast with satellites data (significant wave height only) shows a correlation coefficient value of 0.89. The SWAN and satellite comparisons also show better fit for high wave conditions. The resulted maximum significant wave height of 7.3 m by SWAN is considerably higher in energy than the seasonal waves normally prevalent at Myanmar’s deltaic coast. The possibility of high energy waves due to cyclones should be considered during the design and operation of coastal and offshore projects in the area, particularly given the risks that climate change can intensify cyclones in the future. Since Myanmar lacks a dense network of in-situ observational stations, the methodology used in the current study presents the potential application of various numerical techniques and satellite data to estimate extreme wave conditions near the Myanmar coast.