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Coastal erosion of sandy coasts due to extreme storms or long-term changes in marine forcing is a serious natural hazard, that can directly damage coastal investments and increase flooding in low-lying coastal areas. Assessing erosion hazard at large spatial scales (from global to regional) can provide useful information for the identification of general trends and vulnerable hotspots or to guide coastal zone management and adaptation priorities at a more regional scale. So far, continental and global assessments of coastal erosion use specific geophysical datasets or assumptions to describe the coastline, without looking into the effects of these choices on the assessments. Additionally, a large spatial scale of assessment can impose computational constrains even at regional scales, where extreme storm impacts on dunes are assessed mainly with simple qualitative models. In this research, a merged product of available global elevation and bathymetric data together with global wave statistics are used to create a global map of nearshore slopes. The variability of the estimated nearshore slopes is validated in a both qualitative and quantitative manner, using available coastal classifications and in-situ surveys, showing good agreement. The uncertainties related to the use of available geophysical datasets to describe the sandy coastline in continental scale assessments are further studied at the European scale. The effects of the nearshore slope description are studied in combination with different datasets on sandy beach occurrence, using future coastal erosion due to SLR as the diagnostic. An uncertainty analysis is performed to compare the distribution of uncertainty sources for coastal erosion projections during the 21st century. Next, data-driven statistical methods are combined with process-based modelling to produce a fast meta-model, able to predict dune erosion during extreme events for the entire Dutch coast (260 km). Here a novel technique that reduces a dataset of 1,400 elevation profiles at the Dutch coast to 100 real-world representative profiles, called Typological Coastal Profiles is presented. This enabled the efficient creation of 10,000 synthetic training cases that were simulated with the well-established XBeach dune impact model. A meta-model based on artificial neural networks is created and trained with the previously described synthetic dataset. The meta-model needs just 10 morphological and hydrodynamic local profile characteristics and 4 offshore storm parameters as input and can produce an estimate of dune erosion volumes for the whole Dutch coast in a matter of seconds.

The strengths and torque of well-boiling hydrocarbons are of utmost significance. Boiling a well is one of the most critical steps in the discovery and production of oil and gas. The well&rsquo, s boiling process is expensive because the drilling depth can be as much as 7000 meters. Any delay (breakdown time) in boiling costs a lot of money for hydrocarbon firms. Various boiler parameters are continuously tracked and regulated to avoid drilling delays. This paper focuses on the vibrations occurring at the bottom hole assembly (BHA) stick-slip. Two modelling methods, the lumped parameter model and the combination of the distributed&ndash, lumped (D&ndash, L) parameter model, were used and compared to the actual measurement performance. The D&ndash, L model was found to be more precise, particularly for long strings. Using the simulations, the most comprehensive modelling methodology is introduced.

Climate change and anthropogenic pressures are widely expected to exacerbate coastal hazards such as episodic coastal flooding. This study presents global-scale potential coastal overtopping estimates, which account for not only the effects of sea level rise and storm surge, but also for wave runup at exposed open coasts. Here we find that the globally aggregated annual overtopping hours have increased by almost 50% over the last two decades. A first-pass future assessment indicates that globally aggregated annual overtopping hours will accelerate faster than the global mean sea-level rise itself, with a clearly discernible increase occurring around mid-century regardless of climate scenario. Under RCP 8.5, the globally aggregated annual overtopping hours by the end of the 21st-century is projected to be up to 50 times larger compared to present-day. As sea level continues to rise, more regions around the world are projected to become exposed to coastal overtopping., As sea levels rise, coasts are being increasingly threatened by overtopping caused by the combination of sea level rise, storm surge and wave runup. Here the authors find that global coastal overtopping has increased by over 50% in the last two decades, and under a RCP 8.5 scenario this could increase up to 50 times by 2100 compared to today.

BACKGROUND: Low Testosterone (mainly total testosterone; TTe) has been noted in patients with COVID-19. Calculated free testosterone (FTe) and bioavailable testosterone (BavTe) may reflect more accurately this hormone’s levels. In this study, we sought to assess TTe, FTe as well as BavTe in male patients with COVID-19. SUBJECTS/METHODS: Sera were collected upon admission from 65 men (10 in the intensive care units [ICU] and 55 in the wards) with polymerase chain reaction - proven COVID-19. A group of age-matched COVID-19-negative men (n=29) hospitalized in general medical wards served as controls. Age, body mass index (BMI) and 28-day mortality were noted. Measurements included TTe, sex-hormone binding globulin, albumin (the latter two for calculating FTe and BavTe) and laboratory markers of inflammation (white blood cell count [WBC], D-Dimers [D-D], lactate dehydrogenease [LDH], ferritin [Fer] and C-reactive protein [CRP]). RESULTS: Profoundly low TTe, FTe and BavTe were noted in most patients, and were associated with disease severity/outcome (being the lowest in COVID-19 patients in the ICU and overall being lower in non-survivors; analysis of covariance p

Sea level rise (SLR) will cause shoreline retreat of sandy coasts in the absence of sand supply mechanisms. These coasts have high touristic and ecological value and provide protection of valuable infrastructures and buildings to storm impacts. So far, large-scale assessments of shoreline retreat use specific datasets or assumptions for the geophysical representation of the coastal system, without any quantification of the effect that these choices might have on the assessment. Here we quantify SLR driven potential shoreline retreat and consequent coastal land loss in Europe during the twenty-first century using different combinations of geophysical datasets for (a) the location and spatial extent of sandy beaches and (b) their nearshore slopes. Using data-based spatially-varying nearshore slope data, a European averaged SLR driven median shoreline retreat of 97 m (54 m) is projected under RCP 8.5 (4.5) by year 2100, relative to the baseline year 2010. This retreat would translate to 2,500 km2 (1,400 km2) of coastal land loss (in the absence of ambient shoreline changes). A variance-based global sensitivity analysis indicates that the uncertainty associated with the choice of geophysical datasets can contribute up to 45% (26%) of the variance in coastal land loss projections for Europe by 2050 (2100). This contribution can be as high as that associated with future mitigation scenarios and SLR projections.

Climate change driven sea level rise (SLR) is expected to rise with even higher rates during the second half of the present century. This will exacerbate shoreline retreat of sandy coasts, which comprise one third of the global coastline. Sandy coasts have high touristic and ecological value while they are the first level of defense against storms, protecting valuable infrastructures and buildings. Therefore, in recent years, large scale risk assessments are considered useful tools for the guidance of policy makers to identify high risk hotspots. Reliable input data at this scale are required in order to make useful estimations. Among others, crucial data to assess the impact of SLR on shoreline retreat are the detection of different coastal types and, in particular, of sandy erodible beaches, and the nearshore slope, which is usually assumed to be uniform.The important issue of input data uncertainty and spatial variation and consequent impact on predictions has been so far ignored in most large-scale studies. Estimates of shoreline retreat are however very sensitive to the variation in these inputs. Here we quantify SLR driven potential shoreline retreat and consequent land loss in Europe during the 21st century by employing different combinations of geophysical datasets for (a) the location of sandy beaches and (b) their nearshore slopes. For the estimation of the shoreline retreat, the Bruun Rule is used, which offers a suitable approach for a first approximation of erosion impacts at large scales. Sea level rise projections associated with the moderate-emission- mitigation-policy (RCP4.5) and the high-end, business-as-usual scenario (RCP8.5) are used as boundary conditions. The location of sandy beaches is determined from two different datasets. One is based on manual visual estimation from satellite images and the other on automatic detection from satellite images using machine learning techniques. For nearshore slopes we apply the commonly used constant slope assumption of 1:100 and a newly produced global dataset which captures the spatial variation of coastal slopes.With this approach, we create four different combinations for each SLR scenario, for which we estimate and compare land loss at EU, country and NUTS3 regional level. We find that the land loss estimations for each combination can differ significantly, especially at the regional and local level. At the European or country level, even though differences in total land loss projections can be significant, they can be concealed by the spatial aggregation of the results. Using data-based spatially-varying nearshore slope data, a European averaged median shoreline retreat of 97 m (54 m) is projected under RCP 8.5 (4.5) by year 2100, relative to the baseline year 2010. This retreat would translate to 2,500 km2 (1,400 km2) of land loss. A variance-based global sensitivity analysis indicates that the uncertainty associated with the choice of geophysical datasets can contribute up to 45% (26%) of the variance in land loss projections for Europe by 2050 (2100).

Made available in DSpace on 2021-06-24T11:35:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-11 info:eu-repo/semantics/publishedVersion

Understanding long-term sandbar dynamics can be crucial for informed coastal zone management, but is often hampered by data availability. To increase the number of sandbar observations available from bathymetric surveys, this study proposes and evaluates a method to manually extract the sandbar location using freely available satellite imagery for the case study of Anmok beach in South Korea. Validation of the satellite extracted sandbar locations against 9 in-situ measurements shows good agreement with errors well within the pixel resolution of the satellite imagery (i.e. 30 m for Landsat missions). The applicability of the method is constrained to locations where (1) the cross-shore crescentic length scales are larger than the image resolution, (2) frequent wave breaking and clouds are absent and (3) the water clarity is sufficient to enable the manual extraction of the sandbar crest line. Using the additional sandbar observations from the satellite imagery significantly increases the temporal extent and resolution of the dataset for Anmok beach. This allows the study of sandbar characteristics, dynamics and impacts of human interventions to an extent that would not have been possible without the satellite imagery. Within the study period 1990–2017 it is found that the sandbar maintains a persistent crescentic pattern that is only altered during prolonged and very intense storm conditions. The cumulative alongshore migration of the sandbars is investigated and found to be in the order of hundreds of meters over the 27 years study period. Comparing the sandbar characteristics prior and after the construction of Gangneung port shows that both the amplitudes and wavelengths of the sandbar crescents near the port have decreased after its construction.

Nearshore sandbar patterns can affect the hydrodynamics and, as a result, the beach morphodynamics in the nearshore zone. Hence, spatial and temporal variability in the sandbars can influence beach accretion and erosion. Understanding the variability of the sandbar system can therefore be crucial for informed coastal zone management. So far, the methods to study sandbar dynamics mainly include datasets of video observations or occasional bathymetric surveys. However, at most locations around the world, these types of data are not or only scarcely available. In this paper we present an alternative method to analyze long-term sandbar variability by means of freely available satellite imagery. These images are globally available since the 1980’s and, thus, have the potential to be applicable at any location in the world. Here, we will illustrate the methodology by means of a case study at Anmok beach at the South Korean East coast.

Sandy beaches occupy more than one-third of the global coastline(1) and have high socioeconomic value related to recreation, tourism and ecosystem services(2). Beaches are the interface between land and ocean, providing coastal protection from marine storms and cyclones(3). However the presence of sandy beaches cannot be taken for granted, as they are under constant change, driven by meteorological(4,5), geological(6) and anthropogenic factors(1,7). A substantial proportion of the world's sandy coastline is already eroding(1,7), a situation that could be exacerbated by climate change(8,9). Here, we show that ambient trends in shoreline dynamics, combined with coastal recession driven by sea level rise, could result in the near extinction of almost half of the world's sandy beaches by the end of the century. Moderate GHG emission mitigation could prevent 40% of shoreline retreat. Projected shoreline dynamics are dominated by sea level rise for the majority of sandy beaches, but in certain regions the erosive trend is counteracted by accretive ambient shoreline changes; for example, in the Amazon, East and Southeast Asia and the north tropical Pacific. A substantial proportion of the threatened sandy shorelines are in densely populated areas, underlining the need for the design and implementation of effective adaptive measures. Erosion is a major problem facing sandy beaches that will probably worsen with climate change and sea-level rise. Half the world's beaches, many of which are in densely populated areas, could disappear by the end of the century under current trends; mitigation could lessen retreat by 40%. info:eu-repo/semantics/publishedVersion

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.