Degrading permafrost river catchments and their impact on Arctic Ocean nearshore processes

Arctic warming is causing ancient perennially frozen ground (permafrost) to thaw, resulting in ground collapse, and reshaping of landscapes. This threatens Arctic peoples' infrastructure, cultural sites, and land-based natural resources. Terrestrial permafrost thaw and ongoing intensification of hydrological cycles also enhance the amount and alter the type of organic carbon (OC) delivered from land to Arctic nearshore environments. These changes may affect coastal processes, food web dynamics and marine resources on which many traditional ways of life rely. Here, we examine how future projected increases in runoff and permafrost thaw from two permafrost-dominated Siberian watersheds—the Kolyma and Lena, may alter carbon turnover rates and OC distributions through river networks. We demonstrate that the unique composition of terrestrial permafrost-derived OC can cause significant increases to aquatic carbon degradation rates (20 to 60% faster rates with 1% permafrost OC). We compile results on aquatic OC degradation and examine how strengthening Arctic hydrological cycles may increase the connectivity between terrestrial landscapes and receiving nearshore ecosystems, with potential ramifications for coastal carbon budgets and ecosystem structure. To address the future challenges Arctic coastal communities will face, we argue that it will become essential to consider how nearshore ecosystems will respond to changing coastal inputs and identify how these may affect the resiliency and availability of essential food resources.

Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images

Accurate water surface elevation estimation is essential for understanding nearshore processes, but it is still challenging due to limitations in measuring water level using in-situ acoustic sensors. This paper presents a vision-based water surface elevation estimation approach using multi-view datasets. Specifically, we propose a visual domain adaptation method to build a water level estimator in spite of a situation in which ocean wave height cannot be measured directly. We also implemented a semi-supervised approach to extract wave height information from long-term sequences of wave height observations with minimal supervision. We performed wave flume experiments in a hydraulic laboratory with two cameras with side and top viewpoints to validate the effectiveness of our approach. The performance of the proposed models were evaluated by comparing the estimated time series of water elevation with the ground-truth wave gauge data at three locations along the wave flume. The estimated time series were in good agreement within the averaged correlation coefficient of 0.98 and 0.90 on the measurement and 0.95 and 0.85 on the estimation for regular and irregular waves, respectively.

Sediment Transport and Morphological Response to Nearshore Nourishment Projects on Wave-Dominated Coasts

Nearshore nourishments are constructed for shoreline protection from waves, to provide sediment nourishment to the beach profile, and to beneficially use dredged sediment from navigation channel maintenance. However, it is poorly understood how placement morphology and depth influence nearshore processes operated on wave-dominated coasts. This study investigates the wave fields, sediment transport, and morphological response to three common nearshore nourishment shapes, nearshore berm (elongated bar), undulated nearshore berm, and small discrete mounds, with numerical experiments utilizing the Coastal Modeling System. The nourishments are placed in depths between 3 m and 7 m with a volume of approximately 100,000 m3 and between 400 m and 1000 m in alongshore length. Numerical experiments are carried out in three distinct coastal settings with representative wave climates and geomorphology. Simulation results indicate that shallower, more continuous berms attenuate the most wave energy, while deeper, more diffuse placements retain more sediment. Results from this study improve the understanding of nearshore nourishment shapes and can support decision makers identifying the most appropriate construction technique for future nearshore nourishment projects.

Enhanced surf zone and wave runup observations with hovering drone-mounted LiDAR

We demonstrate that a hovering, drone-mounted laser scanner (LiDAR) paired with a survey-grade satellite and inertial positioning system measures the wave transformation across the surf zone and the resulting runup with accuracy almost equal to a stationary truck-mounted terrestrial LiDAR. The drone, a multi-rotor small uncrewed aircraft system (sUAS), provides unobstructed measurements by hovering above the surf zone at 20 m elevation while scanning surfaces along a 150 m-wide cross-shore transect. The drone enables rapid data collection in remote locations where terrestrial scanning may not be possible. Allowing for battery changes, about 17 minutes of scanning data can be acquired every 25 minutes for several hours. Observations were collected with a wide (Hs = 2.2 m) and narrow (Hs = 0.8 m) surf zone, and are validated with traditional land-based survey techniques and an array of buried pressure sensors. Thorough post-processing yields a stable ( = 1.7 cm) back beach topography estimate comparable to the terrestrial LiDAR ( = 0.8 cm). Statistical wave properties and runup values are calculated, as well as bathymetry inversions using a relatively simple nonlinear correction to wave crest phase speed in the surf zone, illustrating the utility of drone-based LiDAR observations for nearshore processes.

Drivers and impact of the seasonal variability of the organic carbon offshore transport in the Canary upwelling system

The Canary upwelling system (CanUS) is a productive coastal region characterized by strong seasonality and an intense offshore transport of organic carbon (Corg) to the adjacent oligotrophic offshore waters. There, the respiration of this Corg substantially modifies net community production (NCP). While this transport and the resulting coupling of the biogeochemistry between the coastal and open ocean has been well studied in the annual mean, the temporal variability, and especially its seasonality, has not yet been investigated. Here, we determine the seasonal variability of the offshore transport of Corg, its mesoscale component, latitudinal differences, and the underlying physical and biological drivers. To this end, we employ the Regional Ocean Modeling System (ROMS) coupled to a nutrient–phytoplankton–zooplankton–detritus (NPZD) ecosystem model. Our results reveal the importance of the mesoscale fluxes and of the upwelling processes (coastal upwelling and Ekman pumping) in modulating the seasonal variation of the offshore Corg transport. We find that the region surrounding Cape Blanc (21∘ N) hosts the most intense Corg offshore flux in every season, linked to the persistent, and far reaching Cape Blanc filament and its interaction with the Cape Verde Front. Coastal upwelling filaments dominate the seasonality of the total offshore flux up to 100 km from the coast, contributing in every season at least 80 % to the total flux. The seasonality of the upwelling modulates the offshore Corg seasonality hundreds of kilometers from the CanUS coast via lateral redistribution of nearshore production. North of 24.5∘ N, the sharp summer–fall peak of coastal upwelling results in an export of more than 30 % of the coastal Corg at 100 km offshore due to a combination of intensified nearshore production and offshore fluxes. To the south, the less pronounced upwelling seasonality regulates an overall larger but farther-reaching and less seasonally varying lateral flux, which exports between 60 % and 90 % of the coastal production more than 100 km offshore. Overall, we show that the temporal variability of nearshore processes modulates the variability of Corg and NCP hundreds of kilometers offshore from the CanUS coast via the offshore transport of the nearshore production.

Integrating Seafloor Mapping Data with Sediment Transport and Coastal Change Studies: Preliminary Results from the Southern Gulf of Maine, USA

<p>Coastal erosion, intense storm events and sea-level rise pose threats to coastal communities and infrastructure. Managers and scientists often lack the high-resolution data needed to improve estimates of sediment abundance and movement, shoreline change, substrate heterogeneity and other seabed characteristics that influence coastal vulnerability. To address these and other needs the U.S. Geological Survey is conducting a multi-tiered research initiative consisting of shoreline change characterization, sediment transport numerical modeling and seafloor mapping in Cape Cod Bay, Massachusetts, USA. Here we present the seafloor mapping findings and their applications to an integrated coastal change analysis. Our comprehensive seafloor mapping technique includes the collection of multibeam and phase-discriminating data, seismic-reflection profile data, sediment samples, seabed imagery, as well as the synthesis of regional legacy datasets. A first-order comparison of the interdisciplinary results indicates that the presence of seafloor bedforms and the thickness of Late Holocene sediments correspond to patterns of modeled seabed elevation change and observed relative coastline stability. Analyses of these data are ongoing and may further resolve the relationships among shoreline change, nearshore processes and antecedent geology.</p>

Drivers and impact of the seasonal variability of the organic carbon offshore transport in the Canary Upwelling System

The Canary Upwelling System (CanUS) is a productive coastal region characterized by strong seasonality and an intense offshore transport of organic carbon (Corg) to the adjacent oligotrophic offshore waters. There, the respiration of this Corg substantially modifies net community production (NCP). While this transport and the resulting coupling of the biogeochemistry between the coastal and open ocean has been well studied in the annual mean, the temporal variability, and especially its seasonality has not yet been investigated. Here, we fill this gap, and determine the seasonal variability of the offshore transport of Corg, its mesoscale component, latitudinal differences, and the underlying physical and biological drivers. To this end, we employ the Regional Ocean Modeling System (ROMS) coupled to a nutrient, phytoplankton, zooplankton, and detritus (NPZD) ecosystem model. Our results reveal the importance of the mesoscale fluxes and of the upwelling processes (coastal upwelling and Ekman pumping) in modulating the seasonal variation of the offshore Corg transport. We find that the region surrounding Cape Blanc (21° N) hosts the most intense Corg offshore flux in every season, linked to the persistent, and far reaching Cape Blanc filament. Coastal upwelling filaments dominate the seasonality of the total offshore flux up to 100 km from the coast, contributing in every season season at least 80 % to the total flux. The seasonality of the upwelling modulates the offshore Corg seasonality hundreds of km from the CanUS coast via lateral redistribution of nearshore production. North of 24.5° N, the sharp summer-fall peak of coastal upwelling results in an export of more than 30 % of the coastal Corg at the 100 km offshore due to a combination of intensified nearshore production and offshore fluxes. To the south, the less pronounced upwelling seasonality regulates an overall larger, but farther-reaching and less seasonally varying lateral flux, which exports between 60 and 90 % of the coastal production more than 100 km offshore. Overall, we show that the temporal variability of nearshore processes impacts the variability of Corg and NCP hundreds of km offshore from the CanUS coast via the offshore transport of the nearshore production.

An Overview of the Expected Shoreline Impact of the Marine Energy Farms Operating in Different Coastal Environments

The aim of the present work is to provide an overview of the possible implications involving the influence of a generic marine energy farm on the nearshore processes. Several case studies covering various European coastal areas are considered for illustration purposes. These include different nearshore areas, such as the Portuguese coast, Sardinia Island or a coastal sector close to the Danube Delta in the Black Sea. For the case studies related to the Portuguese coast, it is noted that a marine energy farm may reduce the velocity of the longshore currents, with a complete attenuation of the current velocity for some case studies in the coastal area from Leixoes region being observed. For the area located close to the Danube Delta, it is estimated that in the proposed configuration, a marine energy farm would provide an efficient protection against the wave action, but it will have a relatively negligible impact on the longshore currents. Summarizing the results, we can conclude that a marine energy farm seems to be beneficial for coastal protection, even in the case of the enclosed areas, such as the Mediterranean or Black seas, where the erosion generated by the wave action represents a real problem.

Post-storm recuperation as a stepping-stone towards long-term integrated modelling in steep beaches

<p>Integrated modelling approaches for the evolution of the entire dune-beach system have become increasingly sought-after, not only for management purposes, but also to allow better understanding of the feedbacks between processes and scales and a closer approximation of where critical system thresholds may lie. The effective reproduction of both destructive and constructive processes over a broad spectrum of temporal scales is crucial to any, such, integrated approach. Recent improvements of the XBeach-Duna model regarding approximation of nearshore processes were tested using in-situ data from the Emma storm impacts on a reflective beach (Praia de Faro, in S. Portugal). The model results compare well with measured post-storm and recovered profiles, showing high model skill under both erosive and constructive regimes. Building from this event-scale analysis, a gradual increase of temporal windows in simulated forcing conditions, through wave schematisation, is presented and discussed in terms of optimisation between gains in simulation time and losses in geomorphic change information. This methodological approach and findings are the basis that will allow passing on to dependable, long-term simulations of the beach-dune system evolution.</p><p> </p><p><em>Acknowledgements: The work was implemented in the framework of the ENLACE project (ref. 28949 FEDER), funded by FCT (Fundação para a Ciência e a Tecnologia)</em></p>

Exploiting satellite technology and machine learning to describe and predict hazardous shoreline change

<p>East Anglia is particularly vulnerable to sea level rise, increases in storminess, coastal erosion, and coastal flooding. Critical national infrastructure (including Sizewell’s nuclear power stations and the Bacton gas terminals), population centres close to the coastal zone (> 600,000 in Norfolk and Suffolk) and iconic natural habitats (the Broads, attracting 7 million visitors a year) are under threat. Shoreline change, driven by complex interactions between environmental forcing factors and human shoreline modifications, is a key determinant of coastal vulnerability and exposure; its prediction is imperative for future coastal risk adaptation.</p><p>An automated, python-supported, tool has been developed to simultaneously extract the water and vegetation line from satellite imagery. PlanetLab multispectral optical imagery is used to provide multi-year, frequent (up to fortnightly) images with 3-5m spatial resolution. Net shoreline change (NSC) has been calculated along multiple stretches of the East Coast of England, most notably for areas experiencing varying rates of change in front of, and adjacent to, ‘hard’ coastal defences. The joint use of water and vegetation line proxies enables calculation of inter-tidal width variability alongside NSC. The image resolution used provides new opportunities for data-led approaches to the monitoring of shoreline response to storm events and/or human shoreline modification.</p><p>Artificial Neural Networks (ANN) have been trained to predict shoreline evolution until 2040. Early results are presented, alongside considerations surrounding data pre-processing and input parameter selection requirements. Training data comprises decadal-scale shoreline positions recovered using automated shoreline detection. Shoreline position, alongside databases of nearshore bathymetry, sea defences, artificial beach renourishment, nearshore processes (wave and tide gauge data, meteorological fields), combined with land cover, population and infrastructure data act as inputs. Optimal input filtering and ANN configuration is derived using hindcasts.</p><p>The research is timely; ANN predictions are compared with the Anglian Shoreline Management Plans (SMPs), which identify locations at greatest risk and assign future risk management funding. The findings of this research will feed into future revisions of the plans.</p>