scholarly journals Thresholds in road network functioning on US Atlantic and Gulf barrier islands

2021 ◽  
Author(s):  
Sofia Aldabet ◽  
Evan Goldstein ◽  
Eli Lazarus
Keyword(s):  
Road Network ◽  
Management Practices ◽  
Gulf Coast ◽  
Barrier Island ◽  
Barrier Islands ◽  
Water Levels ◽  
Exceedance Probability ◽  
Infrastructure Networks ◽  
Island Dynamics ◽  
Forward Looking

Barrier islands predominate the Atlantic and Gulf coastlines of the USA, where development exceeds national trends. Forward-looking models of barrier island dynamics often include feedbacks with management practices – particularly those aimed at mitigating damage to buildings from natural hazards – and how real estate markets may be linked to barrier island dynamics. However, models thus far do not account for networks of infrastructure, such as roads, and how the functioning of infrastructure networks might influence management strategies. Understanding infrastructure networks on barrier islands is an essential step toward improved insight and foresight into the future dynamics of human-altered barriers. Here, we examine thresholds in the functioning of 72 US Atlantic and Gulf Coast barrier islands. We use digital elevation models to assign an elevation to each intersection in each road network. From each road network we sequentially remove intersections, starting from the lowest elevation. In each network we identify a critical intersection – and corresponding elevation – at which the functioning of the network fails, and we match the elevation of each critical intersection to local annual exceedance probabilities for extreme high-water levels. We find a range of failure thresholds for barrier island road network functioning, and also find that no single metric – absolute elevation, annual exceedance probability, or a quantitative metric of robustness – sufficiently ranks the susceptibility of barrier road networks to failure. Future work can incorporate thresholds for road network into forward-looking models of barrier island dynamics that include hazard-mitigation practices for protecting infrastructure.

scholarly journals PRE- and POST-STORM LiDAR SURVEYS FOR ASSESSMENT OF IMPACT ON COASTAL EROSION

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 261-266
Author(s):  
A.-L. Montreuil ◽  
M. Chen ◽  
A. Esquerré ◽  
R. Houthuys ◽  
R. Moelans ◽  
...  
Keyword(s):  
Management Practices ◽  
Morphological Changes ◽  
Storm Surges ◽  
High Water ◽  
Water Levels ◽  
Airborne Lidar ◽  
Coastline Change ◽  
The Mean ◽  
The Impact

<p><strong>Abstract.</strong> Sustainable management of the coastal resources requires a better understanding of the processes that drive coastline change. The coastline is a highly dynamic sea-terrestrial interface. It is affected by forcing factors such as water levels, waves, winds, and the highest and most severe changes occur during storm surges. Extreme storms are drivers responsible for rapid and sometimes dramatic changes of the coastline. The consequences of the impacts from these events entail a broad range of social, economic and natural resource considerations from threats to humans, infrastructure and habitats. This study investigates the impact of a severe storm on coastline response on a sandy multi-barred beach at the Belgian coast. Airborne LiDAR surveys acquired pre- and post-storm covering an area larger than 1 km<sup>2</sup> were analyzed and reproducible monitoring solutions adapted to assess beach morphological changes were applied. Results indicated that the coast retreated by a maximum of 14.7 m where the embryo dunes in front of the fixed dunes were vanished and the foredune undercut. Storm surge and wave attacks were probably the most energetic there. However, the response of the coastline proxies associated with the mean high water line (MHW) and dunetoe (DuneT) was spatially variable. Based on the extracted beach features, good correlations (r>0.73) were found between coastline, berm and inner intertidal bar morphology, while it was weak with the most seaward bars covered in the surveys. This highlights the role of the upper features on the beach to protect the coastline from storm erosion by reducing wave energy. The findings are of critical importance in improving our knowledge and forecasting of coastline response to storms, and also in its translation into management practices.</p>

scholarly journals Modeling Barrier Island Habitats Using Landscape Position Information

2019 ◽  
Vol 11 (8) ◽  
pp. 976
Author(s):  
Nicholas M. Enwright ◽  
Lei Wang ◽  
Hongqing Wang ◽  
Michael J. Osland ◽  
Laura C. Feher ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Barrier Island ◽  
Barrier Islands ◽  
Machine Learning Algorithms ◽  
Landscape Position ◽  
K Nearest Neighbor ◽  
Island Habitats

Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.

scholarly journals ADAPTATION PATHWAY FOR A BARRIER ISLAND TO FUTURE HURRICANES

2018 ◽  
pp. 52
Author(s):  
Stephanie Smallegan ◽  
Evan Mazur
Keyword(s):  
Storm Surge ◽  
Atlantic Coast ◽  
Barrier Island ◽  
Hurricane Sandy ◽  
Water Levels ◽  
Storm Damage ◽  
Sea Levels ◽  
The North ◽  
Adaptation Pathway ◽  
Comprehensive Study

The numerical model XBeach is used to simulate hydrodynamics and morphological change of Bay Head, NJ, which is located on a developed barrier island. Bay Head is fronted with a seawall buried beneath its dunes, and the seawall has been shown to mitigate damage due to storm surge and waves during Hurricane Sandy (2012). The objective of this study is to re-evaluate the effectiveness of the seawall in mitigating damage from a synthetic storm and sea level rise, and refine an adaptation pathway previously created for Bay Head. Utilizing the wave and surge data generated from the North Atlantic Coast Comprehensive Study, synthetic Storm 391 is simulated using XBeach. Model results show the seawall is overtopped by storm surge and waves, causing overwash and reducing dune heights. As sea levels rise, the backbarrier region of the barrier island is severely eroded and the seawall acts as a barrier preventing elevated bay water levels from freely flowing across the island and into the ocean, exacerbating sediment transport on the backbarrier. To fully evaluate the capabilities and limitations of the seawall in mitigating storm damage, additional synthetic storms need to be simulated and the results re-evaluated. This will, in turn, lead to a comprehensive, more robust adaptation pathway for Bay Head.

scholarly journals Simulating barrier island response to sea level rise with the barrier island and inlet environment (BRIE) model v1.0

2019 ◽  
Vol 12 (9) ◽  
pp. 4013-4030 ◽  
Author(s):  
Jaap H. Nienhuis ◽  
Jorge Lorenzo-Trueba
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Barrier Island ◽  
Barrier Islands ◽  
Emergent Behavior ◽  
Tidal Inlets ◽  
Rise Rate ◽  
Ecological Importance ◽  
Alongshore Sediment Transport

Abstract. Barrier islands are low-lying coastal landforms vulnerable to inundation and erosion by sea level rise. Despite their socioeconomic and ecological importance, their future morphodynamic response to sea level rise or other hazards is poorly understood. To tackle this knowledge gap, we outline and describe the BarrieR Inlet Environment (BRIE) model that can simulate long-term barrier morphodynamics. In addition to existing overwash and shoreface formulations, BRIE accounts for alongshore sediment transport, inlet dynamics, and flood–tidal delta deposition along barrier islands. Inlets within BRIE can open, close, migrate, merge with other inlets, and build flood–tidal delta deposits. Long-term simulations reveal complex emergent behavior of tidal inlets resulting from interactions with sea level rise and overwash. BRIE also includes a stratigraphic module, which demonstrates that barrier dynamics under constant sea level rise rates can result in stratigraphic profiles composed of inlet fill, flood–tidal delta, and overwash deposits. In general, the BRIE model represents a process-based exploratory view of barrier island morphodynamics that can be used to investigate long-term risks of flooding and erosion in barrier environments. For example, BRIE can simulate barrier island drowning in cases in which the imposed sea level rise rate is faster than the morphodynamic response of the barrier island.

Shoreline Analysis and Barrier Island Dynamics: Decadal Scale Patterns from Cedar Island, Virginia

2012 ◽  
Vol 280 ◽  
pp. 332-341 ◽  
Author(s):  
Stephanie H. Nebel ◽  
Arthur C. Trembanis ◽  
Donald C. Barber
Keyword(s):  
Barrier Island ◽  
Decadal Scale ◽  
Cedar Island ◽  
Island Dynamics

Salinity and the small-scale distribution of three barrier island shrubs

1994 ◽  
Vol 72 (9) ◽  
pp. 1365-1372 ◽  
Author(s):  
Donald R. Young ◽  
David L. Erickson ◽  
Shawn W. Semones
Keyword(s):  
Water Relations ◽  
Barrier Island ◽  
Barrier Islands ◽  
Small Scale ◽  
Similar Response ◽  
Groundwater Salinity ◽  
Baccharis Halimifolia ◽  
Myrica Cerifera ◽  
Interspecific Differences ◽  
Iva Frutescens

The importance of salinity to small-scale distribution patterns was examined for three shrubs common on barrier islands of the southeastern United States. Field measurements focused on the salt marsh – upland interface zone on Hog Island, Virginia, where Myrica cerifera, Baccharis halimifolia, and Iva frutescens form distinct distributional zones. Although considerable variation in salinity occurred throughout the growth season (June through October), total soil chlorides and groundwater salinity were lowest for M. cerifera, intermediate for B. halimifolia, and highest for I. frutescens. All three species showed similar diurnal and seasonal patterns in stomatal conductance and leaf xylem pressure potential, despite the differences in salinity. However, a laboratory experiment revealed interspecific differences in water relations when the three shrubs were exposed to identical salinity regimes. The field data and water relations experiment indicated M. cerifera is least tolerant to salinity, I. frutescens is most tolerant, and B. halimifolia is intermediate. Seed germination experiments revealed a similar response, except that B. halimifolia was more sensitive to salinity than M. cerifera. The interspecific differences in soil and groundwater salinity, along with the physiological response differences, indicated that salinity may be one of the major environmental factors influencing zonation among the three shrubs; however, the absence of I. frutescens and B. halimifolia in low salinity areas implied that other factors also influence zonation patterns on barrier islands. Key words: Baccharis halimifolia, Iva frutescens, Myrica cerifera, barrier island, salinity tolerance, shrub.

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