Tool For Coastal Management Research Articles

COAST-PROSIM: A Model for Predicting Shoreline Evolution and Assessing the Impacts of Coastal Defence Structures

Coastal zones, at the interface between land and sea, face increasing challenges from erosion, sea-level rise, and anthropogenic interventions, necessitating innovative tools for effective management and protection. This study introduces COAST-PROSIM, a novel numerical model specifically designed to predict shoreline evolution and assess the impacts of coastal defence structures on coastal morphology. Unlike existing models that often face a trade-off between computational efficiency and physical accuracy, COAST-PROSIM balances these demands by integrating two-dimensional wave propagation routines with advanced shoreline evolution equations. The model evaluates the effects of interventions such as breakwaters and groynes, enabling simulations of shoreline dynamics with reduced computational effort. By using high-resolution input data, COAST-PROSIM captures the interplay between hydrodynamics, sediment transport, and structural impacts. Tested on real-world case studies along the coasts of San Leone, Porto Empedocle, and Villafranca Tirrena, the model demonstrates its adaptability to diverse coastal environments. The results highlight its potential as a reliable tool for sustainable coastal management, allowing stakeholders to anticipate long-term changes in coastal morphology and design targeted mitigation strategies.

EDITORIAL

With great enthusiasm, we present Volume 57, Issue 1, of the journal Arquivos de Ciências do Mar, reaffirming the commitment of this publication to the dissemination of highquality scientific research in the fields of oceanography, marine ecology, and coastal studies. In this volume, we have assembled a diverse selection of 10 original articles, 3 critical reviews, and 1 scientific note that offer significant contributions to the understanding and management of marine and coastal ecosystems, with a special focus on biodiversity and the socioenvironmental challenges of tropical and subtropical regions. The original articles in this issue explore emerging and multidisciplinary topics, including fungal contamination on beaches and its impact on environmental quality, the development of tools to assess ocean literacy, and studies on environmental perception as a tool for coastal management. We also highlight research on the gametogenic cycle of Lytechinus variegatus, population dynamics modeling of Ucides cordatus, and advances in understanding the ontogenetic development of otoliths in coastal fish. Additionally, this volume includes systematic reviews that expand knowledge about the Serranidae family in the Atlantic and the life history parameters of snapper species (Lutjanidae), as well as a comprehensive analysis of bycatch of non-target species in Brazil, a crucial topic for marine conservation. In the scientific notes, we highlight contributions to the knowledge of length-weight relationships of threatened shark species from the Brazilian Amazon coast, underscoring the importance of baseline data to support conservation and management strategies. This issue reflects the dedication of researchers and reviewers, as well as the commitment of the editorial team to ensuring the quality and relevance of the published works. We hope the contents presented here inspire new research, promote knowledge exchange, and support informed decision- aking for the conservation and sustainable use of marine resources. We thank the authors for sharing their findings and the readers for their trust in our publication. We invite everyone to explore this issue and actively participate in the scientific dialogue it fosters. Enjoy your reading!

Advancing sea level anomaly modeling in the black sea with LSTM Auto-Encoders: A novel approach

Rising sea levels pose significant risks to coastal communities and ecosystems. Accurate modeling of sea level changes is crucial for effective environmental management and disaster mitigation. Machine learning methods are emerging as an important asset in improving sea level predictions and understanding the impacts of climate change. Especially, Long Short-Term Memory (LSTM) models have emerged as a powerful tool for sea level anomaly modeling, but there is an increasing need for more advanced models in this area. This study enhances existing methodologies by introducing a novel approach using an LSTM Auto-Encoder model, designed to compress input data into a lower-dimensional latent space before reconstructing it, thereby capturing complex temporal dependencies and anomalies effectively. We compared LSTM Auto-Encoder model performance with that of a Stacked LSTM network, which learns complex temporal patterns through multiple layers, and a traditional damped-persistence statistical model. Our results demonstrate that the LSTM Auto-Encoder model not only outperformed these models in predicting sea level anomalies across various lead times but also exhibited superior generalization capabilities across both satellite altimeter and in-situ data. These findings highlight the potential of the LSTM Auto-Encoder model as a powerful tool in coastal management and climate change studies, underscoring the critical role of advanced machine learning techniques in enhancing our predictive abilities and informing disaster preparedness strategies.

Mapping coastal resilience: a Gis-based Bayesian network approach to coastal hazard identification for Queensland’s dynamic shorelines

Coastal regions worldwide face increasing threats from climate change-induced hazards, necessitating more accurate and comprehensive vulnerability assessment tools. This study introduces an innovative approach to coastal vulnerability assessment by integrating Bayesian Networks (BN) with the modern coastal vulnerability (CV) framework. The resulting BN-CV model was applied to Queensland's coastal regions, with a particular focus on tide-modified and tide-dominated beaches, which constitute over 85% of the studied area. The research methodology involved beach classification based on morphodynamic characteristics, spatial subdivision of Queensland's coast into 78 sections, and the application of the BN-CV model to analyze interactions between geomorphological features and oceanic dynamics. This approach achieved over 90% accuracy in correlating beach types with vulnerability factors, significantly outperforming traditional CVI applications. Key findings include the identification of vulnerability hotspots and the creation of detailed exposure and sensitivity maps for Gold Coast City, Redland City, Brisbane City, and the Sunshine Coast Regional area. The study revealed spatial variability in coastal vulnerability, providing crucial insights for targeted management strategies. The BN-CV model demonstrates superior precision and customization capabilities, offering a more nuanced understanding of coastal vulnerability in regions with diverse beach typologies. This research advocates for the adoption of the BN-CV approach to inform tailored coastal planning and management strategies, emphasizing the need for regular reassessments and sustained stakeholder engagement to build resilience against climate change impacts.Recommendations include prioritizing adaptive infrastructure in high-exposure areas like the Gold Coast, enhancing flood management in Brisbane, improving socio-economic adaptive capacity in Redland, and maintaining natural defences in Moreton Bay. This study contributes significantly to the field of coastal risk management, providing a robust tool for policymakers and coastal managers to develop more effective strategies for building coastal resilience in the face of climate change.

On the shoreline monitoring via earth observation: An isoradiometric method

Shoreline variations, triggered by climate change, eustatism, and tectonic, drive the coastal landscape evolution over multiple spatial and temporal scales. Among the many different existing coast types, sandy coasts are the most sensitive to coastal erosion and accretion processes and, at the same time, often host valuable anthropogenic assets. The rapid and ongoing evolution of these coastal environments poses challenges for their management, necessitating cost-effective and highly reliable methods for measuring these changes. Many remotely sensed shoreline extraction methods have been proposed in the literature, providing valuable tools for improving coastal management. Even if these methodologies allow the demarcation of the shoreline, its pixelated shape usually requires refinement through subsequent smoothing or vector generalization processes. It is important to note that the position of the thus extracted coastline is not a direct result of a measured physical quantity but rather a product of these refinement techniques. To address this problem, we developed a sub-pixel resolution method for extracting shorelines from remotely sensed images of sandy beaches, leveraging the radiometric signature of the shoreline. Validated through precise Global Navigation Satellite System field surveys for positioning the beach foreshore, this method was successfully applied to three beaches in Sicily, in the central Mediterranean, all exhibiting similar microtidal conditions. Its robust design allows for application across various satellite images, employing a straightforward radiometric interpolation method adaptable to different spatial resolutions. This method would be a valuable tool for coastal managers in detecting and mitigating coastal erosion and developing and maintaining anthropogenic coastal assets.

Application of the Ocean Health Index to assess ocean and coastal conditions of the Tokyo Metropolitan area

Ocean ecosystem services are fundamental to coastal economic development and climate regulation. The Tokyo Metropolitan Area (TMA) is one of the world’s most significant bay areas, with its economic development and residents relying on a healthy ocean and diverse marine ecosystem services. Post-World War II, the coastal zones of TMA have suffered from various human-induced stresses and impacts, jeopardizing the capacity of ocean ecosystem services and sustainable development. Although recent years have seen improvements in basic marine water quality, other ecosystem health issue, such as biodiversity, carbon storage, coastal protection are remain limited and require comprehensive enhancement. To better understand the current state of ocean health, promote more comprehensive ocean management decisions and plans, and support sustainable development in the region, it is essential to evaluate the ocean’s condition in the TMA and to assess future trends and risks. The Ocean Health Index (OHI) offers a comprehensive framework to evaluate global and regional ocean health conditions and their ecosystem services. This study applies the OHI to assess the ocean and coastal health of TMA, with indicators and reference points adjusted based on local conditions. From 2014 to 2019, the OHI assessment results indicate that TMA obtained an overall OHI score of 49 (out of 100), with goals ranging from 13 to 87. The environmental and social goals, such as Tourism and Recreation (87), Clean Water (67), Coastal Livelihoods and Economies (62), Sense of Place (62), and Artisanal Fishery Opportunity (51), are relatively high. In contrast, the ecological goals, such as Biodiversity (14), Carbon Storage (13), Coastal Protection (38), and Food Provision (24), are relatively low. This case study highlights strategies for improving ocean health conditions in TMA from both OHI goals and management structure perspectives. The methods and results provide a quantitative tool for regional marine and coastal management in TMA, aiding regional management organizations to establish quantifiable objectives for actions that were previously difficult to quantify. These results can indirectly promote the establishment of more comprehensive and holistic regional management organizations in TMA, which is currently fragmented, thereby effectively enhancing ocean health in TMA.

Initial Assessment of the Benthic Profile and Reef Fish Composition of the Damilisan Marine Sanctuary, Miagao, Iloilo, Philippines

Coral reefs are among the world’s most diverse and productive ecosystems. They provide valuable ecosystem services, including support for fisheries production and the livelihood of coastal communities. However, coral reefs are also threatened by anthropogenic and climate stressors. Establishing a marine protected area (MPA) is a typical coastal management tool widely used in the Philippines for conserving coral reefs and managing the fishing effort. This study presents initial information on the benthic and fish community profiles of the Damilisan Marine Sanctuary (DMS) in Miagao, Iloilo, Philippines. A modified point-intercept transect method was used to characterize the benthic profile, while the underwater visual census (UVC) was used to determine the fish community profile (diversity and density). The substrate was mainly composed of hard coral (HC) (39.3±18.3%), and the common HC observed were massive Porites and Goniopora, each contributing 12.8% of the total HC cover. Twenty coral reef fish species belonging to 10 families were identified during the UVC and had an overall density of 99±69 ind./1000 m2. Damselfishes (Family Pomacentridae) was the most abundant family (23±0.4 ind./500 m2), while Lutjanus semicinctus was the most abundant species (8.5±1.4 ind./500 m2). It is recommended that regular coral reef monitoring surveys be conducted in the DMS and in other MPAs in Miagao to monitor trends and changes in the HC cover and reef fish density and diversity in the MPA.

Initial Assessment of the Benthic Profile and Reef Fish Composition of the Damilisan Marine Sanctuary, Miagao, Iloilo, Philippines

Coral reefs are among the world’s most diverse and productive ecosystems. They provide valuable ecosystem services, including support for fisheries production and the livelihood of coastal communities. However, coral reefs are also threatened by anthropogenic and climate stressors. Establishing a marine protected area (MPA) is a typical coastal management tool widely used in the Philippines for conserving coral reefs and managing the fishing effort. This study presents initial information on the benthic and fish community profiles of the Damilisan Marine Sanctuary (DMS) in Miagao, Iloilo, Philippines. A modified point-intercept transect method was used to characterize the benthic profile, while the underwater visual census (UVC) was used to determine the fish community profile (diversity and density). The substrate was mainly composed of hard coral (HC) (39.3±18.3%), and the common HC observed were massive Porites and Goniopora, each contributing 12.8% of the total HC cover. Twenty coral reef fish species belonging to 10 families were identified during the UVC and had an overall density of 99±69 ind./1000 m2. Damselfishes (Family Pomacentridae) was the most abundant family (23±0.4 ind./500 m2), while Lutjanus semicinctus was the most abundant species (8.5±1.4 ind./500 m2). It is recommended that regular coral reef monitoring surveys be conducted in the DMS and in other MPAs in Miagao to monitor trends and changes in the HC cover and reef fish density and diversity in the MPA.

Investigation of the applicability of the microwave image to complement the optical image for the preparation of a coastal configuration map

The coastal area, being the transition zone between the sea and land, is a country's most diverse and dynamic zone in terms of its environment and economy. A variety of economic activities revolve around this region. Mapping the dynamic coastal configuration is an essential tool for sustainable coastal zone management. Satellite remote sensing offers excellent resources for monitoring and mapping coastal zones. Microwave remote sensing images, in tandem with optical remote sensing imagery, can be used for mapping coastal configurations. However, both images have some limitations. In this context, the present article explores the integrated use of microwave images to supplement optical images to overcome these limitations and generate a coastline configuration map. Res. Agric. Livest. Fish. Vol. 11, No. 1, April 2024: 47-51

Assessment of Ecosystem Services Provided by Macrophytes in Southern Baltic and Southern Mediterranean Coastal Lagoons

The ecological importance of macrophytes is well known and reflected in nature protection law, for example, as a key biological quality element. However, the socio-economic role, such as the impact of macrophyte presence on recreational activities, is often overlooked. The purpose of this study was to assess the human benefits (or ecosystem services) provided by macrophytes. We developed a list of 25 macrophyte ecosystem services and 79 assessment indicators based on expert knowledge and literature data. First, hypothetical scenarios of coastal lagoons were developed to assess the impact of different ecological states (i.e., macrophyte coverage) and management measures (i.e., fisheries) on the ecosystem service provision. Scenario assessments were carried out by stakeholder workshops and literature search. Second, the ecosystem service potential of submerged and emergent macrophyte habitats were assessed by macrophyte experts and literature data. Results showed that cultural services are most important in terms of the overall actual provision of ecosystem services (scenario assessment) but also showing highest potential of the hypothetical ecosystem service provision (habitat assessment). Highest overall potential is shown for reeds and tall forb communities (83 out of maximum 125), followed by seagrass beds (71) and seaweed communities (61). Our ecosystem service assessment approaches (i.e., scenario and habitat-based) using socio-cultural data (i.e., stakeholders and experts-based) and biophysical data (i.e., indicators-based) can serve as supportive tools for coastal management and policy implementation visualizing the benefits of macrophytes to humans.

Assessing Shallow Soft Deposits through Near-Surface Geophysics and UAV-SfM: Application in Pocket Beaches Environments

This study employs a multimethod approach to investigate the sediment distribution in two pocket beaches, Ramla Beach and Mellieha S Beach, in Malta. Both study sites were digitally reconstructed using unmanned aerial vehicle (UAV) photogrammetry. For each case, an ERT and a dense network of ambient seismic noise measurements processed through a horizontal-to-vertical spectral ratio (HVSR) technique were acquired. Electrical resistivity tomography (ERT) analysis enables the estimation of sediment thickness in each beach. HVSR analysis revealed peaks related to beach sediments overlying limestone rocks in both sites and also indicated a deeper stratigraphic contact in Mellieha S Beach. Based on ERT measurements, sediment thickness is calculated for each HVSR measurement. Interpolation of results allows for bedrock surface modelling in each case study, and when combined with digital terrain models (DTMs) derived from photogrammetric models, sediment volumes are estimated for each site. The geometry of this surface is analyzed from a geological perspective, showing structural control of sediment distribution due to a normal fault in Mellieha S Beach and stratigraphic control facilitated by a highly erodible surface in Ramla Beach. The results emphasize the importance of adopting a three-dimensional perspective in coastal studies for precise sediment volume characterization and a deeper understanding of pocket beach dynamics. This practical multimethod approach presented here offers valuable tools for future coastal research and effective coastal management, facilitating informed decision making amidst the growing vulnerability of coastal zones to climate change impacts.

Improvement of an extended ensemble coupled data Assimilation–Forecast system and its application in El Niño diversity predictions

The El Niño–Southern Oscillation (ENSO) can cause climate anomalies on a global scale, and further affect human life and activities in coastal zones. Therefore, its forecast is of great significance for early disaster warning and coastal management. However, the frequent occurrence of central Pacific (CP) El Niño events increases the diversity and complexity of ENSO, severely reducing its prediction efficiency. In this study, an extended ensemble coupled data assimilation–forecast system was employed to investigate the prediction of different types of El Niño events, including eastern Pacific (EP) and CP events. The extended system was based on the fifth-generation Lamont–Doherty Earth Observation (LDEO5) model, in which an advanced ensemble Kalman filter was used to construct a multisource data assimilation system, and a stochastic optimal method was used to measure the influence of atmospheric stochastic processes on model prediction errors. The extended system was used to predict two types of El Niño events that occurred between January 1950 and December 2018. The results showed that the extended system was generally able to predict EP events with a higher accuracy than CP events for all lead times. The extended system successfully predicted the mature phase of EP events up to 12 months in advance but could only predict the mature phase of CP events up to 6 months in advance. The extended system was also able to depict the evolution of both EP and CP events, although the sea surface temperature anomalies were underestimated. The extended system not only provides a useful platform for improving ENSO prediction accuracies in association with El Niño diversity but also provides an important tool for disaster early warning and coastal management.

A dynamic energy budget model for black rockfish Sebastes schlegelii: Parameterization and application in marine ranching areas, Yellow Sea, China

Marine ranching has become a powerful tool for coastal habitat restoration, ecosystem conservation and stock enhancement. With increasing development of marine ranching in recent years, it has attracted wide attentions for fishery management and sustainability. The stock of black rockfish Sebastes schlegelii has undergone a serious decline due to overfishing in the past few decades, but has considerably recovered recently in marine ranching areas along the coasts of the Northwest Pacific. Appropriate management of the species is essential for marine ranching development. In this study, a dynamic energy budget (DEB) model of S. schlegelii was developed and applied in the Xixiakou, Changdao and Qiansandao marine ranching areas, Yellow Sea, China. The effects of food availability and water temperature on its growth and reproduction were quantified. The model was parameterised following the AmP procedure, which resulted in mean relative error of 0.123 and symmetric mean square errors of 0.134. The application of the model has showed that the model could reproduce the growth trajectories of both length and weight at each of the three marine ranching areas without site-specific calibration. The growth rates of S. schlegelii showed that Qiansandao > Changdao > Xixiakou in both length and weight. Temperature and food availability are the main contribution to the variations of growth amongst sites. In Qiansandao, the fast growth and maturation of the species correspond to relative high water temperature and sufficient food availability. Food availability limited growth and reproduction of S. schlegelii in Xixiakou, which indicated that the stock density in the areas might be close to the carrying capacity. The earlier maturation was a result of higher temperature and more food supply in Qiansandao than in Changdao and Xixiakou. Fishing and recapture in marine ranching areas should be adjusted to flexibly avoid spawning periods. The presented model is applicable to other marine ranching areas and coastal habitats. It provides an important component of ecosystem management approaches to estimate the ecological carrying capacities. This study provides information about the stock enhancement potential of coastal environments and a powerful tool for sustainable coastal fisheries management.

The Sector Analysis as a Coastal Management Tool for Sustainable Tourism Development on the Mediterranean Coast of Morocco

Beaches are ecologically valuable ecosystems and sites that attract many tourists from all over the world, therefore, knowledge of their environmental conditions to establish sound management strategies is of extreme relevance. This study aims to assess and classify 50 beaches through an innovative coastal management approach called “Sector Analysis”, which integrates Litter grading, the coastal scenic quality and beach typology in order to classify sites into one of three sectors: Green (high value sites), Red (low value sites) and Yellow (sites with contradictory values). Litter Grade makes it possible to classify a site according to the quantity of litter using four grades from “A” (low) to “D” (high amount). The Coastal Scenic Evaluation System (CSES) allows to classify sites into five classes, ranging from extremely attractive natural sites (Class I) to unattractive degraded and urbanized sites (Class V). This innovative methodology for sustainable coastal area management can be easily applied to any beach in the world. The results on Litter Grade and CSES considered in this paper were obtained from previous studies and used to obtain the Sector Analysis that showed only 8 sites (16%) are in the Green sector, 18 sites (36%) in the Red sector and 24 sites (48%) in the Yellow sector. The significant percentage of sites in the Red sector (one third of the sites studied) clearly indicates the degradation that the Moroccan Mediterranean coast has undergone due to considerable anthropogenic activities and the lack of adequate coastal management programs. In this study, various management interventions were proposed to conserve and improve the aesthetic quality of beaches and reduce the impact and presence of litter in the coastal areas.

Introducing ICEDAP: An ‘Iterative Coastal Embayment Delineation and Analysis Process’ with Applications for the Management of Coastal Change

Coastal embayments provide vital benefits to both nature and humans alike in the form of ecosystem services, access to waterways, and general aesthetic appeal. These coastal interfaces are therefore often subject to human development and modifications, with estuarine embayments especially likely to have been anthropogenically altered. Frequent alterations include damming to eliminate tidal influx, backfilling to create new land, and development for the sake of economic gain, which may cause profound damage to local habitats. By providing a record of transitions in surface waters over time, satellite imagery is essential to monitoring these coastal changes, especially on regional to global scales. However, prior work has not provided a straightforward way to use these satellite-derived datasets to specifically delineate embayed waters, limiting researchers’ ability to focus their analyses on this ecologically and economically important subset of coastal waters. Here, we created ICEDAP, a geometry-based ArcGIS toolbox to automatically delineate coastal embayments and quantify coastal surface water change. We then applied ICEDAP to the coast of South Korea, and found that coastal habitat change was particularly profound within embayed regions identified using an 8 km epsilon convexity setting (denoting a moderate distance from the coast and degree of enclosure by surrounding land areas). In the mapped coastal embayments, more than 1400 km2 of coastal habitats were lost during the past 38 years, primarily due to human modification such as large-scale land reclamation projects and the construction of impoundments. Our results suggest that anthropogenic alterations have resulted in the widespread loss of more than USD 70 million of valuable coastal ecosystem services. Together, ICEDAP provides a new innovative tool for both coastal scientists and managers to automatically identify hotspots of coastal change over large spatial and temporal scales in an epoch where anthropogenic and climate-driven changes commonly threaten the stability of coastal habitats.

Multi-model chain for climate change scenario analysis to support coastal erosion and water quality risk management for the Metropolitan city of Venice

Under the influence of anthropogenic climate change, hazardous climate and weather events are increasing in frequency and severity, with wide-ranging impacts across ecosystems and landscapes, especially fragile and dynamic coastal zones. The presented multi-model chain approach combines ocean hydrodynamics, wave fields, and shoreline extraction models to build a Bayesian Network-based coastal risk assessment model for the future analysis of shoreline evolution and seawater quality (i.e., suspended particulate matter, diffuse attenuation of light). In particular, the model was designed around a baseline scenario exploiting historical shoreline and oceanographic data within the 2015–2017 timeframe. Shoreline erosion and water quality changes along the coastal area of the Metropolitan city of Venice were evaluated for 2021–2050, under the RCP8.5 future scenario. The results showed a destabilizing trend in both shoreline evolution and seawater quality under the selected climate change scenario. Specifically, after a stable period (2021–2030), the shoreline will be affected by periods of erosion (2031–2040) and then accretion (2041–2050), with a simultaneous decrease in seawater quality in terms of higher turbidity. The decadal analysis and sensitivity evaluation of the input variables demonstrates a strong influence of oceanographic variables on the assessed endpoints, highlighting how the factors are strongly connected.The integration of regional and global climate models with Machine Learning and satellite imagery within the proposed multi-model chain represents an innovative update on state-of-the-art techniques. The validated outputs represent a good promise for better understanding the varying impacts due to future climate change conditions (e.g., wind, wave, tide, and sea-level). Moreover, the flexibility of the approach allows for the quick integration of climate and multi-risk data as it becomes available, and would represent a useful tool for forward-looking coastal risk management for decision-makers.

Ocean literacy in Brazilian formal education: A tool for participative coastal management

AbstractOcean literacy (OL) proposes to include ocean and marine environment-related content in school curricula. Such a topic has been deemed effective for citizens to develop actions and attitudes towards the health of marine ecosystems. This study aimed to verify the presence and frequency of OL principles and concepts in the Brazilian high school curriculum at the federal (National Curricular Parameters-PCN) and regional (Rio de Janeiro - Curricular Reference-RC) levels. Both PCN and RC contained OL-related content. Moreover, Biology and Geography were the subjects with the highest numbers of OL concepts, both in the PCN (26 and 27, respectively) and in the RC-RJ documents (28 and 24, respectively), while OL concepts were very little represented in History subject. A Mann–Whitney U-test did not indicate statistically significant differences in the number of concepts between PCN and RC-RJ (p = 0.54). A principal component analysis discriminated the documents according to subjects, regardless of their origin (federal or regional). These results provide an unbiased assessment of the relationship between the curriculum and OL in a strongly affected area (Rio de Janeiro State coastal zone [CZ]). Therefore, these results provide valuable support for managers seeking to promote effective CZ management practices and public compliance.

Potential of Transplanted Seagrass Meadows on Wave Attenuation in a Fetch-Limited Environment

In this paper, the effectiveness of transplanted (either created or restored) seagrass meadows as a coastal protection measure is assessed through a five-step methodology. The analysis is focused on a stretch of the Catalan coast (NW Mediterranean) which is a fetch-limited environment. The results show that even considering conservative values for the meadow parameters (plant diameter, meadow density and canopy height), significant reductions of the annual average wave heights reaching the beach may be obtained, reducing flooding and erosion risks. Therefore, the investment in the conservation and restauration of seagrass meadows for protecting coastal areas from erosion and flooding is a measure that must be considered, due to the multiple benefits that they provide including ecosystem services. In addition, the proposed methodology may be a useful tool for coastal managers to help them in the design of seagrass meadows for coastal protection.

Using Random Forest for Future Sea Level Prediction

This research paper presents an investigation into using the random forest algorithm for predicting future sea level. Sea level is a critical indicator of the health of our oceans and coastal areas and is measured in total weight observations. The study employs the random forest algorithm, a powerful machine learning technique, to analyze a dataset of sea level observations. The results of the analysis demonstrate the effectiveness of the random forest algorithm in accurately predicting future sea level changes. The findings of this research have important implications for coastal management and adaptation strategies. This research provides a valuable tool for decision-makers and coastal managers, allowing for more informed and proactive planning for sea level rise. Overall, the paper shows that the random forest algorithm is a promising method for sea level prediction and highlights the importance of continued research in this area.

Shoreline Prediction Modelling as a Base Tool for Coastal Management: The Catania Plain Case Study (Italy)

Coastal dynamic is the complex result of multiple natural and human processes, and past and future coastal behavior studies become fundamental to support coastal zone management. However, the reliability of coastal evolution studies is strongly dependent on the analyzed time interval. The longer the period is, the more reliable the past shoreline change analysis and the forecast of shoreline position will be. The present study showed the 50-years shoreline evolution of the Catania coastal plain (Southern Italy), a densely populated area where human-induced processes profoundly changed natural dynamics. Landsat and Sentinel imageries were used to extract shorelines’ position over the time between 1972 and 2022 and the DSAS tool was used to calculate the shoreline change rates. The shoreline evolution in 2032 and 2042 was performed by the Kalman filter method, a tool largely applied to forecast short-term shoreline future position. Most of the Catania coastal plain was mainly retreating over the last decades. However, the most significant changes were registered in correspondence with the coastal structures and the river deltas. The reliability of the forecasting model was highly related to the coastal morphology. As such, the lower RMSE values were calculated in correspondence with the uniform coastal subsectors.