Coastal Science Research Articles

Enabling coastal analytics at planetary scale

Coastal science has entered a new era of data-driven research, facilitated by satellite data and cloud compute. Despite its potential, the coastal community has yet to fully capitalize on these advancements due to a lack of tailored data, tools, and models. This paper demonstrates how cloud technology can advance coastal analytics at scale. We introduce GCTS, a novel foundational dataset comprising over 11 million coastal transects at 100-meter resolution. Our experiments highlight the importance of cloud-optimized data formats, geospatial sorting, and metadata-driven data retrieval. By leveraging cloud technology, we achieve up to 700 times faster performance for tasks like coastal waterline mapping. A case study reveals that 33% of the world’s first kilometer of coast is below 5 meters, with the entire analysis completed in a few hours. Our findings make a compelling case for the coastal community to start producing data, tools, and models suitable for scalable coastal analytics.

Super-resolution on unstructured coastal wave computations with graph neural networks and polynomial regressions

Accurate high-resolution wave forecasts are essential for coastal communities, but local and even coastal coverage is often still missing due to the heavy computational load of modern state-of-the-art wave models. This study presents a machine learning super-resolution approach that drastically reduces the computational effort, while keeping errors negligible for the majority of forecasting applications. The method consists of first computing a wave forecast on a coarse mesh which is then converted to a forecast of finer resolution with the help of machine learning. To demonstrate the feasibility and the potential for practical applications of this approach, we present a case study of a 44-year hindcast along the French Basque coast over an unstructured mesh. We introduce two machine learning approaches, a graph neural network and a polynomial ridge regression and compare their performances in different sea states and spatial environments. Both models exhibit very small prediction errors for the significant wave heights, with Root Mean Square Errors (RMSEs) ranging from 0.3cm to 2cm, depending on the study region, while being up to 80 times faster than a direct computation of a numerical wave model at the corresponding spatial resolution. To the best of our knowledge, this is the first time that a super-resolution approach is extended to unstructured meshes in the field of coastal sciences.

Analysis of shoreline changes in the severe storm surge region of upper Coromandel coast, India, using Remote sensing (RS) and Geographic information system (GIS)

Coastlines have been subject to ongoing vulnerability from natural disasters, resulting in erosion and accretion occurring at varying intervals. The impacts of human activities and alterations in natural processes, including those induced by climate change, exacerbate the existing challenges faced along coastal regions. The present study aimed to analyze the changing shoreline along the Bapatla coastal tract in the upper coromandel coast of Andhra Pradesh due to erosion and accretion using the DSAS toolbar in ArcGIS software. Multiple statistical methods are available in DSAS; the present study confined attention to the end point rate (EPR), linear regression rate (LRR), and net shoreline movement (NSM) for this investigation. The Nizampatnam Mandal had the greatest erosion rate (-16.8 m/yr) and accretion rate (10.5 m/yr), according to EPR, as well as the highest erosion rate (-16.7 m/yr) and accretion rate (10.4 m/yr) according to LRR. As per NSM, between 1990 and 2020, the highest erosion and accretion was seen again in Nizampatnam. The shoreline at Nizampatnam either retreated by as much as -285.2 meters or advanced by 211.1 meters. The research indicated that the Bapatla district's shoreline is susceptible to erosion and accretion. The present investigation revealed that the coastal region of Bapatla district exhibits susceptibility to both erosion and accretion phenomena. The findings of the present study hold significant implications for professionals in the fields of coastal science, engineering, and administration. The findings are of utmost importance in the development of sustainable approaches for coastal management.

Benchmarking satellite-derived shoreline mapping algorithms

Satellite remote sensing is becoming a widely used monitoring technique in coastal sciences. Yet, no benchmarking studies exist that compare the performance of popular satellite-derived shoreline mapping algorithms against standardized sets of inputs and validation data. Here we present a new benchmarking framework to evaluate the accuracy of shoreline change observations extracted from publicly available satellite imagery (Landsat and Sentinel-2). Accuracy and precision of five established shoreline mapping algorithms are evaluated at four sandy beaches with varying geologic and oceanographic conditions. Comparisons against long-term in situ beach surveys reveal that all algorithms provide horizontal accuracy on the order of 10 m at microtidal sites. However, accuracy deteriorates as the tidal range increases, to more than 20 m for a high-energy macrotidal beach (Truc Vert, France) with complex foreshore morphology. The goal of this open-source, collaborative benchmarking framework is to identify areas of improvement for present algorithms, while providing a stepping stone for testing future developments, and ensuring reproducibility of methods across various research groups and applications.

Coastal forecast through coupling of Artificial Intelligence and hydro-morphodynamical modelling

ABSTRACT As climate-driven risks for the world’s coastlines increase, understanding and predicting morphological changes as well as developing efficient systems for coastal forecast has become of the foremost importance for adaptation to climate change. Artificial Intelligence is a powerful technology that has been rapidly evolving recently and can offer new means of analysis for the coastal science field. Yet, the potential of these technologies for coastal geomorphology remains relatively unexplored with respect to other scientific fields. This article investigates the use of Artificial Neural Networks and Bayesian Networks in combination with fully coupled hydrodynamics and morphological models (Delft3D) for predicting morphological changes and sediment transport along coastal systems. Two sets of Artificial Intelligence models were tested, one set relying on localized modeling outputs or localized data sources and another set having reduced dependency from modeling outputs and, once trained, solely relying on boundary conditions and coastline geometry. The first set of models provides regression values greater than 0.95 and 0.86 for training and testing, respectively. The second set of reduced dependency models provides regression values greater than 0.84 and 0.76 for training and testing, respectively. Our results highlight the potential of AI and statistical models for coastal applications.

BASIC POSITION OF MODERN GEOGRAPHY AND ITS STRUCTURE

During last decades geographical science are subjected to crisis events under impact of new social-economic factor in Ukraine. The aim of the article is analysis of modern geography as a fundamental science during new conditions in Ukraine. New scientific structure of geography was elaborated. As a result, additional geographical spheres of the science were distinguished, for instance: limnology, coastal science, engineering geography, methods of geographical research, etc. Geography is one of the basic natural resource-attitude sciences, that are researching factors, processes and mechanisms of natural environmental systems of various genetic level within the Global Geographical jacket and hers natural spheres, with participation of anthropogenous factor, in addition with a spatial spreading on squire of the World. Geography is studying properties and territorial spreading geographical objects and regularities of its interactions. Destination of the research and development are harmonic usage of natural resources and comfort reside people.

O’Brien Award Winners: An interview with Lee Weishar, Ph.D.

Dr. Lee Weishar won the Morrough P. O’Brien Award, ASBPA’s highest honor, in 2022. This interview presents his thoughts on his career and the future of coastal science and engineering.

Stable and effective traveling wave solutions to the non-linear fractional Gardner and Zakharov–Kuznetsov–Benjamin–Bona–Mahony equations

The space–time fractional Gardner and Zakharov–Kuznetsov–Benjamin–Bona–Mahony (ZKBBM) equations are used to explain the transmission of shallow water waves inside a water channel of uniform speed and constant depth. It moreover simulates water waves in harbors and shallow water, which is useful for oceanography. These equations are significant nonlinear model equations to illustrate numerous physical structures namely in marine and coastal science, water wave mechanics, control theory, plasma engineering, fluid flow, optical fibers, phenomena of fission and fusion, and so on. In this study, we employed the extended tanh-function approach to generate some fresh and further general closed-form traveling waves solutions of those equations in the lite of conformal derivatives. The achieve results are more applicable to resolve the above mentioned phenomena properly. The fractional differential transform simplifies generate ordinary differential equations from fractional order differential equations. We discovered many types of solutions using the maple, including solitons, kink types, bell types, and other types of solutions that are illustrated using 3D and contour plots. It is important to note that all derived solutions are checked for accuracy by being directly replaced with the original equation. We proposed that the technique be revised to be more realistic, effective, and trustworthy and that we explore more generalized exact results of traveling waves, like solitary wave solutions.

Coastal Science for Resilience and Management at the Cape Hatteras National Seashore, NC, USA

National seashores are cherished public lands with rich environmental, cultural, and historic resources. The Cape Hatteras National Seashore is one such coastal asset that is both bountiful yet vulnerable, with historic lighthouses, critical habitats, and recreational amenities alike facing threats of sea-level rise and continual storm and climate change impacts. Over 3 million visitors to the Seashore in 2021 set an annual visitation record. Historic resources such as the Bodie Island Lighthouse and Ocracoke Lighthouse are among the most visited sites, yet these assets are also among those most vulnerable to flooding, compromised structural integrity, and reduced accessibility. Future challenges to the protection and management of such resources are already being felt in the form of storms, extreme rainfall, and recurrent compound flooding. Such threats are also coincident with increasing visitation and recreational demand. This paper examines the science-based data that are being collected and management efforts underway to inform future planning, intervention, or adaptation to sea-level rise and barrier island evolution. The paper identifies the opportunities for mitigation and adaptation as well as potential environmental tipping points and limits to resilience by assessing the frequency and magnitude of flooding events and shoreline change.

CLASSIFICATION OF ANTHROPOGENOUS RELIEF FORMS WITHIN COASTAL ZONE OF UNTIDAL SEAS IN EUROPE

Problems Statement and Purpose. The article defines the intensification of the development of anthropogenic geomorphology as one of the important areas of geographical science in Ukraine. Until now, there is practically no geomorphological classification of anthropogenic relief forms in the coastal zone of the seas and on the coasts in fundamental works. This significantly inhibits the further development of this scientific direction and adversely affects practical factors. Review and analysis of the history of research on the development of anthropogenic geomorphology showed the beginning of the occurrence of anthropogenic relief many centuries ago. But this was addressed by only a few geographers and representatives of the authorities. Only with the advent of machine production and the progress of modern coastal development, geomorphologists recognized a separate morpholithogenesis. Even today, foreign authors generally pay little attention to geomorphological issues of the study of anthropogenic relief on oceanic and sea coasts. Data and Methods. Mainly own experience of geomorphological studies in different European countries was used. Additional material is borrowed from publications of domestic and foreign authors supplemented with illustrations. Synthesis, classification, comparative and geographic as well as cartographic methods are used. Actual illustrative and graphic materials are included. Results. The first attempt to classify the anthropogenic relief on the sea coasts of the non-tidal seas of Europe (Mediterranean, Black, Azov) in several groups is outlined according to purposes: a) port; b) navigational; c) coast guard; d) recreational; e) extractive; f) residential. We also allow the presence of coastal and marine anthropogenic relief forms of other purposes in different regions of the sea coast. Separate geomorphological taxa are distinguished within elementary forms. It was found that within individual groups and types, they are divided into longitudinal and transverse, active and passive, large and small, vertical and horizontal, surface, underwater and complex, purposeful and accompanying, according to the material from which they are built, with natural relief and without him, ancient and modern. Some few taxa and elementary forms can be included in two or three complex forms and assignment groups, which leads to violations of the logical principles of classification construction. But such are the features of not only coastal and marine but also other classifications of anthropogenic relief. The given classification has a generally important theoretical and practical significance in geomorphology: it will contribute to further development of the general theory of anthropogenic geomorphology; will provide a characteristic of various anthropogenic forms necessary during the design and economic development of coasts; will be included in the educational program for the geographic disciplines “Geomorphology” and “Coastal science”, which will relieve geographer students of a false understanding of anthropogenic relief in the system of the coastal and marine environment.

The challenges of fieldwork: Improving the experience for women in coastal sciences

Abstract Women face disproportionate challenges while undertaking coastal fieldwork. We draw on 18 responses that specifically raise fieldwork issues from an international survey about perceptions and experiences of gender inequality for those working in coastal sciences to discuss two common themes. These themes are barriers to fieldwork participation and challenges for women working in coastal field settings such as boats or working on beaches, including discrimination and sexual harassment. We suggest five priority behavioural and policy changes to improve the fieldwork experience for women in coastal sciences: (i) publicise field role models and trail blazers, (ii) improve opportunities and capacity for women to undertake fieldwork, (iii) establish field codes of conduct, (iv) acknowledge the challenges women face in the field and provide support where possible, and (v) foster an enjoyable and supportive fieldwork culture.

Social science to accelerate coastal adaptation to sea-level rise

Abstract The latest IPCC report estimates that approximately 1 billion people will be at risk from coastal hazards in the near term due to coastal population increase, sea-level rise and other coastal changes. This will occur in a world that is changing rapidly due to climate change, ecosystem decline, human development and the projected transformations of the economy to meet the objectives of the Paris Agreement. In this context, social sciences provide a pivotal perspective to coastal adaptation, for example, while assessing barriers and opportunities across scales, from local to global. This scoping review explores how social sciences support coastal adaptation. We show that Political Sciences, Economics, Sociology and Geography are already supporting coastal adaptation. Yet, scientific fields such as legal sciences, psychology, history and archaeology as well as anthropology and ethnography are less developed in the context of coastal adaptation to sea-level rise. New research avenues could also integrate education, media and communication research and aim at truly interdisciplinary studies linking different branches of social sciences with coastal science and climate services. This effort could help moving from a coastal adaptation often focused on coastal engineering protection to a broader vision of coastal resilient development, also addressing the challenges of mitigation, sustainable development and coastal ecosystem decline.

‘The tool didn’t make decisions for us': metrics and the performance of accountability in environmental governance

ABSTRACT Governments use metrics made possible by new data technologies to allocate budgets, manage pandemics, valorize ecosystems, and demonstrate how these actions are legitimate. Big data is pointed to as providing objective answers that emerge untampered from observations of the world as it is – a view from nowhere. While data is more valued than ever in environmental governance, so too are arrangements that seek stakeholders’ input and otherwise address their subjective interests – a view from everywhere. Different kinds of metrics perform state actors as accountable in both registers: metrics that are responsive to dynamic conditions; that account for specific stakeholders; that can be prioritized against one another in interactive data visualization tools. Louisiana, USA’s Coastal Master Plan is an attempt to stem wetlands loss through fine-scale modeling of large volumes of data and calculation of these kinds of social and environmental metrics. State actors there make accountability claims that appear contradictory: their decisions are legitimate because they are driven by the best available coastal science and technology, while their data tools ‘didn’t make decisions for us.’ As state actors deploy environmental big data and metrics to make sense of it, we should be able to explain these apparently contradictory stances and the controversies that result. STS theory on metrics in environmental governance benefits from characterizing how ‘modes of authorized seeing’ are given expression by different metrical forms and what brings modes into contact and conflict.

The future of coastal monitoring through satellite remote sensing

AbstractSatellite remote sensing is transforming coastal science from a “data-poor” field into a “data-rich” field. Sandy beaches are dynamic landscapes that change in response to long-term pressures, short-term pulses, and anthropogenic interventions. Until recently, the rate and breadth of beach change have outpaced our ability to monitor those changes, due to the spatiotemporal limitations of our observational capacity. Over the past several decades, only a handful of beaches worldwide have been regularly monitored with accurate yet expensive in situ surveys. The long-term coastal-change data of these few well-monitored beaches have led to in-depth understanding of many site-specific coastal processes. However, because the best-monitored beaches are not representative of all beaches, much remains unknown about the processes and fate of the other >99% of unmonitored beaches worldwide. The fleet of Earth-observing satellites has enabled multiscale monitoring of beaches, for the very first time, by providing imagery with global coverage and up to daily frequency. The long-standing and ever-expanding archive of satellite imagery will enable coastal scientists to investigate coastal change at sites vulnerable to future sea-level rise, that is, (almost) everywhere. In the past decade, our capability to observe coastal change from space has grown substantially with computing and algorithmic power. Yet, further advances are needed in automating monitoring using machine learning, deep learning, and computer vision to fully leverage this massive treasure trove of data. Extensive monitoring and investigation of the causes and effects of coastal change at the requisite spatiotemporal scales will provide coastal managers with additional, valuable information to evaluate problems and solutions, addressing the potential for widespread beach loss due to accelerated sea-level rise, development, and reduced sediment supply. Monitoring from Earth-observing satellites is currently the only means of providing seamless data with high spatiotemporal resolution at the global scale of the impending impacts of climate change on coastal systems.

Challenges in Estuarine and Coastal Science

Challenges in Estuarine and Coastal Science

Quantifying Coastal Storm Impacts Using a New Cumulative Storm Impact Index (CSII) Model: Application Along the Virginia Coast, USA

AbstractThis paper presents a new empirical model, called the cumulative storm impact index (CSII), that quantifies the impact of coastal storms on sandy beaches. The new model utilizes user‐defined storm data to incorporate both individual storm magnitude and the cumulative effect of successive storms into an index, which is a proxy for beach erosion at a given time. Applying this model to long‐term water‐level data from a Virginia tide gauge showed that the greatest storm impact resulted not from the larger individual storms, such as the Ash Wednesday nor'easter of 1962, the “Perfect Storm” of 1991, or Hurricane Sandy of 2012, but rather from especially stormy winter seasons that occurred during the twenty‐first century. Additionally, the CSII model uncovered a trend—not detectable by single storm impact analyses—toward greater storm impacts, which began c. 1980 and continued to the present day. Finally, comparative analyses using wave power as a storm index shows CSII can capture decadal or seasonal scale storminess. We expect this model to have utility in many areas of the coastal sciences and engineering, including developing holistic response models, quantifying erosion potential at other locations, and managing coastal ecosystems.

‘Coastal Management Guide - Managing Coastal Erosion’: A STEM education resource for secondary school teachers

“Hello, I'm a school student and we're doing a project about the coast, and I was wondering if you could talk to me about what you do or send me some information?“. Many coastal scientists and engineers regularly experience similar emails and phone calls from secondary school students (typically ages 12–16) and it is often very rewarding to speak with and assist these bright, young, people. Motivated by the increasing number and frequency of these requests, presented here is an overview of the approach taken and lessons learnt by a group of university and government coastal researchers during the creation of a secondary school education resource. Underpinned by fundamental concepts of coastal science and coastal management, the resulting Coastal Management Guide – Managing Coastal Erosion is primarily targeted at STEM teachers, that spans and is explicitly linked to a broad range of national curriculum areas, including the physical sciences, physical and human geography, environmental sciences and mathematics. Developed around an enquiry-based mode of learning and employing the well-established Five Es ‘engage, explore, explain, elaborate & evaluate’ instructional model, the content of the Guide was designed to include all the background technical information and additional resources that are necessary for teachers to lead their students through the range of classroom activities provided. Freely available as both an online eBook and 70-page downloadable pdf, at the time of writing more than 90,000 website views originating from 77 countries have been recorded, with the eBook used over 3000 times and more than 10,000 pdf copies of the Guide downloaded. Importantly, during the development of the Guide several key lessons were learnt, that can assist other individuals or teams of researchers who may be motivated to extend the reach of their own research into the school classroom: (1) Seeking the assistance of curriculum experts and specialist STEM content creators is invaluable. (2) Don't underestimate your potential reach into a broad range of school curriculum areas that may extend well beyond the physical sciences. (3) Decide if your audience is individual students or alternatively their teachers, remembering that one teacher can deliver to many students. (4) Consider teachers' practical needs and preferences – for example, downloadable pdfs that can be printed and copied are highly regarded. (5) Seek user feedback to find out what does and doesn't work, and to potentially generate new ideas for the next version …. In outlining and summarising the approach that was used to develop the secondary school educational resource presented here, it is hoped that other coastal researchers may be stimulated and encouraged to also give it a go. To do so is both professionally and personally very rewarding.

A COMBINED COLOR AND WAVE-BASED APPROACH TO SATELLITE DERIVED BATHYMETRY USING DEEP LEARNING

Abstract. Knowledge of the evolution of the littoral zone over time is paramount for coastal science and coastal zone management. However, traditional bathymetric surveys using echo-sounding techniques are unsuitable for large-scale applications due to a variety of constraints. On the other hand, remote sensing data such as satellite imagery allow for the development and application of bathymetry inversion models on a large scale. Deep learning is a growing field of artificial intelligence that allows for the automatic construction of models from data and has been successfully used for various Earth Observation and model inversion applications. In this work, we develop and apply a deep learning-based depth inversion model combining wave kinematics and water color information from Sentinel-2 satellite imagery. We present two different satellite image processing methods to augment wave kinematics and color information as inputs to the proposed deep learning-based models. We show competitive results with a state-of-the-art physical inversion method for satellite derived bathymetry, Satellite to Shores (S2Shores), demonstrating a promising direction for the use of deep learning models in Satellite Derived Bathymetry (SDB) and Earth observation in general.

Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes

AbstractNumerical models are of fundamental usage for estuarine and coastal sciences. Although numerical simulations are widely applied, analyzing and improving them are often challenging tasks given their large volume and huge parameter space. In this study, a novel data‐driven framework is introduced to study the Minjiang River Plume (MJRP). The framework combines Self‐Organizing Map (SOM) clustering with a Hidden Markov Model (HMM). A three‐dimensional Regional Ocean Model System for MJRP is first configurated with realistic atmospheric, oceanic, and riverine forcings. By applying SOM clustering to the modeled sea surface salinity (SSS) with ∼2,000 2‐day averaged records from 2010 to 2020, we identify six major patterns of MJRP. Each pattern exhibits distinct circulation and plume structures. These MJRP patterns contain not only seasonal signals, but also rich short‐term variabilities driven by the riverine inputs and oceanic dynamics. Then, the SOM‐HMM method was applied to predict the future of the hidden state (i.e., patterns of MJRP) from the observable states (wind and river runoff). With a hypothetic SSS product from a geostationary satellite as the ground truth, we show that the SOM‐HMM method can predict MJRP patterns considerably high prediction accuracy and computational efficiency. Further, these patterns were translated back to SSS with high forecast skills. Combining a conventional numerical model with a data‐driven method, this approach can be promisingly applied in the short‐term marine forecast to support the utilization and management of other estuaries.

A conceptual beachhead: “Beaches and dunes of human-altered coasts” by Karl F. Nordstrom (1994)

Approaching 30 years since its publication in Progress in Physical Geography, Nordstrom’s work from 1994 reads like an uncannily current synopsis of grand challenges in interdisciplinary coastal science. The article is a playbook of testable hypotheses for emerging and future empirical coastal research.