OS2.5 | Observing, forecasting and projecting the Global Coastal Ocean (CoastPredict)
Observing, forecasting and projecting the Global Coastal Ocean (CoastPredict)
Co-organized by CL3.1
Convener: Anna Katavouta | Co-conveners: Giorgia Verri, Jacopo Alessandri, Abe Woo, Joseph Ansong
| Tue, 16 Apr, 08:30–12:30 (CEST)
Room 1.61/62
Posters on site
| Attendance Tue, 16 Apr, 16:15–18:00 (CEST) | Display Tue, 16 Apr, 14:00–18:00
Hall X4
Posters virtual
| Attendance Tue, 16 Apr, 14:00–15:45 (CEST) | Display Tue, 16 Apr, 08:30–18:00
vHall X5
Orals |
Tue, 08:30
Tue, 16:15
Tue, 14:00
With increasing coastal urbanisation and impacts from climate change, there is a pressing need for understanding, monitoring and predicting the environmental conditions and hazards in the global coastal ocean. This challenge requires ocean observing and modelling systems designed to monitor coastal variability at regional to local scales, as well as regional downscaling tailored to include coastal processes absent in the global Earth system models. This session is affiliated with CoastPredict, a UN Ocean Decade endorsed programme dedicated to the design and implementation of an integrated coastal observation and prediction system to support coastal community resilience. With this session we aim to provide a discussion platform around observing, forecasting and projecting coastal processes, from short time scale events to climate projections. We welcome contributions related to:
• Challenges and advances in observing and modelling the global coastal system with high temporal and spatial resolution (e.g. new community science platforms and modelling the coupled coastal system: ocean - atmosphere - hydrology - land – ecosystem - humans system).
• Complementary use of observations and models towards better short-term forecasting and early warnings along the coastal regions; and observing system design experiments focusing on the coastal seas.
• Dynamical and/or statistical downscaling and forecasting methodologies for the coastal ocean including machine learning, bias correction techniques and spectral nudging.
• Uncertainty treatment for short to long-term coastal ocean projections, e.g. ensemble approaches.
• Nature-based solutions for adaptation planning in coastal systems, and coastal carbon dioxide removal for climate mitigation.
• Coastal management, covering the full cycle of transdisciplinary information collection, planning, decision-making, management and monitoring of implementation.

Orals: Tue, 16 Apr | Room 1.61/62

Chairpersons: Anna Katavouta, Jacopo Alessandri, Abe Woo
On-site presentation
Aiko Love del Rosario, Adonis Gallentes, Princess Hope Bilgera, and Cesar Villanoy

The mechanisms behind the high productivity of the Visayan Sea (Philippines) need to be understood for better fisheries management. However, current global ocean models are limited to spatial resolution of 1/8° to 1/12° which is around 8-14 km in grid size. Due to the presence of islands, shallow depths and narrow straits surrounding the Visayan Sea, global models cannot resolve and explain the Visayan Sea surface currents. In this paper, we explore the possible reasons for the high productivity in the region through analysis of satellite-derived chl-a and high resolution hydrodynamic models of the Visayan Sea in DELFT3D-Flow and SURF-NEMO.

Tide analysis suggests that the dominant constituents in the Visayan Sea are both semi-diurnal - M2 (principal lunar) and S2 (principal solar). In the larger Philippine Internal Seas which include the Visayan Sea, notably higher M2 and S2 amplitudes are observed in the latter. This can be attributed to the possible resonance of wave-wave interaction by tides coming from surrounding basins. Being a relatively shallow body of water (30-90 m) surrounded by deeper waters (100-1,700 m) and an area where maximum tidal amplitudes are found, stronger vertical mixing and nutrient exchange are ensued, thereby reinforcing productivity. 

Satellite-observed chlorophyll-a concentrations from 2002 to 2022 in the Visayan Sea and adjacent seas are consistently high, regardless of monsoon reversal. Empirical orthogonal function (EOF) analysis of chlorophyll data was also conducted to determine the dominant patterns of chl-a variance. The first 10 modes contribute 65.2 % of the total variance. Mode 1 (18.8 %) is attributed to the seasonal variability (i.e., monsoons). Mode 2 (11.1 %) pattern can be associated with the Nino3.4 ENSO Index, suggesting that primary productivity in the Visayan Sea could be well affected by the changing climate. 

Lastly, this study presents a recommendation of areas that need protection and focused management for better implementation of fishing seasonal closure in the Visayan Sea.

How to cite: del Rosario, A. L., Gallentes, A., Bilgera, P. H., and Villanoy, C.: Oceanographic processes influence the high primary productivity in the Visayan Sea, Philippines  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19232, https://doi.org/10.5194/egusphere-egu24-19232, 2024.

On-site presentation
Peter Holtermann, Jacob Geersen, Robert Mars, Sebastian Neubert, Miriam von Thenen, and Maren Voss

The Baltic Sea, a European semi-enclosed marginal sea, is driven by the estuarine circulation of dense, saline North-Sea water entering the Baltic Sea and mixing with the freshwater input due to rivers and precipitation. The mixed brackish water leaves the Baltic Sea at the surface through the Danish Belts and the Sound. These processes lead to a strong vertical stratification of the Baltic Sea water masses, the halocline. The slow water exchange causes a mean residence time of the water of 30 years, which leads an accumulation of nutrients. A major consequence of the long residence time and the halocline are low oxygen concentrations below the halocline, with virtually permanent anoxic conditions in the deep basins of the Baltic Sea. To what respect climate change, with the warming of the Baltic Sea as one effect, is impacting the Baltic Sea ecosystem is an open and very relevant research question. One potential consequence could be a further spreading of low or anoxic zones towards the coastal areas, which is already being observed. A less well understood part of the Baltic Sea are the shallow coastal zones, but recent research points to the direction of a strong relevance for e.g. the nutrient turnover. To develop a fundamental understanding of the relevant processes and their coupled effect on the biota, it is therefore essential to measure, monitor and predict the shallow water processes along the margins of the Baltic Sea and how they alter the state of the sea at a basin-wide scale.

To address these research questions, the IOW is establishing an interdisciplinary network of long-term and short-term observations in the coastal area of the southern Baltic Sea. This involves deploying moorings in shallow water that send their data to the institute in real time, where they are made immediately available to the general public. The essential ocean parameters (EOP) acquired will be used to control specialized sampling. A measurement program on biogeochemical nutrient turnover, sediment dynamics, geophysics, phytoplankton and zooplankton takes place regularly and is linked to physical data on current patterns, wave intensity and turbulence. The in-Situ measurements are combined with high-resolution numerical modeling to be able to extrapolate the field measurements and to develop numerical experiments.

Different stakeholders groups will be involved to provide society and authorities with the latest findings of the measurement campaigns.

How to cite: Holtermann, P., Geersen, J., Mars, R., Neubert, S., von Thenen, M., and Voss, M.: The Baltic Sea Observatory – A new holistic approach to understand the coastal ocean, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19020, https://doi.org/10.5194/egusphere-egu24-19020, 2024.

On-site presentation
Ji-sook Park, Jong-yeon Park, Jeong-hwan Kim, and Yoo-geun Ham

Marine biogeochemistry governs the flux of climate-active gases at the ocean-atmosphere interface, influencing diverse climate feedbacks. Despite advances in Earth System Models (ESMs) for climate-ecosystem predictions, challenges persist in the initialization and validation with biogeochemical observation data. In this study, Convolutional Neural Network (CNN)-based models predict chlorophyll concentrations in a productive large coastal area. The model was trained and validated using Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble datasets and physical–biogeochemical reanalysis data from a data assimilative ESM run. Through sensitivity tests on the model structure and input data, the CNN-based model demonstrates physical interpretability consistent with previous studies. Our optimized model adeptly reproduces annual observational chlorophyll variations in coastal regions where dynamic models face challenges, demonstrating comparable prediction skill to dynamic models in seasonal prediction by capturing large-scale climate variabilities. These findings highlight the importance of combining dynamic models and deep learning approaches, offering the potential for more accurate and comprehensive predictions of marine ecosystems.

How to cite: Park, J., Park, J., Kim, J., and Ham, Y.: Seasonal to multiannual marine ecosystem prediction using a deep learning approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3340, https://doi.org/10.5194/egusphere-egu24-3340, 2024.

On-site presentation
Mahmud Hasan Ghani, Francesco Trotta, and Nadia Pinardi

Recent advancements within the arena of Artificial Intelligence have widened the potential applications of Machine Learning (ML) frameworks in climate prediction and weather forecasting. For any modern forecasting system, a core objective is linked with handling uncertainty and scientists are interested in the accuracy of the forecasts.  The time series forecast of air temperature using ML approaches is available in the literature. But for this study, we have selected major atmospheric variables- air temperature, dew point temperature, wind components and mean sea-level pressure (MSL-P) retrieved from the ECMWF analysis system and which are to be used in perturbation of the ocean forecasting system. In our previous approach, we analysed the probability distributions of the selected atmospheric variables. In this study, we intend to forecast those atmospheric variables using machine learning algorithms to compare with the analysis dataset produced by ECMWF. Under the initial approach, a Convolution Neural Network (CNN) approach is built to predict the time series for the atmospheric variables. The predicted results from the forecasts have shown minimal differences in comparison to the observations.  Based on the results produced from the CNN, we would like to apply other ML approaches to compare the accuracy and in the process of selecting a better ML model.  

How to cite: Ghani, M. H., Trotta, F., and Pinardi, N.: Forecasting of atmospheric variables based on ECMWF analysis data using Machine Learning approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12416, https://doi.org/10.5194/egusphere-egu24-12416, 2024.

On-site presentation
Hongyang Lin

Investigations on the spatiotemporal variability of coastal sea level and its mechanisms are of great scientific and practical importance. Unlike deep-ocean sea level that can be measured by satellite altimetry, studies on the spatial variability of coastal sea level require measurements from tide gauges and their associated vertical leveling information. Also, the dynamical mechanisms controlling the temporal variability has long been a research hotspot of coastal ocean dynamics. Local winds on the shelf and coastal currents are well recognized to be important in driving coastal sea level variability, but how do open-ocean signals affect sea level at the coast is less known. In addition, coastal sea level reconstruction or prediction often relies on climate models or statistical models. From a new and more dynamic perspective, we recently propose a dynamic framework to quantitatively reconstruct sea level at the coast. This presentation will focus on our recent works on the spatiotemporal variability of coastal sea level, including its alongshore tilts, mechanisms and dynamic reconstruction, as well as the implications and outstanding issues for further research.

How to cite: Lin, H.: Coastal sea level variability: Geodetic measurements, driving factors & dynamic reconstruction., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3419, https://doi.org/10.5194/egusphere-egu24-3419, 2024.

On-site presentation
Matthew Newman, Xiaoyu Long, and Sang-Ik Shin

The need for skillful seasonal prediction of coastal sea level anomalies has become increasingly evident as climate change has increased the risk of coastal flooding events. Aiming to improve our ability to forecast coastal inundation risk on seasonal and longer time scales, NOAA and NASA initiated the RISE project, a collaborative effort focused on developing and assessing novel dynamical and statistical forecast methods for coastal sea level and inundation risk for US coasts. This presentation is an outgrowth of that project, initially based on a pilot study of monthly sea level anomaly forecast skill assessed at two tide gauge stations, San Diego CA, and Charleston SC. In this study, we evaluate several current forecast systems -- NCAR Community Climate System Model Version 4 (CCSM4), GFDL Seamless System for Prediction and Earth System Research (SPEAR), and ECMWF Seasonal Forecast System 5 (SEAS5) -- by calculating deterministic and probabilistic skill from a few decades (1993-2015) of their retrospective forecasts (“hindcasts”) and for lead times of up to 6-9 months. Additionally, we examine potential local enhancement of hindcast skill by two post-processing downscaling techniques, an observationally-based multivariate linear regression and a hybrid dynamical model approach, using the adjoint model of the Estimating Circulation and Climate of the Ocean (ECCO) system forced by observed and model-predicted surface forcings.

We find that all these approaches face challenges stemming from whether the modeled sea surface height sufficiently represents observed local variations of coastal sea level, because of ocean model limitations and because of inadequacies in both model initialization and ensemble spread. Some of these issues also complicate the ability of the downscaling techniques to improve probabilistic skill, even though they do somewhat improve deterministic skill. In general, while deterministic hindcast skill is considerably higher for San Diego than Charleston, ensemble spread metrics such as forecast reliability and sharpness are mediocre for both locations. Additionally, evaluating how well any technique predicts seasonal coastal sea level variations is considerably complicated by the forced trend component and particularly how it is estimated, especially for Charleston; .essentially, skill assessment of US coastal sea level seasonal prediction is also a trend detection problem. Moreover, these results are largely matched by hindcasts from a Linear Inverse Model (LIM), a simple stochastically-forced linear prediction model constructed from observations, suggesting that substantial improvement still remains for coastal sea level prediction.

How to cite: Newman, M., Long, X., and Shin, S.-I.: Evaluating Current Statistical and Dynamical Forecast Techniques for Seasonal United States Coastal Sea Level Prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14252, https://doi.org/10.5194/egusphere-egu24-14252, 2024.

On-site presentation
Jeemijn Scheen, Dewi Le Bars, Iris J. Keizer, Tim H.J. Hermans, Sophie J.C. Tubbergen, Bert Wouters, Stef Lhermitte, and Aimée B.A. Slangen

Global mean sea level is rising due to anthropogenic climate change, via the thermal expansion of seawater and the mass loss of land ice. Regional sea-level change is also affected by changes in ocean currents due to the changing climate or internal climate variability. We use the Regional Ocean Modeling System (ROMS) to simulate future sterodynamic sea-level change – the combined contribution of thermal expansion and ocean dynamics – on the Northwestern European Shelf. Regional ocean models such as ROMS are suitable to simulate the exchange of deep ocean currents in the Atlantic with the Northwestern European shelf, and can improve the horizontal resolution from the order of 100 by 100 km (typical for global climate models) to the order of 10 by 10 km. The ROMS model is driven by CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate model output at the domain boundaries, and uses dynamical downscaling to produce projections of sterodynamic sea-level change at a 12 by 12 km horizontal resolution with 30 terrain-following depth layers.

We present projections until 2100 based on 2 CMIP6 models and 5 emission scenarios, for Western Europe at this high resolution. Our results show the advantage of dynamical downscaling on projecting annual average sea level and how this differs between the chosen CMIP6 models and the different emission scenarios. In addition, we assess the linkage between regional sea level and freshwater input of European rivers, comparing simulations without river input, with realistic river input (based on observations) and with enhanced river input. This tests whether a potential future increase in river discharge is relevant to consider in projections of regional sea-level change.

How to cite: Scheen, J., Le Bars, D., Keizer, I. J., Hermans, T. H. J., Tubbergen, S. J. C., Wouters, B., Lhermitte, S., and Slangen, A. B. A.: Projecting future sea-level change along the coast of the Netherlands with a regional ocean model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19023, https://doi.org/10.5194/egusphere-egu24-19023, 2024.

On-site presentation
Michela De Dominicis, Lucy Bricheno, Ryan Patmore, Toby Marthews, Segolene Berthou, and Laurent Amoudry

Extreme coastal sea level events are driven by various mechanisms, spanning a wide range of time scales. The long-term decadal and seasonal variability of mean sea level is combined at the coast with the seasonal variability of freshwater discharges, the daily scale of weather-related wave and surge events, and the semidiurnal to diurnal scale of astronomical tidal oscillations. Currently, future extreme sea levels are calculated as a combination of individually modelled sea surface height associated with storm surges and waves, tide and sea level rise, with number of limitations, e.g. the interaction between sea level rise and extreme sea surface height associated with storm surges, waves and tides is not taken into account. Progress in the modelling of the coupled coastal processes is urgently needed to predict how sea level rise will influence extreme sea level change at the coast and to ensure that design criteria for coastal protection are correctly specified, and hazard warning systems picks up potential disasters.

To reproduce the non-linear interactions between mean sea level, storm surge, tides and waves, we are developing an innovative high-resolution (500m) UK scale coastal ocean model based on the NEMO and WaveWatchIII systems (NEMO-WWIII UK500). This new configuration will include intertidal areas and processes (wetting and drying scheme); tides-surge-waves and sea level rise interactions; fully vertically resolved physics to include wave-current interactions and river plume dynamics; near-shore wave processes (wave set-up and run-up); sea level rise impact on tidal range/phase. NEMO-WWIII UK500 will provide predictions of water levels and waves conditions for present (fully validated by contemporary observations) and future scenarios.

The NEMO-WWIII UK500 will also provide a downstream boundary condition to the hydrological model JULES. This will enable the quantification of the effects of ocean water levels on rivers (backwater effect), which is important to lead to correct water levels in the transitional waters (e.g. estuaries and tidal rivers) which host a large proportion of infrastructure (e.g. ports, airports, power stations) and habitats of national and international significance.

How to cite: De Dominicis, M., Bricheno, L., Patmore, R., Marthews, T., Berthou, S., and Amoudry, L.: A new UK scale ocean-wave-river modelling system for predicting extreme sea levels at the coast , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6248, https://doi.org/10.5194/egusphere-egu24-6248, 2024.

On-site presentation
Vladimir Santos da Costa, Giorgia Verri, Murat Gunduz, Alessandro De Lorenzis, Luca Furnari, Alfonso Senatore, Jacopo Alessandri, and Lorenzo Mentaschi

This comprehensive study, conducted within the AdriaClim project, offers a detailed exploration of the multifaceted impacts of climate change on the Adriatic Sea. We propose a limited area climate downscaling with mesoscale integrated modeling of the Adriatic water cycle, including the atmosphere, hydrology and marine thermo-hydrodynamics. The analysis covers the climate window between 1992 and 2050, under the high emission scenario RCP8.5 Examination of Sea Surface Temperature (SST) patterns, revealing a discernible warming trend, particularly along the continental slope influenced by the Western Adriatic Coastal Current. This regional warming has substantial implications for the delicate balance of the Adriatic Sea's ecosystems and underscores the need for targeted adaptive measures.

Marine Heatwaves (MHWs) exhibit both increased duration and intensity during the projection period. This emphasizes the imminent ecological and socio-economic repercussions, necessitating a proactive approach in policy formulations. The study delves into the intricacies of Brunt–Väisälä frequency analysis, unraveling alterations in ocean circulation and heat transport. This comprehensive understanding of regional climate impacts is crucial for informed decision-making in climate adaptation strategies.

Sea Level Rise (SLR) dynamics are explored in detail, showcasing nuanced spatial variations. A latitudinal decrease towards the northeast and heightened levels along the west coast are identified. The mid-term projection indicates a steric-driven increase in SLR, highlighting the importance of region-specific considerations and factors influencing sea level changes. These findings contribute significantly to the broader discourse on global sea-level rise and its regional variations.

A pivotal aspect of the study addresses the impact of projected changes in river release on local density stratification and SLR. Projections indicate a mid-term future decrease of approximately 35% in river release, affecting the Northern and Southern sub-basins differently. The Northern sub-basin will experience salinization prevailing on heating through the whole water column due to the projected runoff decrease, resulting in dense water formation increase and moderated sea level rise. Conversely,  the runoff decrease will have a lower impact in the Southern sub-basin where the future changes of other mechanisms may play a major role, making heating prevailing on salinization at intermediate to deep water column, resulting in lower dense water formation and higher SLR.

This integrated analysis underscores the intricate dynamics of regional climate impacts on the Adriatic Sea. The interplay of warming trends, altered ocean stratification, intensified MHWs, and river release dynamics demands a holistic approach to climate adaptation. Despite the significant strides made, the study acknowledges certain limitations, such as the absence of land subsidence models. The dynamic nature of the Adriatic Sea and the evolving landscape of climate change necessitate continuous monitoring and refinement of models for heightened accuracy in future projections.

The study serves as a testament to the importance of integrated research with a more comprehensive representation of the local water cycle at high time and space resolutions emphasizing the imperative of harmonizing scientific insights with pragmatic policy implementations.

How to cite: Santos da Costa, V., Verri, G., Gunduz, M., De Lorenzis, A., Furnari, L., Senatore, A., Alessandri, J., and Mentaschi, L.: Climate Change Impacts on the Adriatic Sea: Integrating Sea State Indicators and River Release Dynamics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9126, https://doi.org/10.5194/egusphere-egu24-9126, 2024.

On-site presentation
Beatrice Maddalena Scotto, Antonio Novellino, Giovanni Besio, and Andrea Lira Loarca


The Mediterranean Sea is facing escalating environmental threats due to increasing maritime activities, resulting in increased marine pollution. Effectively addressing these challenges necessitates an expanded focus on reliable monitoring services to predict and mitigate the impacts of pollution spills. This study aims to comprehensively understand the dynamics of oil spills in the Mediterranean region, with the objective of establishing a robust and user-friendly framework for an application. This application not only assesses historical oil spill events but also elucidates the intricate interplay of environmental factors, serving as a predictive tool for effective monitoring and planning.


The study centers on determining the dispersion velocity of pollutants, accounting for three significant contributions influencing spill movement: surface velocity of currents, Stokes drift induced by waves, and wind influence within the initial 10 meters above the sea surface. These contributions are fine-tuned using coefficients, building on established methodologies. Data integration from three oceanic models—Copernicus Marine Environmental Monitoring Service (CMEMS), Naval Hydrographic and Oceanographic Service (SHOM), and the French Research Institute for the Exploitation of the Sea (IFREMER)—provides a nuanced analysis of surface current velocities, addressing uncertainties within the ensemble.

The dispersion simulation utilizes the OceanParcels Lagrangian Particle Tracking Model (PTM), tailored to specific events. The analysis includes the temporal and spatial evolution of oil slicks, determining particle release parameters, and evaluating centroids at each moment. Comparison with Synthetic Aperture Radar (SAR) satellite imagery refines model precision, offering real-world validation and aiding in model selection for accurate environmental protection decision-making.


Validation with a real-case scenario, a shipping accident off the coast of Corsica in October 2018, reveals distinctive trajectories among models (Figure1). Integrating wind and Stokes drift refines outputs, with notable alignment to observed events, showcasing enhanced predictive capabilities, especially during the detection of hydrocarbons in France on October 16th (Figure2).

Figure 1(left) – Models trajectory simulated with sea surface currents, showing the evolution of the centroid trajectory and particle distribution in space and time. On the right the color bar showing the values of the particles’ experimental distribution. Figure 2 (right) -Trajectories simulated with the contributions of sea surface currents, wind at 10m and stokes drift, showing the evolution of the centroid trajectory and particle distribution in space and time.

Graphical representations illustrate the spatio-temporal evolution of the particle cloud, providing comprehensive insights into oil spill movement.


Despite evident progress, persistent uncertainties in climate services pose challenges in predicting and mitigating oil spill impacts. Sustained investments in research and development for climate monitoring services are crucial for addressing uncertainties and ensuring the long-term sustainability of the Mediterranean ecosystem. The future lies in refining models, integrating high-resolution data, and advancing climate monitoring to enhance prediction accuracy and minimize environmental repercussions from pollutant spills in the Mediterranean basin.

How to cite: Scotto, B. M., Novellino, A., Besio, G., and Lira Loarca, A.: Multi-model statistical system for monitoring the dispersion of pollutants: Mediterranean case study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18780, https://doi.org/10.5194/egusphere-egu24-18780, 2024.

Coffee break
Chairpersons: Giorgia Verri, Joseph Ansong, Anna Katavouta
On-site presentation
Giovanni Coppini, Villy Kourafalou, Joaquín Tintoré, Emma Heslop, Jo Hopkins, Miguel Charcos Llorens, Mairéad O’Donovan, and Nadia Pinardi

The CoastPredict Programme, an endorsed Programme of the Ocean Decade, has established a central framework for coordination and practical implementation called ‘GlobalCoast’. GlobalCoast will coordinate implementation and integration of the science and technology advances from CoastPredict’s six Focus Areas at Pilot Sites in a range of contrasting Regions of the Global Coastal Ocean, using and developing best practice principles in observing, data management, modelling and co-design. The Programme Focus Areas projects address priorities related to coastal resilience including: Integrated observing and modelling for short term coastal forecasting and early warnings; Future Coastal Ocean climates: Earth System observing and modelling; Solutions for integrated coastal management; Coastal information integrated in an open and free international exchange infrastructure; Equitable coastal ocean capacity.

GlobalCoast will overcome a number of existing barriers including: the lack of an international network for Global Coastal Ocean innovation and solutions for integrated observing and prediction, and associated fragmentation of knowledge; the particular challenge regarding open and free data access in the Global South; the lack of end-user (coastal managers / communities) involvement and the long timeframe currently required to demonstrate solutions.  

Through GlobalCoast, CoastPredict will demonstrate (at Pilot Sites) an integrated observing and predicting system for the global coastal ocean and create globally replicable solutions, standards, and applications that enhance coastal resilience. A global digital cloud-based infrastructure will be key to acceleration - the cloud-based computing platform will enable accelerated data collection and open and free data sharing, and advancement of modelling and analysis tools, aligned with best practices.

How to cite: Coppini, G., Kourafalou, V., Tintoré, J., Heslop, E., Hopkins, J., Charcos Llorens, M., O’Donovan, M., and Pinardi, N.: CoastPredict: the GlobalCoast framework for accelerated coordination of an integrated global system for coastal ocean observing and prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21906, https://doi.org/10.5194/egusphere-egu24-21906, 2024.

On-site presentation
Juan Carlos Herguera and the CIGOM

The need to understand and forecast disasters driven by anthropogenic and natural forces in the Gulf of Mexico and to support management responses to hazardous events led policymakers, scientists, and industry representatives in Mexico to launch an ocean observation and modeling project (2015–2023) aimed at collecting multi-layered baseline information and continuous monitoring of the ocean environment across the southern Gulf of Mexico. We will show the observational network and modeling efforts, led by the Research Consortium for the Gulf of Mexico (CIGoM), include developing a marine hazard warning system to investigate the multiple stressors that are altering the state and health of this large marine ecosystem and its coastal communities. This warning system is intended to aid in establishing of national contingency plans and mitigate the impacts of extreme events and long-term ocean trends. Stressors include hydrocarbon spills, tropical cyclones, marine heatwaves, long-term ocean surface warming, and harmful algal blooms. In this talk we will present part of our work related to the early warning system we have developed involving satelita detection, forecasting models and impact assessments of some oil spills that have occurred in the past few months in this region.

How to cite: Herguera, J. C. and the CIGOM: Ocean Monitoring and Prediction Network for the Sustainable Development of the Gulf of Mexico, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4170, https://doi.org/10.5194/egusphere-egu24-4170, 2024.

On-site presentation
Laurent Amoudry, Elina Apine, Sara Kaffashi, Constantinos Matsoukis, Marta Meschini, Marta Payo Payo, Amani Becker, Kenisha Garnett, Simon Jude, Claire Evans, Stephen Jay, Francisco Mir Calafat, Andy Plater, Leonie Robinson, Joanna Zawadzka, Jennifer Brown, Richard Dunning, Anil Graves, and Tim Stojanovic

Coastal hazards pose a significant risk to people, property, and infrastructure worldwide. They will be increasing over the next century mainly driven by sea level rise. Managing the coast in a sustainable way requires understanding the impacts under a changing climate of actions done and decisions taken now. This often relies on exploring the response of coastal systems to changing natural and/or anthropogenic drivers using modelling tools. The experimental design of such modelling work is essential in providing the robust scientific evidence needed to underpin effective coastal management. Yet, this experimental design often remains rooted within disciplinary silos and may not take a holistic view of the whole coupled human-environment coastal system. We will explore how considering whole coastal social-ecological systems and social acceptance can shape the experimental design of modelling coastal impacts under a changing climate, and lead to better scientific evidence.

We will present a new integrated, transdisciplinary system-based framework that brings together the provision of a conceptual representation of the complex coastal social-ecological system and consideration of key drivers in this, modelling coastal flooding and valuing ecosystem services now and into the future, and the influence of social perceptions and values. We will illustrate our approach with case studies across the United Kingdom. We will also discuss the benefits and challenges of following a transdisciplinary approach with respect to common coastal managements ambitions, such as improving coastal resilience, promoting a transition towards greener nature-based solutions, and following national and/or global net-zero and net gain objectives.

Our case studies span a range of coastal systems across three nations of the United Kingdom in order to provide examples of different policies and interventions as well as different environmental drivers. Our work builds on the outcomes of a  transdisciplinary capacity-building workshop, which highlighted the need for robustness, consistency, and communication when developing modelling scenarios. We use Fuzzy-Cognitive Mapping to elicit maps of generic coastal social-ecological systems. This is complemented by Soft System Modelling of coastal scheme decision making. We use questionnaire surveys and focus groups combined with Q-sort methodology to define and rank key factors in social acceptability of coastal schemes. Numerical modelling of coastal flooding relies on nested implementations of DELFT3D and SFINCS (Super-Fast INundation of CoastS) models for our case studies. Economic assessment and cost benefit analyses are grounded in the CICES framework and use GIS for habitat mapping to identify the extent and value of various habitats and assess potential flood losses. Overlaying social and flood maps for different scenarios ensures a thorough understanding of impacts, aiding informed decision-making. This approach integrates current habitat conditions with future change projections, essential for effective environmental management and policy planning.

Applying these methods for our case studies, and bringing them together via morphological analysis, our results show that Fuzzy-Cognitive Mapping, Soft System Modelling, Q-sort, and focus groups all provide valuable information and change the optimal output of the experimental design and selection of scenarios. The outcome is that the scientific evidence produced becomes more useful, useable, and trusted.

How to cite: Amoudry, L., Apine, E., Kaffashi, S., Matsoukis, C., Meschini, M., Payo Payo, M., Becker, A., Garnett, K., Jude, S., Evans, C., Jay, S., Mir Calafat, F., Plater, A., Robinson, L., Zawadzka, J., Brown, J., Dunning, R., Graves, A., and Stojanovic, T.: Transdisciplinary co-design to assess impacts of climate change on coastal schemes., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7495, https://doi.org/10.5194/egusphere-egu24-7495, 2024.

On-site presentation
Anabela Oliveira, Marta Rodrigues, Isa Elegbede, Gonçalo Jesus, André Fortunato, Ricardo Martins, and Alberto Azevedo

The concept of open forecast data has been gaining importance throughout the world, whether they address global, regional or local dynamics.  Most forecast systems in operation, however, just publish images, without providing quantified predictions that could be used to produce new services and have a greater societal impact. The initiatives under the UN Ocean Decade such as CoastPredict (https://www.coastpredict.org/) and DITTO (https://ditto-oceandecade.org/) programs aim at opening forecast information to all and address dynamics from the global to the coastal dimension.

Setting up model or forecast systems are complex tasks that require considerable expertise of coastal dynamics, numerical modeling and computer science. In the last few years, several initiatives have emerged to provide simplified ways to address this challenge and provide user-friendly tools to set up models and their forecast systems, with automatic linkage to global or regional forcings and access to data comparison in near real time. These on-demand forecast platforms aim at expanding the application of forecast systems worldwide, allowing for a broad implementation of decision support and emergency tools thus being an integral part of Digital Twins creation for coastal areas. Examples include SURF (Trotta et al., 2021), Delft-FEWS (Delft-Flood Early Warning System, Werner et al., 2013) and OPENCoastS (Oliveira et al., 2019, 2021).

Herein the OPENCoastS service and web platform are used to illustrate the creation of a core coastal Digital Twin for a data-poor region, using model outputs to compute relevant indicators for fisheries. The application site is the coast of Nigeria in Africa and CMEMs global data is used both to force the predictions and to evaluate its results through comparison with remote sensing products. Indicators suitable for fisheries sustainable operation are presented, developed in close collaboration with local players. This demonstration showcases the importance of on-demand forecast platforms and their role in the construction of Digital twins, facilitating the implementation of the UN Decade goals. The proposed methodology can be expanded in the future to other coastal regions in the scope of the UN Decade WOLLF project, supported by the human and computational resources provided by the ATTRACT European Digital Innovation Hub project.


Trotta, F., Federico, I., Pinardi, N., Coppini, G., Causio, S., Jansen, E., Iovino, D., Masina, S., 2021. A Relocatable Ocean Modeling Platform for Downscaling to Shelf-Coastal Areas to Support Disaster Risk Reduction. Front. Mar. Sci. 8, 642815. https://doi.org/10.3389/fmars.2021.642815.

Werner, M., Schellekens, J., Gijsbers, P., van Dijk, M., van den Akker, O., Heynert, K., 2013. The Delft-FEWS flow forecasting system. Environmental Modelling & Software 40, 65–77. https://doi.org/10.1016/j.envsoft.2012.07.010

Oliveira, A., A.B. Fortunato, M. Rodrigues, A. Azevedo, J. Rogeiro, S. Bernardo, L. Lavaud, X. Bertin, A. Nahon, G. Jesus, M. Rocha, P. Lopes, 2021. Forecasting contrasting coastal and estuarine hydrodynamics with OPENCoastS, Environmental Modelling & Software, Volume 143,105132, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2021.105132.

Oliveira, A., A.B. Fortunato, J. Rogeiro, J. Teixeira, A. Azevedo, L. Lavaud, X. Bertin, J. Gomes, M. David, J. Pina, M. Rodrigues, P. Lopes, 2019. OPENCoastS: An open-access service for the automatic generation of coastal forecast systems, Environmental Modelling & Software, Volume 124, 104585, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2019.104585

How to cite: Oliveira, A., Rodrigues, M., Elegbede, I., Jesus, G., Fortunato, A., Martins, R., and Azevedo, A.: Using on-demand prediction services to build user-tailored coastal Digital Twins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8719, https://doi.org/10.5194/egusphere-egu24-8719, 2024.

On-site presentation
Joanna Staneva, Nadia Pinardi, Giovanni Coppini, Benjamin Jacob, Wei Chen, Philip-Neri Jayson-Quashigah, Jacopo Alessandri, Lorenzo Mentaschi, and Yann Drillet

Digitalization, particularly through the utilization of digital twins of the ocean, can play a significant role in advancing the sustainable development of the marine environment. The Digital Twin (DT) creates a digital replica of the ocean, enabling testing of various What-If scenarios, such as the impacts of sea level rise, Nature-Based Solutions (NBS), as well as the effectiveness of mitigation and adaptation plans. DTs provide insights into ocean conditions, ecosystems, and human effects, guiding decisions for sustainable resource use. DT-based What-If scenarios in NBS foster cooperation among stakeholders in shared oceanic spaces, enabling data-driven decisions and collaboration. This platform serves for decision-making and management strategies aimed at fostering the sustainable utilization of ocean resources. Such an application is a Digital Twin strategy in designed experiments for nature-based solutions.  It can be employed to evaluate the effects of sea level rise and wave actions on seagrass meadows; and evaluate different management approaches to enhance resilience, while assessing diverse management tactics for bolstering resilience. We demonstrate how Digital twins of the coastal ocean can contribute by aiding decision-making through the use of Whar-If scenarios for coastal protection against erosion and sediment transport by sea level rise, while also ensuring the preservation of coastal biodiversity. By monitoring and optimizing solutions through digital twins, effectiveness and long-term sustainability are heightened, necessitating collaborative efforts for coastline protection and ecosystem preservation.

To further support the coastal restoration (e.g. of seagrass meadows), digital twin technology can be utilized to monitor and model the climate (e.g. sea level rise) and human induced effects on coastal ecosystems. The collaborative efforts for nature based solution as coastline protection and ecosystem preservation are demonstrated in various coastal areas around the Global coast (e.g., in the North Atlantic, Wadden Sea coast, Danube-western Black Sea, Mediterranean coast, Eastern coast of Ghana) In the context of nature-based solutions, a digital twin help identifying areas where seagrass is most vulnerable to the impacts of sea level rise and evaluate different management strategies to promote resilience. In addition to seagrass restoration, other nature-based solutions can effectively address the impacts of sea level rise. These solutions include the restoration of wetlands, dunes, and mangroves, as well as the implementation of green infrastructure such as bioswales and green roofs. Coastal communities can play a critical role in implementing and supporting nature-based solutions. This includes engaging in community-based monitoring and restoration efforts, as well as advocating for policies that prioritize nature-based solutions for coastal protection. The integration of digital twin technology with community efforts can foster a collaborative and data-driven approach to sustainable coastal management and resilience.

How to cite: Staneva, J., Pinardi, N., Coppini, G., Jacob, B., Chen, W., Jayson-Quashigah, P.-N., Alessandri, J., Mentaschi, L., and Drillet, Y.: Advancing Marine Sustainability through Digital Twin What-If Scenarios in Nature Based Solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14266, https://doi.org/10.5194/egusphere-egu24-14266, 2024.

Virtual presentation
Cristina Forbes, Mairéad O’Donovan, and Giovanni Coppini

Search and rescue planning tools and programs use surface currents and wind data to perform drift simulations to determine the approximate location of persons lost at sea. Access to accurate ocean and atmospheric modeling forecast data and real-time observations is critical for drift modeling simulations to enable targeted SAR operations and planning, and narrowing of search areas in the marine environment, thus saving lives at sea.

The United States Coast Guard (USCG) employs the Search and Rescue Optimal Planning System (SAROPS) for search and rescue (SAR) and planning. SAROPS accesses more than 100 environmental global and local ocean and meteorological surface currents and wind products through the Environmental Data Server (EDS) to perform thousands of Monte Carlo drift simulations and generate time-evolving probability maps which depict the envelope of the search area.

The accuracy of ocean and atmospheric models combined with observations is essential to save lives. Real-time measurements are critical in:

1) areas covered by two or more models which render current speeds/directions that do not match,

2) areas where one model is not accurate at that particular time and location,

3) remote areas (e.g. small islands in the Pacific Ocean) where ocean dynamics are not adequately represented by the global models available.

Inaccuracy in model data becomes very challenging for SAR of mariners lost at sea because searches will be conducted in wrong locations, thus delaying the rescue and expending resources.

Observations from drifters and observational networks are essential for additional SAR guidance. 95% of SAR cases are within 20 NM from the coast. 

The U.S.C.G. deploys self-locating datum marker buoys (SLDMB), Davis-style oceanographic surface drifters, from aircrafts and vessels to provide real-time currents and assist in determining the best model that matches the observations to use for drift modeling and planning. Other oceanographic measurements useful for SAR are near real-time surface currents from High Frequency Radar (HFR) networks which provide continuous maps of ocean surface currents within 200 km of the coast at high spatial (1–6 km) and temporal resolution (hourly or higher).  HFR surface currents are used for model validations, for assimilation into models, and for input to the Short-Term Predictive System (STPS), a forecast model based on HFR - all products used in SAROPS.

Collaboration between the US Coast Guard and CoastPredict’s Predict-on-Time Core Project is intended to have particular impact in remote areas, e.g. remote islands where low-resolution models restrict the efficacy of drift modeling simulations for SAR. Small islands and atolls of the Pacific rely on ocean resources for their subsistence with limited technology so the likelihood of having a distress incident is higher. Getting search and rescue units (SRUs) to those remote areas requires additional time, so the uncertainty in the location of the lost craft or persons becomes larger. Access to more accurate, high-resolution models is critical and the international network for collaboration established by CoastPredict offers an opportunity to leverage existing knowledge and a shared digital infrastructure to improve capacity in areas currently under-resourced. 

How to cite: Forbes, C., O’Donovan, M., and Coppini, G.: Saving lives at sea: Integration of Oceanographic Models and Observations to Improve Coastguard Search and Rescue Operations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21569, https://doi.org/10.5194/egusphere-egu24-21569, 2024.

On-site presentation
Constantinos Matsoukis, Marta Payo Payo, Elina Apine, Sara Kaffashi, Marta Meschini, Amani Becker, Kenisha Garnett, Simon Jude, Claire Evans, Stephen Jay, Francisco Mir Calafat, Andy Plater, Leonie Robinson, Joanna Zawadzka, Jennifer Brown, Richard Dunning, Anil Gaves, Tim Stojanovic, and Laurent Amoudry

Most coastal areas around the world are currently at risk of flooding, which is increasing due to sea level rise and other impacts of a changing climate. The design of appropriate flood protection policies and schemes is thus becoming more imperative. Partly in response to net zero and net gain agendas, coastal practitioners across sectors have started to champion ‘greener’ nature-based solutions in place of traditional hard coastal defences. However, social acceptance is limited, and examples worldwide are too scarce to fully test and demonstrate the efficiency and societal benefits of nature-based solutions. Appropriate case studies are required to build the knowledge and evidence base needed for the implementation of nature-based solutions.  

In this study, the efficiency of nature-based solutions (e.g., managed realignment) against flooding is investigated for an estuarine case study in Scotland. The Forth Estuary is one of UK’s most important estuarine ecosystems both for economic and ecological reasons. In recent years, flooding events have considerably affected urban areas and infrastructure along the estuary. The frequency and intensity of such events is expected to increase due to climate change and result in significant adverse impacts on local population and economy. Airth is a village situated in the south bank of the inner Forth Estuary. It is a residential area that covers 5500 hectares of agricultural land with some woodland as well. Part of it is designated as a conservation area because of its significant historical background. However, it is often subject to coastal and/or surface water flooding. The local authority has launched a management plan strategy for flooding mitigation seeking adaptation solutions. 

 A 2D numerical model has been built in Delft3D-FM to determine the hydrodynamic setup in the Forth estuary. The model encompasses a large area starting from the inland tidal limit and including both the inner and outer Forth estuary. It is forced upstream by river discharge and downstream by water level time series. To account for additional flood drivers such as wave set-up, run-up, and wind-driven surges, a second model is built in SFINCS with a finer resolution and with its extents locally restrained around the Airth coast. Modelling scenarios comprise at first a series of hindcast simulations performed to reproduce the impact of three recent storm events that largely affected the local community by causing extensive inundation and flooding of properties. The simulations are then repeated with bathymetry adaptations to represent interventions (i.e., managed realignment) into the model and compare their effect against flooding. In addition, simulations with future sea level scenarios are considered to assess these interventions efficiency under a changing climate. As events of similar or higher intensity can be expected in the future, model results can give a good indication of how the system responds when the nature-based defences are in place. These can assist, advise, and direct stakeholders and local authorities to consider alternative and state-of-the-art solutions in their fight against coastal flooding impact. 

How to cite: Matsoukis, C., Payo Payo, M., Apine, E., Kaffashi, S., Meschini, M., Becker, A., Garnett, K., Jude, S., Evans, C., Jay, S., Calafat, F. M., Plater, A., Robinson, L., Zawadzka, J., Brown, J., Dunning, R., Gaves, A., Stojanovic, T., and Amoudry, L.: Investigating the efficiency of nature-based solutions against estuarine coastal flooding under present and future conditions , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11438, https://doi.org/10.5194/egusphere-egu24-11438, 2024.

On-site presentation
Alessandro De Lorenzis, Giorgia Verri, Vladimir Santos Da Costa, Nadia Pinardi, Giovanni Coppini, Albert Sorolla, Adrian Löchner, and Eugènia Martí

Estuarine zones are particularly vulnerable coastal areas as consequence of the changing climate. The river flow decrease, RD, and the sea level rise, SLR, are leading to: (i) salinization of the surface and subsurface catchment waters, (ii) salt-wedge intrusion SWI moving more and more inland, with a non-linear response to the main drivers of the estuarine dynamics.

 The current study uses a one-dimensional two-layer estuary box model, the so-called CMCC EBM (Verri et al 2020; 2021) which solves the estuarine water exchange by means of two conservation equations for volume and salt fluxes averaged over the diurnal tidal cycle, plus two parametric equations estimating the SWI length and the along-estuary diffusivity.
  The EBM has been applied to the Po di Goro branch of the Po river delta, which is characterised by a river-dominated estuary flowing into the micro-tidal Northern Adriatic Sea. A strength of the EBM here proposed is the extremely low computational time which makes it particularly suitable for climate purposes by bridging the gap between available hydrology and marine hydrodynamics projections which reach at most the mesoscale with high computational costs and without representing the estuarine transitional areas. Additional assets are the minimal data storage and no need to postprocess the results as the SWI length is among the model outcomes. On the other hand, a proper tuning of the parametric equations is required and this was made possible by an accurate in-situ monitoring and a site specific “learning dataset” built upon the outcomes of a 3D unstructured modelling of the Po delta system.

  Considering that there are few studies devoted to the impacts of the local SLR on the SWI and the salinity of estuaries in micro-tidal environments, one of the aims of this study is to expand the knowledge on this topic by proving future projections for the selected test-case.  

  Moreover, the increasing salinization of the Po di Goro estuary threats the local economy and the ecosystem health. Thus, the second aim of this study is to evaluate a Nature-Based-Solution, NBS, to mitigate the SWI, i.e. we assess the salt uptake capability of the Atriplex portulacoides within our modelling study.

Three climate experiments with the EBM have been carried out over 1991-2100 with a ‘mechanistic’ approach: (i) Exp1 is a full-forcing experiment with the river inflow (volume flux at the estuary head) and the seawater inflow (volume flux, salinity and sea level at the estuary mouth) provided by a regional climate model RCM considering RCP 8.5; (ii) Exp2 is a twin experiment of Exp1 but neglecting the sea level among input forcings of the EBM; (iii) Exp3 is a twin experiment of Exp1 but with a reduced salinity of the seawater inflow by assuming that 20% of the estuary water volume interacts with the halophytes planted along the estuary banks.

We propose a discussion on the relative role of the SLR and the RD in determining the future projections of the Po di Goro estuarine dynamics and the potential effect of a site-specific NBS.

How to cite: De Lorenzis, A., Verri, G., Santos Da Costa, V., Pinardi, N., Coppini, G., Sorolla, A., Löchner, A., and Martí, E.: Future projections of a salt-wedge estuary under a changing climate: impact of the sea level rise and evaluationof a nature-based-solution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11959, https://doi.org/10.5194/egusphere-egu24-11959, 2024.

On-site presentation
Xuege Wang, Fengqin Yan, and Fenzhen Su

Coastal areas are important and functional regions due to their location and abundant natural resources that support human life and certain industries, while these areas are also ecologically vulnerable and have experienced dramatic changes due to both human activities and natural factors. In this article, remote sensing and geographic information system technology are utilized to extract and analyze the spatiotemporal changes in China’s coastline from 1980 to 2018. Additionally, the study introduces the Ecosystem Service Values (ESVs) evaluation method to quantitatively assess the impact of coastline changes on coastal ESVs. Results indicate that from 1980 to 2018, the length of China's mainland coastline increased by 10.2%, characterized by a significant increase in artificial and a sharp decrease in natural coastlines. Aquaculture ponds were the type of coastline with the most increase, followed by construction land and ports. Bedrock coastline was the type of coastline with the most reduction, followed by sandy and muddy coastlines. Over the past four decades, the changes in coastline have led to a decrease of $6.83 billion in ESV in China's coastal zone. Therefore, protecting and restoring China's natural coastline should be highly prioritized. Local authorities should evaluate the ecological environment of specific coastal zones in a timely and effective manner using big data and decision-making tools, and provide feedback to guide the adjustment and implementation of relevant national/regional policies.

How to cite: Wang, X., Yan, F., and Su, F.: Coastline migration and restoring recommendations in China , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16644, https://doi.org/10.5194/egusphere-egu24-16644, 2024.


Posters on site: Tue, 16 Apr, 16:15–18:00 | Hall X4

Display time: Tue, 16 Apr 14:00–Tue, 16 Apr 18:00
Chairpersons: Jacopo Alessandri, Abe Woo, Giorgia Verri
Are near-Coastal Sea Levels Accelerating Faster than Global during the Satellite Altimetry Era?
Svetlana Jevrejeva, Ying Qu, and Hindumathi Palanisamy
Anna Katavouta, Jason Holt, Yuri Artioli, Giovanni Galli, James Harle, Lee de Mora, Sarah Wakelin, and Chris Wilson

The latest generation CMIP6-class Earth system models (ESMs) are a great tool for projecting climate variability on multi-centennial and global scales as they are designed to explicitly represent the process-coupling amongst the different Earth system components (atmosphere, ocean, land, cryosphere, biosphere) and prioritise system robustness such as minimisation of drift. However, CMIP6-ESMs do not accurately represent the fine-scale circulation and water-masses in ocean margins and shelf seas since by design: (i) their resolution is too coarse and so they only implicitly include regional-scale processes or even exclude these processes (particularly shelf-seas related processes); and (ii) their initialisation from a steady state leads to their divergence from reality and present-day conditions. To address these shortcomings and project the impacts of climate change in the Atlantic Ocean with focus on regional scales, we downscale globally an ensemble of future ocean projections with a NEMO-ERSEM coupled hydrodynamic-ecosystem model. Here, we discuss the design-methodology for our global ocean downscaling experiment: (i) selection of future scenarios, (ii) initialisation from “real” ocean conditions, (iii) selection of the CMIP6-ESMs atmospheric conditions to force our model based on their realism and uncertainty span, and (iv) treatment of the river runoffs as to impose both a realistic rivers state and a future trend consistent with CMIP6-ESMs. Comparisons of our global ocean downscaling simulations to CMIP6-ESMs during the historical period demonstrate their added value in terms of representation of physical ocean conditions and circulation in the Atlantic Ocean. We also present preliminary analysis in terms of future trends in temperature, salinity and circulation patterns in the Atlantic Ocean, with focus on regional features like changes in the Gulf Stream and trends in coastal regions.

How to cite: Katavouta, A., Holt, J., Artioli, Y., Galli, G., Harle, J., de Mora, L., Wakelin, S., and Wilson, C.: Projecting the impacts of climate change in the Atlantic Ocean: a global ocean downscaling approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6367, https://doi.org/10.5194/egusphere-egu24-6367, 2024.

Changshui Xia, Youting wu, Fangli Qiao, and Chalermrat Sangmanee

      Gulf of Thailand is a semi-closed shallow water basin connected to the South China Sea. The seasonal Circulation patterns in the Gulf of Thailand and its water exchange with the South China Sea are studied using the CROCO model from 2017 to 2020. The simulated temperature, salinity, Sea surface height and current field agree with the observation well.  Based on the model result, the upper flow velocity of the horizontal flow field in the Gulf of Thailand a is greater than the lower flow velocity, which means that the Ekman flow driven by the monsoon dominates the upper flow field. Winter and summer are the strongest periods of the monsoon in the sea area, as well as the strongest periods of water exchange between the Gulf of Thailand and the South China Sea. In winter, the upper layer of South China Sea water flows into the Gulf of Thailand, causing an increase in SSH, causing the middle layer of seawater to sink and flow out from the bottom; In summer, the upper layer of Gulf of Thailand water flows out, SSH decreases, the middle layer of seawater surges up to supplement the surface deficiency, and the bottom layer of South China Sea water flows in to compensate.  

Keywords: Gulf of Thailand, Circulation patterns, Gulf of Thailand, South China Sea

How to cite: Xia, C., wu, Y., Qiao, F., and Sangmanee, C.: Numerical Study on the Circulation in the Gulf of Thailand and Its Seasonal Water Exchange with the South China Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19635, https://doi.org/10.5194/egusphere-egu24-19635, 2024.

Birte-Marie Ehlers, Jian Su, and Frank Janssen

The German Strategy for Adaptation to Climate Change (Deutsche Anpassungsstrategie - DAS) is the political framework to climate change adaptation in Germany and laid the foundation to prepare for the impacts of climate change and reduce climate risks in a continuous process. The DAS core service “Climate and Water” provides monitoring and projection data to evaluate requirements for climate change adaptation.

An ensemble of regional climate ocean simulations is provided to tackle a large number of questions on the topics of sea level, water temperature, salinity and currents. The regional climate ocean simulation ensemble is based on atmospheric forcing from five members of the EURO-CORDEX ensemble. The simulations were calculated for a thirty-year ”historical” period (1971-2000), a thirty-year ”near future” period (2031-2060) and one for the ”far future” (2071-2100) for the RCP8.5 scenario.In this study, we focus on the evaluation of the sea surface temperature (SST), which has a major impact on the ecosystem and therefore must be part of the adaptation strategy. A comparison of all SST model results with observational data provides a bias correction of the individual ensemble members, which than is also applied to the projected data. The ensemble approach is examined with respect to substantial uncertainties and different types of ensemble representation are discussed. The results are prepared for policymakers and practitioners engaged in coastal risk assessment and adaptation planning and will be available on the internet at https://das.bsh.de.

How to cite: Ehlers, B.-M., Su, J., and Janssen, F.: Adaptation to climate change: Regional sea surface temperature scenarios for the North Sea and the Baltic Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10061, https://doi.org/10.5194/egusphere-egu24-10061, 2024.

Physical Oceanographic Aspects of Upwelling Events on the Southeast Florida Shelf
Alexander Soloviev, Alfredo Quezada, Megan Miller, Bernhard Riegl, and Richard Dodge
Fucang Zhou, Zhi Chen, Dongyan Liu, Ruishan Chen, Changsheng Chen, Karline Soetaert, and Jianzhong Ge

Increasingly severe and massive floating macroalgal blooms pose significant challenges to the prediction and management of coastal and ocean environment. This study introduces the Floating Macroalgal Growth and Drift Model (FMGDM), a physical-ecological model that tracks, replicates, and extinguishes Lagrangian particles to dynamically simulate the growth and drift pattern of floating macroalgae. The model updates the position, velocity, quantity, and represented biomass of these particles synchronously within its tracking and ecological modules. The macroalgal ecodynamic processes are driven by the oceanic physical-biochemical environments of hydrodynamics, temperature, nutrients, and atmospheric conditions. With the support of the hydrodynamic model and biological macroalgae data, FMGDM can serve as a model tool to forecast floating macroalgal blooms. We developed a forecasting system for large-scale floating macroalgal blooms, which integrates the FMGDM with the Finite-Volume Community Ocean Model (FVCOM). This system is capable of predicting the physical-biogeochemical environment and macroalgal ecodynamic processes in the regional ocean. Biological parameters for this model were specifically derived from culture experiments of Ulva prolifera, a phytoplankton species causing the largest worldwide bloom of green tide in the Yellow Sea, China. With real-time multi-resource satellite data, the system successfully applied to predict green tide events in the Yellow Sea for 2021-2023. The prediction accuracy of coverage can reach 67.0%, and the minimum error of green tide center of mass is 7.39 nautical miles for total coverage of 52.01 km2 and prediction duration of 7 days. Supported by regional marine data and macroalgal physiological characteristics, this system can be expanded to other similar floating macroalgal blooms.

How to cite: Zhou, F., Chen, Z., Liu, D., Chen, R., Chen, C., Soetaert, K., and Ge, J.: Predicting massive floating macroalgal blooms in the regional sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19811, https://doi.org/10.5194/egusphere-egu24-19811, 2024.

Zehua Zhong, Hachem Kassem, Ivan Haigh, Dafni Sifnioti, and Ben Gouldby

A fundamental requirement for the development of nuclear power stations is an evaluation of the risk and exposure to external hazards that may challenge nuclear safety. These hazards are often driven by a wide range of meteorological, oceanographic, and geomorphological processes which act on varying spatial and temporal scales. For coastal flooding and erosion, assessing the hazard potential requires consideration of both the local wave and water level variations and the associated regional weather conditions. Fortunately, the development of downscaling techniques offers useful tools for transferring large-scale climate forcings to local impacts. This research aims to conduct probabilistic assessments of coastal hazard exposure at a nuclear power station in the UK by using a hybrid downscaling framework. First, a weather typing method is employed to statistically downscale from regional atmospheric conditions to coastal waves and storm surges at the Hartlepool nuclear power station, which will be further downscaled to coastal flooding and erosion using physics-based dynamical models. We performed a sensitivity analysis to determine what parameters are significant in weather typing to downscale waves and storm surges. The resulting weather types and their associated wave climate and surge conditions are useful in identifying weather patterns related to extreme wave and surge events, which helps to reduce the computational effort in dynamical downscaling by focusing on those coastal-risk weather types and investigating their impacts. The sensitivity analysis reveals that the inclusion of the gradient of sea level pressure as the predictor and the use of local predictands to guide the classification of weather types are important to improve the model performance. 

How to cite: Zhong, Z., Kassem, H., Haigh, I., Sifnioti, D., and Gouldby, B.: Probabilistic Assessment of Exposure to Coastal Hazards at a Nuclear Power Station Development Site in the UK, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8413, https://doi.org/10.5194/egusphere-egu24-8413, 2024.

Jian Su, Birte-Marie Ehlers, Jacob Woge Nielsen, Kristine Skovgaard Madsen, Morten Andreas Dahl Larsen, and Frank Janssen

Sea level rise is a significant threat to coastal regions, requiring thorough scientific evaluations to implement effective adaptation planning. Recent incidents of damages to ships and infrastructure in harbours during storm surge events in the western Baltic Sea have highlighted the urgent need for robust adaptation strategies to sea level rise. This study utilizes regional climate simulations, employing five members of the EURO-CORDEX ensembles, to investigate sea level rise scenarios under the RCP4.5 and RCP8.5 scenarios. Using the concept of "gate index", the study quantitatively assesses the frequency and duration of potential closures of storm surge gates in harbours in response to extreme sea level rise events. The results show significant spatial differences in vulnerabilities across the region, with increased risks under the RCP8.5 scenario. The analysis also emphasises substantial uncertainties, stemming from various factors such as variations in global climate models, complexities in ocean-atmosphere interactions, potential changes in ice sheet dynamics, and uncertainties in future greenhouse gas emissions trajectories. In addition, regional factors such as local sedimentation processes, tectonic activities, and land-use changes can further amplify these uncertainties. The interplay of these multifaceted factors underscores the complex nature of projecting sea-level rise, highlighting the need for a cautious and adaptive approach in coastal planning and policy formulation. These findings provide critical insights for policymakers and practitioners engaged in coastal risk assessment and adaptation planning in the vulnerable Baltic Sea region.

How to cite: Su, J., Ehlers, B.-M., Nielsen, J. W., Madsen, K. S., Larsen, M. A. D., and Janssen, F.: Quantifying adaptation strategies: The gate index approach to extreme sea level scenarios in the western Baltic Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1858, https://doi.org/10.5194/egusphere-egu24-1858, 2024.

Leonardo Aragão, Lorenzo Mentaschi, Giorgia Verri, Alfonso Senatore, and Nadia Pinardi

Located in the central Mediterranean Sea, the Adriatic Sea experiences complex water circulation patterns driven by saltwater inflow from the Ionian Sea through the Strait of Otranto and the outflow through the same strait but richly charged by fresh and dense waters formed in the northern Adriatic Sea. These circulation patterns place the unique hydrodynamics of the Adriatic Sea as the mainframe in shaping its diverse marine ecosystem, making it a primary region in dense water formation within the Mediterranean Sea. However, in recent decades, the region has continually recorded longer and more intense periods of drought. Some studies account for the loss of about 80 billion tons of freshwater during the 2021-2022 drought only at the Po River basin. To explore this matter further, the present work aims to analyse the last decade (2013-2022) of river discharges into the Adriatic Sea and frame the impacts of recent drought events in the current climatological period. To this end, the hydrological data reconstructed with the European Flood Awareness System (EFAS) were analysed for the period 1991-2022, quantifying river discharges separately in the four subregions of the Adriatic Sea: Shallow Northern Adriatic Sea (SNAd), Northern Adriatic Sea (NAd), Central Adriatic Sea (CAd), and Southern Adriatic Sea (SAd). Over the past 32 years, river discharges have shown different trends along the Adriatic Sea subregions, where a delicate balance between dry seasons in some subregions has been slightly balanced by flood seasons in others and vice versa. This delicate balance, combined with the diversity of its river basins, prevents us from estimating a trend with statistical significance for the Adriatic Sea. However, the river discharge trends are forthright when computed individually for each subregion, balancing slightly negative trends in the northern subregions (-0.6% and -1.0% per year in SNAd and NAd) with intriguingly positive trends in the southern subregions (+0.4% and +1.3% per year in CAd and SAd). When the analysis window narrows to the last decade (2013-2022), this balance breaks down, and a strong negative trend emerges across the entire Adriatic Sea, without exception, indicating reductions of -4.2% per year in freshwater input throughout the river basin. As suggested by the Standardised Flow Index (SFI) results, a climate indicator used to estimate the long-term impact of drought and flood periods on river discharges, 2022 was crucial for the last negative decadal trend. During this year, the northern Adriatic experienced the driest period in the last 32 years, while the southern Adriatic experienced river discharge reductions during flood months. Nevertheless, the most worrying element about the extreme drought of 2022 is that this year is part of a drought cycle that has continuously reduced freshwater availability in the Adriatic Sea every 4-5 years since 2008.

How to cite: Aragão, L., Mentaschi, L., Verri, G., Senatore, A., and Pinardi, N.: Critical decade for freshwater discharge into the Adriatic Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10202, https://doi.org/10.5194/egusphere-egu24-10202, 2024.

Andrei Gabriel Dragos, Monica Palaseanu-Lovejoy, Gabriel Iordache, Irina Stanciu, Florin Pitea, Maria Ionescu, Adrian Gherghe, and Adrian Stanica

The security and wellbeing of a community is partially dependent on a critical understanding of the natural environment, landscape evolution, available resources, vital information communication in response to an event or increasing rates of change. The ability to map flooding, erosion, and habitat loss is a key tool in a country's resilience and strategies for mitigation and adaptation against costly natural hazards, and this cannot be done without high-resolution, accurate 3D data.

Structure-from-Motion (SfM) photogrammetry is a powerful technique for creating high-resolution 3D digital terrain models (DTMs) from overlapping 2D images at a relatively low cost.

We conducted several SfM-photogrammetric surveys in the Romanian Black Sea coastal zone, on the wild protected beaches in front of the Danube Delta - Edighiol barrier beach (July and November 2023) on the southern extent of the Danube Delta, and the Sf. Gheorghe beach immediately North of the southernmost arm of the Danube in the Black Sea (August 2022 and August 2023). Sf. Gheorghe is the only asymmetric active lobe in the Romanian part of the Danube Delta associated with a river-sea confluence barrier island (South of the mouth), which is a very dynamic spit with a large cyclic development. We generated DEMs and orthomosaics at 4 – 5 cm pixel resolution with a vertical mean square error between 5 to 8 cm and a mean error bias of 2 cm or less.

In the case of Sf. Gheorghe beach, between 2012 LiDAR survey and 2022 SfM survey, shoreline erosion up to 100 m was observed immediately adjacent of the northern side of the Sf. Gheorghe branch, at the confluence with the Black Sea. The erosional trend increases closer to the confluence on both Sf. Gheorghe bank and Black Sea shore sides. About 2 km North of the Sf. Gheorghe mouth the 2012 and 2022 shorelines coincide, while on the Sf. Gheorghe branch shore, the two left bank positions coincide after 400 m only. The uneven erosion near the confluence point suggests the impact of Black Sea longshore currents due to insufficient sediment from the Danube. Edighiol SfM surveys analyzed coastal dynamics, emphasizing winter storms, inundation, vegetation changes, sand dune shifts, and beach erosion.

The Danube Delta Black Sea coast erosion is primarily caused by human activities, including reduced sediment supply, altered sediment pathways (resulting from damming, embankments, and canal cutting), and accelerated climate change. Natural factors like subsidence, sea-level rise, and occasional extreme storms also contribute. SfM surveys provide quantitative analysis for assessing short- and long-term changes influenced by episodic and seasonal events in this dynamic environment.



This work was financed by The Core Program PN 23 30 03 01 and the H2020 DOORS, EC Grant 101000518 -  Developing Optimal and Open Research Support for the Black Sea (DOORS) project.

How to cite: Dragos, A. G., Palaseanu-Lovejoy, M., Iordache, G., Stanciu, I., Pitea, F., Ionescu, M., Gherghe, A., and Stanica, A.: 3D high-resolution Romanian Black Sea – Danube Delta coastal geomorphic surveys for change analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20826, https://doi.org/10.5194/egusphere-egu24-20826, 2024.

Kwang-Young Jeong, Gwangho Seo, Seok Jae Kwon, Hyun-Sik Ham, and Hyun-Ju Oh

Constructed in June 2003, "The Ieodo Ocean Research Station (Ieodo-ORS)," a steel-framed tower-type platform, is strategically positioned near a submarine rock named Ieodo. Its primary objective is to advance our understanding of oceanic and atmospheric phenomena and their intricate interactions in the East China Sea. This station attains global significance owing to its distinctive open-sea location, situated approximately 149 km away from Jeju Island. Initially established by KIOST as part of the Research and Development project of the Ministry of Oceans and Fisheries, control of the Ieodo-ORS was transferred to the KHOA in 2007. Outfitted with 29 instruments for collecting oceanographic, meteorological, and environmental data, the Ieodo-ORS functions as a central hub for diverse projects initiated by the KHOA. These projects are geared towards refining observation techniques, optimizing the utilization of observational data, and systematically monitoring oceanic and atmospheric environments. Since 2014, the KHOA has executed the 'Ieodo-ORS Field Research Trip' program, providing support for a dedicated ship to service the Ieodo-ORS and leverage its facilities. The ultimate objective of the KHOA is to establish the Ieodo-ORS as a globally recognized scientific station through a comprehensive array of academic research initiatives.

How to cite: Jeong, K.-Y., Seo, G., Kwon, S. J., Ham, H.-S., and Oh, H.-J.: The Ieodo Ocean Research Station (Ieodo-ORS) and Research Endeavors by the Korea Hydrographic and Oceanographic Agency (KHOA), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4995, https://doi.org/10.5194/egusphere-egu24-4995, 2024.

Lucía Quirós-Collazos, Justino Martínez, Lluc Segura-Lladó, Gerard Llorach-Tó, Joaquim Ballabrera-Poy, Concepción Bueno, Emilio García-Ladona, and Jordi Isern-Fontanet

Over the last year 2023, a new high-frequency (HF) radar network has been implemented along the Catalan Coast by the ICATMAR, a cooperative body between the Institute of Marine Sciences (ICM-CSIC) and the Catalan Government that aims to provide scientific advice for the maritime governance in this region. The network consists of 7 CODAR antennas, 5 of which are already operating and the rest will be commissioned before the end of 2024. These antennas provide surface radial velocities and waves measurements along the Catalan Coast, between the coastline and about 40 miles offshore. The radial velocity measurements obtained by two or more antennas are currently being combined using the (unweighted) least-squares fitting method to derive the total current velocity fields. Current data provided by the ICATMAR HF radar network has a spatial resolution of about 9 km2 and is delivered every hour.

The results presented here focus on the characterisation Probability Density Functions (PDFs), statistical moments and structure functions of radial velocities. Quality control standards of JERICO network (defined in JERICO-Next D5.13) have been applied on radial velocity data measured by two antennas (stations CREU and BEGU) over an almost 1-year time series since their installation in 2023. The analysis of the main four moments were performed on validated data in order to characterize the main statistical properties. The derived PDFs differ from a Gaussian distribution by showing heavy tails, characteristic of turbulent flows and ocean observations. Structure functions up to the 15th order were calculated along each radial direction and their scaling were derived, unveiling a spatial variation of the anomalous scaling of the structure functions.

These preliminary results highlight the HF radars value as a tool for sampling surface sub-mesoscale turbulence structures which, in turn, will help improve our understanding of the dynamical properties of ocean flows, specially, in near coastal marine regions where high resolution currents data is scarce.

How to cite: Quirós-Collazos, L., Martínez, J., Segura-Lladó, L., Llorach-Tó, G., Ballabrera-Poy, J., Bueno, C., García-Ladona, E., and Isern-Fontanet, J.: The new ICATMAR high-frequency radar network: data analysis and preliminary results on the Catalan Coast turbulence characterization., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13192, https://doi.org/10.5194/egusphere-egu24-13192, 2024.

Huan Meng Chang, Jian Wu Lai, Hsin Yu Yu, Hwa Chien, and Jenq Chi Mau

    The pivotal role of oceanographic parameters in informed decision-making spans a spectrum of marine issues, including resource management, ecological preservation, maritime safety, and national security. Conventional data acquisition methods, such as buoys and survey ships, have become increasingly inadequate in addressing the complex demands of these domains. This insufficiency has propelled the global development of spatially-oriented observational technologies, among which High-Frequency (HF) ocean radar is a standout. Globally, the deployment of HF ocean radar systems has surpassed 1000 units. Taiwan, since the 1990s, has installed 48 HF radar systems, including variants like CODAR, WEAR, and LERA. Despite initial successes, a decline in system performance post-deployment is a recurring issue, often linked to policy discontinuity, fluctuating financial support, team dynamics, technical proficiency, and data application and dissemination challenges. This study explores Taiwan's unique experiences with these impediments in the evolution of HF ocean radar systems, aiming to strategize effective long-term operational planning.

    A case in point is the Taiwan Ocean Radar Observation System (TOROS), established by the Taiwan Ocean Research Institute (TORI) in 2016. This system confronted operational challenges due to insufficient maintenance budgets soon after its inauguration, leading to the cessation of some station operations. Further analysis identified a reactive data dissemination model, requiring user applications and approvals, as a primary issue. This inefficiency, compounded by inadequate promotion, weakened the system's perceived utility, resulting in unsustainable policy support, budget cuts, and the loss of specialized personnel, thereby adversely impacting system functionality in a cyclic manner.

    This paper argues that the ubiquity of data fosters demand, a critical metric for evaluating system utility. Such utility influences administrative decisions, which in turn affect financial commitment, vital for cultivating a skilled technical team. The strategic deployment of this workforce is crucial for consistent system operation and maintenance, ultimately determining operational success. Leveraging insights from past installations and operational experiences, the study proposes methodologies to sustain operational continuity and bolster the efficacy and resilience of HF ocean radar systems.

How to cite: Chang, H. M., Lai, J. W., Yu, H. Y., Chien, H., and Mau, J. C.: Challenges and Strategies in the Development and Operation of High-Frequency Marine Radar Systems in Taiwan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16161, https://doi.org/10.5194/egusphere-egu24-16161, 2024.

Posters virtual: Tue, 16 Apr, 14:00–15:45 | vHall X5

Display time: Tue, 16 Apr 08:30–Tue, 16 Apr 18:00
Chairpersons: Joseph Ansong, Abe Woo, Anna Katavouta
Hung Nguyen, Samantha Siedlecki, Enrique Curchitser, Charles Stock, Cesar Rocha, Zhuomin Chen, and Felipe Soares

Oxygen concentrations are of fundamental importance for organisms as well as geochemical cycling in oceans. Since the middle of the 20th century, oxygen concentrations have been declining in the open ocean and the coastal ocean. Located near the intersection of subtropical and subpolar circulation, the northwest Atlantic (NWA) Shelf is sensitive to climate variability. Recent work has been done on regional NWA trends in sea-surface temperature, salinity, and chlorophyll, but the trends and drivers of oxygen in the region have not yet been established. Here, we use World Ocean Database oxygen observations to determine the temporal trend of subsurface oxygen concentrations between 50-100m on the NWA Shelf from 1988 to 2019. We also use a regional NWA ROMS and MOM6 configuration to simulate the historical decadal trends and spatial patterns in dissolved oxygen concentrations over the shelf. Our results indicate a significant decrease of oxygen by 1.542±0.308 µmol/kg/year, which surpasses the established Atlantic basin-wide trend. The greatest subregional oxygen loss occurs on the Scotian Shelf and in the Gulf of St. Lawrence. A detailed analysis revealed that the oxygen trends on the NWA shelf are driven by changes in Apparent Oxygen Utilization (AOU), consistent with the decreased influence of Labrador Current in the region and associated water mass properties. Our model identifies the location of minimum oxygen concentrations occurring both at the bottom but also at midwater column depths in the Mid-Atlantic Bight and Gulf of Maine. Under SSP5-8.5, our dynamically downscaled projection (2014-2098) projects that the bottom oxygen in the NWA Shelf will accelerate relative to the historical period (1980-2014). Diagnosis of the mechanisms behind the future acceleration as well as the mid-water column minimum oxygen pattern using various tools will be presented.

How to cite: Nguyen, H., Siedlecki, S., Curchitser, E., Stock, C., Rocha, C., Chen, Z., and Soares, F.: Temporal trends and causes of deoxygenation: a comparison of the Northwest Atlantic Shelf and Atlantic Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7012, https://doi.org/10.5194/egusphere-egu24-7012, 2024.

Mohammad Al-Suwaiyan and Zaher Mundher  Yaseen

Coastal areas are more vulnerable due to the high extreme natural events and anthropogenic activates are destroying the environment. Food demand is gradually increasing because of the population pressure, while alternative planning like aquaculture land is increasing in some parts of the Saudi Arabia. Global sea level rise is also a triggering factor for shoreline change in the coastal environment with high soil salinity increase. Without any sustainable planning and management, those areas are gradually affected through shoreline shifting and soil salinity-related problems. Remote sensing is the most powerful tool to detect those earth’s surface changes through earth observational datasets. Landsat series datasets were applied for detecting shoreline shifting, aquaculture land identification, and soil salinity along with automatic water area detection using Google Earth Engine cloud computing platform from 1994 to 2023 near Alqalh area, Saudi Arabia. Decadal shoreline shifting observed like 4.48 km2 (1994-2002), 8.82 km2 (2002-2014), 6.61 km2 (2014-2023) and 9.24 km2 (1994-2023), while overall 43.47 km2 (1994-2023) of the area is accretion measured. In the initial periods (1994) aquaculture land did not exit in this area but in the recent time (2023) this area have 71.16 km2 (13.38%) of aquaculture land. Some geo-spatial indices also applied for soil salinity, vegetation and water body area where Vegetation Soil Salinity Index (VSSI) observed high salinity in the year of 2024 due to huge aquaculture land and shoreline shifting towards north-west, south and south-east position of the study area. This investigation outcomes may help local planners in developing novel adaptation strategies in order to protect the environmental degradation.  

Keywords: Coastal area water assessment; Shoreline shifting; Soil salinity; Aquaculture; Remote sensing.                                          

How to cite: Al-Suwaiyan, M. and Yaseen, Z. M.: Coastal area shoreline shifting detection and water salinity assessment based on remote sensing and google earth engine platform: Active aquacultural case study area in Saudi Arabia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2656, https://doi.org/10.5194/egusphere-egu24-2656, 2024.