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.

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.

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.

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.

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.

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.

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.

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.

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.

INTRODUCTION

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.

METHODOLOGY

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.

RESULTS

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.

CONCLUSIONS

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.

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.

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.

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.

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.

References

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.

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.

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.

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.

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.

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.