Geographically, two-thirds of Bangladesh lies in a vast deltaic plain, formed at the mouth of the Ganges, Brahmaputra and Meghna rivers (GBMD). This delta is the largest and most populated in the world, covering approximately 150,000 km² and home to more than 150 million inhabitants. Floods are frequent, and each year, during the summer monsoon, between 20% and 60% of the country is submerged. In the face of current and future climate disruption, IPCC projections predict in the GBMD: an intensification of floods and extreme events, as well as a worrying rise in mean sea level relative to land elevation. Today, more than 10% of the GBMD is less than one meter above mean sea level and land therein is subsiding, as in the largest deltas of the tropical areas. The future of the GBMD and its ability to remain above water depend on a delicate balance between sea level rise and land subsidence. Quantifying these parameters is essential to guide local policies and adapt strategies to climate change. However, the data available to analyze these phenomena are still very limited in Bangladesh and virtually no data is transmitted to decision-making institutions in real time. This new project, called “Partnership for Ocean Level Monitoring” (POLM), has the main objective of strengthening the technical capacities for observing variations in sea level rise and land level. It is based on the use of "Global Navigation Satellite System" (GNSS) stations, i.e. all satellite positioning systems, to determine in a coupled manner the water level (by Interferometry Reflectometry, IR, method) and the land level. Our project aims to technically support the national institutes dedicated to the study of these parameters and involved in the management of climatic hazards, through: 1. deploying innovative and affordable instrumentation tools; 2. establishing a real-time data transfer network; and 3. contributing to the training of the actors of these institutes, in particular the new generation of female scientists. POLM will enable several proofs of concept adapted to the economic and technical realities of Bangladesh, in particular: a low-cost assembly of stations, which reduces costs by ~50%; and the use of 4G networks, well adapted to the country's satellite coverage, to ensure real-time data transmission. Preliminary results from the analysis of the collected water level measurements, their processing and quality assessment, will be presented, as well as their experimental utilization in numerical models for the representation of the sea level dynamics. This project is part of an international scientific effort to collect coastal oceanographic data, necessary for monitoring global sea level and for predicting ocean rise by regional models. Moreover, the study area where implemented- the GBMD and the coastal belt of Bangladesh- represents one of the pilot site (ID PS-014-01) of the Northern Indian Ocean submitted to the “Coast Predict GlobalCoast” program.
How to cite: Capuano, T. A., Grall, J. C., Chowdhury, A., Ashfaq, M., Hossain, S., Albin, E., Karmakar, A., and Tahsin, T.: Partnership for Ocean Level Monitoring: Reinforcing technical capacity of water level data collection, processing and analysis for climate hazard management in the Bangladeshi delta. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-356, https://doi.org/10.5194/egusphere-egu26-356, 2026.
CoastPredict, a UN Ocean Decade Programme, is co-designing and implementing an integrated coastal ocean observing and predicting system that adheres to best practices and international standards, conceived as a global framework and implemented locally through sustained partnerships.
Many coastal services remain fragmented: observing assets, models, and downstream applications are often developed in isolation, and operational solutions do not consistently connect real-time data streams, multi-scale predictions, and decision workflows. This limits the capacity to (i) evaluate compound impacts from extreme events to long-term climate trends, (ii) compare performance across regions, and (iii) translate prediction skill into actionable management solutions. GlobalCoast is CoastPredict’s framework for implementation to address these gaps by linking observations, modelling, and stakeholder needs into fit-for-purpose, locally-led coastal resilience services that can be compared, transferred, and improved across diverse environments.
A major step forward has been the consolidation of the GlobalCoast Network of Pilot Sites. The first GlobalCoast survey (2023) identified 138 Pilot Sites in over 74 countries, establishing a global foundation for implementation and benchmarking; the Pilot Site submission process has since been reopened to expand geographic coverage and fill thematic gaps. A new GlobalCoast Network Memorandum of Understanting has been set up and signed by more than 50 parnters.
Over the last year, CoastPredict/GlobalCoast has strengthened the enabling backbone for scalable, interoperable services. This includes ProtoCoast, the prototype GlobalCoast cloud infrastructure co-designed by CMCC, SOCIB and EGI (with EOSC/Pangeo-aligned approaches and federated providers), supporting shared code, interactive analysis, and reproducible workflows across Pilot Sites. In parallel, the CoastPredict Secretariat, funded by CMCC, has enhanced coordination across projects and technical support for programme development and integration.
GlobalCoast is now advancing from operational oceanography toward operational management solutions through a “menu of solutions” approach: multi-hazard early warning services; coastal climate and risk indicators; pollution and marine litter applications; and decision-support tools for planning and adaptation. By deploying comparable building blocks across sites—while accounting for local dynamics, exposure, governance, and capacity—GlobalCoast enables systematic evaluation of what is transferable, what must be tailored, and what standards and best practices accelerate impact.
How to cite: Pinardi, N., coppini, G., Kourafalou, V., Tintore, J., Helsop, E., and O'Donovan, M.: The GlobalCoast Initiative of CoastPredict: from operational oceanography to management solutions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23125, https://doi.org/10.5194/egusphere-egu26-23125, 2026.
In recent years, several studies have investigated the effectiveness of Nature-based Solutions in mitigating extreme coastal hazards such as waves, storm surges, and erosion, highlighting their significant role in coastal protection. Observational measurements have provided the basis for understanding ocean–vegetation interactions, enabling parameterizations that have been incorporated into numerical models. These models are widely used to simulate real conditions and explore what-if scenarios involving plant phenotypic traits, species composition, and the spatial distribution and organization of vegetated meadows.
However, such simulations are computationally demanding, limiting their applicability in operational and exploratory contexts. To overcome this limitation, we exploit machine learning and artificial intelligence to develop numerical emulators within a digital twin framework. Several models were tested, including Random Forest, LightGBM, SVM, and deep learning architectures. Among them, a U-Net-like architecture demonstrated the best performance, and the results are here shown.
The training dataset consists of one year of numerical simulations for 28 vegetation configurations, generated by varying shoot density, leaf length, and leaf width. Simulations were produced using the SHYFEM-MPI circulation model coupled with the WAVEWATCH III wave model, incorporating the vegetation formulation of Shirinov et al. (2025). The AI emulator estimates vegetation-induced impacts on multiple ocean variables, including significant wave height, mean wave period and direction, near-bottom orbital velocity, and currents.
Results show that the AI emulator accurately captures nonlinear wave–vegetation interactions, reproducing wave attenuation and current modulation at high spatial resolution across two regional pilot areas. The model generalizes well, providing reliable estimates for intermediate vegetation configurations not included in the training dataset. Low error levels across variables and temporal consistency of the results demonstrate the robustness and stability of this approach.
This work highlights the potential of integrating artificial intelligence into predictive coastal modeling as a science-based risk assessment tool for evaluating the effectiveness of Nature-based Solutions, significantly enhancing coastal protection strategies.
How to cite: Lezzi, S. M., Causio, S., Maglietta, R., Giunti, L., Shirinov, S., Jafaar, N., Alessandri, J., Federico, I., and Coppini, G.: AI-based Emulation for Assessing the Impact of Nature-based Solutions on Waves and Currents for Coastal Protection and Resilience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17449, https://doi.org/10.5194/egusphere-egu26-17449, 2026.
The assessment of coastal vulnerability to hazards associated with extreme sea levels is strongly influenced by the combined effects of storm surges and waves. In addition, irregularities in available observations limit the reliable estimation of extreme return levels. To address this, we present a globally consistent dataset of wave heights and storm surges derived from a hindcast spanning 1950–2023, based on the high-resolution, unstructured and coupled global coastal model of Mentaschi et al. (2023), along with those from tropical cyclone events.
We apply a quantile mapping framework to debias the model with a focus on upper-tail values. We use along-track global L3 wave heights from satellite measurements, by the Copernicus Marine Service, to bias-correct the model-based significant wave heights. The corrected wave heights are independently validated using in-situ wave observations from ISPRA and Copernicus Marine Service buoy networks. For storm surges, in-situ coastal sea level observations from the GESLA3 database are employed. Historical tropical cyclone tracks and associated coastal water levels are simulated using the Deltares D-Flow Flexible Mesh (D-Flow FM) numerical model, whereas the wave heights during cyclonic events are obtained from satellite altimetry observations. The performance of the dataset is evaluated using state-of-the-art metrics tailored for the accuracy of extreme values. The results demonstrate substantial improvements in the representation of extremes. For example, extreme wave heights in the Italian Mediterranean region exhibit average (median) normalized biases below −30% in the original hindcast, which are reduced to within 0 and −10% after bias correction. Similarly, for storm surges, biases in the upper tail (above the 99.9th percentile) are reduced from −11.28% (−6.5%) to 0.38% (−0.55%) across selected global locations. In equatorial regions, where ERA5 wind forcing exhibits known deficiencies, extreme surge underestimation exceeding −40% is reduced to within −10%.
The dataset provides a robust foundation for determining the intensity of global coastal multi-hazards because its improved suitability for performing extreme value analysis and can be used to study the joint extremes arising from storm surges and waves.
References
Mentaschi, L., Vousdoukas, M. I., García-Sánchez, G., Fernández-Montblanc, T., Roland, A., Voukouvalas, E., Federico, I., Abdolali, A., Zhang, Y. J., and Feyen, L.: A global unstructured, coupled, high-resolution hindcast of waves and storm surge, Front. Mar. Sci., 10, 1233679, 2023 https://doi.org/10.3389/fmars.2023.1233679
Campos, R.M.; Gramcianinov, C.B.; de Camargo, R.; da Silva Dias, P.L. Assessment and Calibration of ERA5 SevereWinds in the Atlantic Ocean Using Satellite Data. Remote Sens. 2022, 14, 4918. https://doi.org/10.3390/rs14194918
Campos-Caba, R., Alessandri, J., Camus, P., Mazzino, A., Ferrari, F., Federico, I., Vousdoukas, M., Tondello, M., and Mentaschi, L.: Assessing storm surge model performance: what error indicators can measure the model's skill?, Ocean Sci., 20, 1513–1526, 2024,https://doi.org/10.5194/os-20-1513-2024.
Tamizi, A., Young, I.R. A dataset of global tropical cyclone wind and surface wave measurements from buoy and satellite platforms. Sci Data 11, 106 (2024). https://doi.org/10.1038/s41597-024-02955-4
Bahmanpour, M. H., Tilloy, A., Vousdoukas, M., Federico, I., Coppini, G., Feyen, L., and Mentaschi, L.: Transformed-Stationary EVA 2.0: A Generalized Framework for Non-Stationary Joint Extremes Analysis, EGUsphere [preprint], 2025.
How to cite: Wadalkar, A. S., Voukouvalas, E., Vousdoukas, M. I., Federico, I., Tondello, M., and Mentaschi, L.: A global dataset of storm surges and waves for coastal hazard mapping from bias corrected unstructured coupled hindcast with cyclone events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21284, https://doi.org/10.5194/egusphere-egu26-21284, 2026.
The Black Sea and the Sea of Azov are high-risk regions for major oil spill incidents due to heavy maritime tanker traffic and potential pipeline leaks. As a result, the Black Sea is currently considered the most oil-polluted marginal sea. In this work, we present a series of forecasting simulations to predict the dispersion of oil following an accidental 4,000-ton mazut release from the tanker Volgoneft 212 in the Kerch Strait from December 15 to December 25, 2024. The Kerch Strait is located in the southern part of the Sea of Azov, connecting this small, shallow, brackish body to the northeastern part of the Black Sea. The Strait is 40 km long and 4-5 km wide, and very shallow (3–5 m) at its center, deepening steadily to depths of 10 and 20 m at its northern and southern parts. Circulation through the Kerch Strait is not steady or unidirectional; it exhibits large synoptic variability in intensity and direction, governed by episodic wind-forcing events.
The Lagrangian particle-tracking numerical models MEDSLIK, OpenDrift, and MEDSLIK II were used in hindcast mode. The CMEMS hydrodynamic fields, the CYCOFOS wave fields, the NOAA-GFS, the ECMWF, and the SKIRON meteorological forecasts were used to force the oil spill models. Sentinel-1 SAR images were used to assess the impact of the oil spill and to evaluate model results. Satellite imagery and modelling results indicate that the spillage significantly affected more than 60 km of the NE Black Sea coastline, from Veselovka to Anapa. Due to the extreme weather conditions, with wave heights between 3 and 5 m, the available SAR imagery was limited. Combined SAR data and local media reports were first compared with the results of the oil spill models. Backtracking modelling and stochastic analysis were implemented to assess the exact location of the oil leak. The oil spill predictions from all models show good agreement with the reported on-site observations regarding the impacted coastal areas, the large extent of the impacted area, and the chronology of oil deposition along the coast.
Upper panels: SAR images of oil spillage in the Kerch Strait on 18/12/2024 (left) and 19/12/2024 (right); Lower panels: Superimposed MEDSLIK oil spill predictions on 18/12/2024 (left) and 19/12/2024 (right).
How to cite: Sylaios, G., Zodiatis, G., Keramea, P., Radhakrishnan, H., Liubartseva, S., Atake, I. R., Nicolaidis, A., Soloviev, D., Prokopi, K., Kokkos, N., Petalas, S., Hadjistassou, C., and Kampanis, N.: Oil spill predictions in the Kerch Strait using SAR imagery, and MEDSLIK, OpenDrift, and MEDSLIK II forecasts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21763, https://doi.org/10.5194/egusphere-egu26-21763, 2026.
This study presents an advanced, integrated numerical framework designed to resolve the non-linear interactions between hydrodynamics, aquatic vegetation, and coastal morphodynamics. Unlike traditional decoupled approaches, this framework captures the complex feedback governing momentum transfer, kinetic energy dissipation, and vegetation-mediated sediment trapping, processes vital for understanding coastal resilience under the pressures of Mediterranean sea-level rise and intensifying storm surges.
The modeling suite is built upon the SHYFEM-MPI finite-element circulation model, into which a novel morphodynamic module has been integrated, and coupled with spectral wave modle WAVEWATCHIII. This module incorporates state-of-the-art formulations for bedform transport and secondary current effects in stratified and channelized flows. To ensure physical consistency, the circulation engine is coupled with a spectral wave model via a shared unstructured computational grid, thereby eliminating interpolation-induced numerical diffusion and ensuring a synchronous exchange of wave-induced radiation stress and current-driven forcing fields. To overcome the chronic scarcity of in-situ sedimentological data, the study employs multi-sensor satellite-derived products to prescribe boundary conditions and provide independent spatio-temporal benchmarks for model validation. Furthermore, a variational data assimilation approach is utilized for bathymetric reconstruction, merging high-resolution local surveys with global datasets to generate a seamless, multiscale digital elevation model.
The framework application is demonstrated through idealized benchmarks and regional applications in the northeastern Tyrrhenian and Adriatic Seas. The results quantify the mechanistic partitioning between bedload and suspended sediment transport and demonstrate how evolving seabed morphology actively modulates local circulation and sea-surface elevations. This fully coupled approach provides a sophisticated tool for assessing the long-term morphodynamic trajectory of Mediterranean coastal systems.
How to cite: Shirinov, S., Federico, I., Pinardi, N., Bonamano, S., Causio, S., and Mentaschi, L.: Integrated modelling of hydrodynamics, vegetation and coastal morphodynamics in the Adriatic and Tyrrhenian Seas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20777, https://doi.org/10.5194/egusphere-egu26-20777, 2026.
A daily automated coastal water level (storm surge) nowcast/forecast guidance system has been developed by the USF Ocean Circulation Lab based on the West Florida Coastal Ocean Model (WFCOM) and the very high-resolution Tampa Bay Coastal Ocean Model (TBCOM). Both models are configured to perform realistic simulations of ocean circulation and water levels which are then combined with tide gauge observations to provide 3-day hindcasts and 3.5-day forecasts of coastal water level along the West Florida coast (http://ocgweb.marine.usf.edu/Models/SeaLevel/). The experimental product was maintained during the approach and passage of Hurricanes Helene and Milton, and provided critical storm surge forecasts to a broad suite of stakeholders including the public. The system successfully predicted the water level set-up and set-down along the west Florida coast three days in advance of each hurricane, with improved forecasts realized each day prior to landfall. The TBCOM-inundation forecast system was also activated during Hurricane Helene. This modeling system extends its dense grid onto the land, facilitating simulation of inundation and flooding associated with storm surge in coastal areas. During Hurricane Helene, areas of severe inundation were identified along the coastal periphery of Tampa Bay and forecasts were accessible two days in advance of landfall.
How to cite: Liu, Y., Xu, H., Qiao, K., John, S., San, S.-C., Weisberg, R. H., Chen, J., Zheng, L., Gilbert, S., Murawski, S. A., Mitchum, G. T., and Frazer, T. K.: Storm Surge and Coastal Inundation Nowcasts/Forecasts During Hurricanes Helene and Milton, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2087, https://doi.org/10.5194/egusphere-egu26-2087, 2026.
Here we present the development of SIM MASE (https://sim.mase.gov.it/portalediaccesso/), an integrated operational platform designed for the Italian Ministry of Environment. Our focus was to develop an integrated set of applications to monitor and mitigate marine pollution in the so-called Vertical 3. The system bridges the gap between complex numerical modeling and stakeholder usability.
Surface hydrocarbon slicks (either oil spill or produced water slicks) are identified by processing satellite images using a neural network, specifically trained for the recognition and semantic segmentation of these events. This object-based approach allows for accurate spatial characterization providing not only their location but also detailed geometric information (area, perimeter, shape), proving robust even in the presence of noise, variable weather and sea conditions, and confounding phenomena.
The hydrocarbon slicks masks generated by the detection module constitute the initial input or validation for a suite of numerical models that simulate the spatiotemporal evolution of the slicks over time.
The modeling takes into account the main environmental forces, such as marine currents, wind, etc., allowing for the prediction of the trajectory, dispersion, and potential impact area. Similarly, the system integrates models dedicated to the dispersion of produced water, allowing for a joint assessment of different scenarios.
For oil spill modeling we have integrated TAMOC and MEDSLIK-II, allowing users to perform subsurface oil spill modeling and follow its drift in the surface. While produced water modeling we have coupled TAMOC and ChemicalDrift, following the same concept. In the platform there is also available a system to check Hazard, Vulnerability and Risk maps generated from millions of simulations run on HPC systems.
The platform's architecture is based on a containerized environment that ensures high portability, scalability, and reproducibility of the models, facilitating their use in an operational context.The user interface is designed to allow institutional stakeholders to independently launch simulations and consult the results, transforming raw satellite data into forecasts and risk maps to support surveillance and rapid response activities.
SIM MASE demonstrates a successful transition from academic modeling to an operational decision-support system, providing the Italian Ministry with a robust tool for the long-term protection of Mediterranean marine ecosystems.
How to cite: Atake, I., Coppini, G., Pecora, S., Dissanayake, A., Ramos, J., Bravo, S., Mannarini, G., Infante, M., D'Amico, M., Unali, E., Hoxhaj, M., Federico, I., Liubartseva, S., Habra, N., Chiffi, A., Kazemi, A., and Kumar, P.: SIM MASE: An Integrated Satellite Monitoring and Early Warning System for Oil Spill and Produced Water Management in Italian Seas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14280, https://doi.org/10.5194/egusphere-egu26-14280, 2026.
Reliable sea-level data is needed for accurate coastal risk assessments, but historical records often have biases, missing entries, and inaccuracies. This study presents a comprehensive framework for augmenting and rectifying storm surge records through the integration of machine learning, hydrodynamic modelling, and statistical analysis. Using a block-median approach and standard statistical methods, station-specific biases are found and fixed. To add more historical data, a combination of methods is used: a machine learning model like Random Forest is trained to fill in the gaps in storms' time series when only model data is available, and hydrodynamic simulations are used to find extreme events that aren't in the observational record. The framework is used at more than 50 tide gauge stations along the Danish coast to create a high-quality, validated dataset of reconstructed extremes and bias-corrected observations for the years 1961 to 2024. This dataset is very useful for climate adaptation and accurate coastal risk assessments because it focusses on critical windows about 24 hours before and after surge peaks.
How to cite: Su, J., Nielsen, J. W., Madsen, K. S., and Larsen, M. A. D.: Reconstructing historical storm surges levels with models and machine learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16604, https://doi.org/10.5194/egusphere-egu26-16604, 2026.
The world’s low-lying and densely populated coasts are at risk of climate-induced sea-level rise from climate change and accelerating coastal subsidence. Sustainable coastal adaption strategies need adequate coastal land and population exposure assessments for coastal hazards like storm surges, coastal (compound) flooding and future relative sea-level rise. The accuracy of coastal impact exposure assessments strongly depends on the proper alignment of both land elevation and sea level data, referenced to a common vertical datum. Shortcomings in this alignment result in incorrect assessment of contemporary coastal sea-level height, which consequently introduces errors into coastal exposure and risk assessments.
Here we unravel shortcomings and errors in the fundamental aspect of vertical datum alignment in most contemporary sea-level rise and coastal hazard impact assessments based on a systematic scientific literature evaluation. More than 99% of the evaluated assessments handled sea-level and land elevation data inadequately and/or contained shortcomings in the methodological documentation, leading to systematic underrepresentation of contemporary coastal sea level. Our meta-analyses on global and regional scales revealed that globally coastal sea-level height is on average 0.3 m higher than often (>90%) assumed, with a disproportionate impact on the Global South and differences of more than 1 m in most affected regions in the Indo-Pacific. We find that worldwide 37% more land and up to 68% more people will fall below sea level following a 1 m relative sea-level rise than frequently assumed, implying the necessity for a potentially much sooner implementation of coastal adaptation strategies. Many reviewed studies informed policy reports (e.g., IPCC AR6), which may have led to misjudged coastal exposure and risk. To improve future coastal hazard and impact assessments, we provide ready-to-use combined products of land elevation and coastal sea level. We also recommend adding dedicated author declarations and review checklists into the scientific peer-review process to catch errors before publication and uphold scientific standards. Applying these measures in policy reports (including IPCC assessments) will enable verification of methodological robustness of cited coastal-hazard studies, strengthening the reliability of scientific evidence (e.g. global climate risk rankings) that informs policy and underly UN-level discussions (e.g. funding priorities, or Loss and Damage negotiations).
How to cite: Minderhoud, P. S. J. and Seeger, K.: Coastal sea level higher than assumed in most hazard assessments: Implications for coastal resilience and policy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6126, https://doi.org/10.5194/egusphere-egu26-6126, 2026.
The Horizon Europe FOCCUS project (Forecasting and Observing the Open-to-Coastal Ocean for Copernicus Users, foccus-project.eu), endorsed by the UN Ocean Decade and aligned with the CoastPredict Program, strengthens the coastal dimension of the Copernicus Marine Environment Monitoring Service (CMEMS) and supports the implementation of the European Digital Twin of the Ocean (DTO) to improve Europe’s coastal resilience. Bringing together 19 partners from 11 European countries, in collaboration with Member State Coastal Systems (MSCS) and users, FOCCUS has already developed and published a suite of innovative digital ocean products and services following three key pillars: i) developing new high-resolution coastal observations, ii) developing advanced hydrology and enhancing coastal models, and iii) establishing co-designed coastal applications for addressing three environmental and societal challenges around Europe. In the realm of coastal observation, products include multiple essential ocean variables, built using data fusion, AI-based algorithms, and leveraging synergies between observing multi-platforms and satellite sensors and missions to improve their accuracy and coverage, as well as detailed inventories of pan-European coastal and river data. Combined with insights from an in-depth analysis of existing European coastal operational systems, these new data products are being integrated into MSCS. In order to further reinforce the connection from land to sea, MSCS are also enhanced by developments in pan-European hydrological data and high-resolution coastal modeling. Vital for integration in the DTO framework, FOCCUS has tested new methodologies to better connect the various MSCS with CMEMS, including the use of innovative nesting methods and new data fusion approaches that incorporate AI technologies. FOCCUS enables the integration of advanced data products into targeted, co-produced applications to address three areas of coastal protection: i) coastal management and protection (including the prediction of coastal erosion risk, marine pollution, and sediment tracking), ii) enhancement of the blue economy (including the co-use of wind and aquaculture resources), and iii) building resilience to coastal climate change (including tracking marine heatwaves, monitoring ecosystem degradation and harmful algae blooms, and predicting storm surge/waves). This project is also set to be integrated within EDITO Model-Lab. The advanced observation data products developed in FOCCUS are to be published in the EDITO Data Catalog, making the datasets and their metadata discoverable, while allowing EDITO users to directly work with these products efficiently, thanks to the collocation of data and computing. The MSCS are planning to also go through uniform validation by utilizing EDITO-Model Lab's Validation Toolbox, a service accessible on the EDITO Platform. FOCCUS showcases how interdisciplinary coordinated advances in observing systems, modeling, and co-design of applications can jointly improve the scientific and operational foundations of CMEMS and accelerate the development of the European DTO to help address natural hazards and extreme events.
FOCCUS is funded by the European Union (Grant Agreement No. 101133911). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them.
How to cite: Johnson, K., Staneva, J., Reyes, E., Bonaduce, A., Verri, G., Federico, I., Bartosova, A., Terskii, P., Christensen, K. H., Jamet, Q., Garcia Hermosa, I., Meszaros, L., Beyaard, L., and El Serafy, G.: FOCCUS: Advancing Europe’s Coastal Monitoring and Forecasting Capabilities to Increase Coastal Resilience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10313, https://doi.org/10.5194/egusphere-egu26-10313, 2026.
The role of spatial variability of sea surface temperature (SST) in Tropical Cyclone Heat Potential (TCHC) has been studied well and it has a direct impact on the distribution of turbulent heat fluxes. However, the thermal structure of the upper ocean is also critical for cyclone formation and the influence of diurnal SST variability to TCHC remains an active area of research, particularly in warm ocean basins such as the Bay of Bengal (BOB), which accounts for many devastating cyclones globally. This study intends to investigate the impact of diurnal SST variability on air-sea heat flux distribution and TCHP in the BOB, with the objective of improving the understanding of the pre-cyclone oceanic conditions. The methodology incorporates a multi-dataset approach to capture the fine-scale temporal and spatial thermal structure of the upper ocean. The Copernicus Global Ocean Physics Analysis and Forecast product is used to obtain sea temperature and sea surface height, which are employed to compute the depth of the 26° C isotherm—a key parameter for calculating TCHP. To address the computational challenges associated with high-resolution datasets, a machine learning approach, a Convolution Neural Network (CNN) is framed to estimate TCHP. Additionally, the inherent uncertainties are quantified using altimetry and SST observations from microwave imager data. The combination of multi-dataset approach is expected to provide a more accurate representaiton of diuarnal SST variablity and its influence on air-sea heat fluxes and TCHP.
How to cite: Ghani, M. H.: The Role of Diurnal Sea Surface Temperature Variability in Air-Sea Heat Fluxes and Tropical Cyclone Heat Potential in the Bay of Bengal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11395, https://doi.org/10.5194/egusphere-egu26-11395, 2026.
Global climate change has become a critical factor influencing marine and coastal safety. According to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), even with global mitigation efforts, the trend of global warming remains irreversible. The increasing intensity of future extreme climate events is expected to affect wave dynamics, storm surges, and coastal hazards. This study aims to assess long-term variations in extreme waves and storm surges around Taiwan under climate change scenarios and to establish design parameters that reflect future conditions for coastal hazard mitigation and engineering applications.
Atmospheric data from the EC-Earth3 global climate model under the high-emission scenario (SSP5-8.5) were used to drive wave and ocean models simulating wave fields and storm surge residuals over the Northwest Pacific. To address the coarse resolution of global climate models, an artificial intelligence–based statistical downscaling framework was developed. This approach integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) into a Convolutional Recurrent Neural Network (CRNN) architecture, improving spatial and temporal resolution and generating data representative of coastal-scale processes. The downscaled wave and surge results were analyzed using the Generalized Pareto Distribution (GPD) for extreme-value statistics to estimate design wave heights and storm surge residuals corresponding to return periods of 10, 25, 50, 75, 100, and 200 years. Statistical uncertainties were evaluated using a bootstrap resampling method.
Results show that under the high-emission scenario, significant wave heights and storm surge residuals exhibit an increasing trend with longer return periods. Wave analysis reveals marked changes in northern coastal waters, where design wave heights increase from 3.05 m to 5.65 m. The eastern coast shows moderate increases (4.08–4.22 m), while the Taiwan Strait and southern waters remain relatively stable at about 3.6 m and 5.2 m, indicating higher sensitivity of the north to extreme forcing. For storm surges, historical maximum residuals ranged from 0.36 m to 1.49 m, while future projections range from 0.31 m to 1.35 m, showing a slight decrease but similar spatial distribution, with larger deviations along western and island coasts. Design storm surge residuals increase with return period, from about 0.20 m for a 10-year event to 0.32 m for a 200-year event. Under future conditions, increases are projected mainly for western and island coasts, with southern and eastern shores also showing gradual rises.
Overall, extreme waves and storm surges around Taiwan exhibit long-term variations under climate change. Although short-term fluctuations remain moderate, both wave and surge intensities increase at longer return periods, implying that future coastal design standards should consider higher thresholds. The AI-based downscaling and extreme-value framework established in this study supports quantitative assessment of coastal hazards, engineering design, and adaptation planning in Taiwan.
How to cite: Fan, Y.-M.: Design wave heights and storm surge residuals around Taiwan under climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3228, https://doi.org/10.5194/egusphere-egu26-3228, 2026.
Storm surge and storm waves are significant marine dynamic disasters that affect coastal areas globally. Interactions among tides, surges, and waves are complex and nonlinear, particularly in shallow coastal regions and estuaries. This study investigated the historical Super Typhoon Saola (2023) through hindcasting and analyzed tide–surge–wave interactions (TSWIs). To achieve this objective, six numerical experiments were conducted using the advanced circulation model (ADCIRC) and the coupled ADCIRC + SWAN model. These experiments aimed to isolate the contributions of astronomical tides, storm surges, waves, and their nonlinear interactions to variations in water levels and significant wave heights (SWHs). Experimental results indicated that the nonlinear effects of TSWIs decreased from the outer edge to the head of Lingding Bay during Super Typhoon Saola. Furthermore, the contribution of wave setup to the total water elevation was found to be relatively minor. Current variations had a significantly greater influence on SWHs in Lingding Bay than water level variations. Moreover, tidal forces could substantially modulate SWHs through TSWI mechanisms. Notably, neglecting tidal effects resulted in a three-orders-of-magnitude reduction in the bottom stress terms, attributed to tidal current velocity and tidal level. This study underscored the critical importance of incorporating TSWIs into simulations related to typhoon-induced storm surges and storm waves. These factors are essential for mitigating typhoon-related disasters and designing criteria pertinent to societal infrastructure and coastal engineering.
How to cite: Huang, L., Zhang, T., Zhang, S., Zhang, X., Wang, H., Chi, C., and Yang, J.: Tide–Surge–Wave Interaction in the Pearl River Estuary during Super Typhoon Saola (2023), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6113, https://doi.org/10.5194/egusphere-egu26-6113, 2026.
Coastal cities located in estuaries often face significant risks from both riverine and tidal flooding due to their low-lying locations. Accurately predicting flood water levels in a complex urban environment is challenging because multiple factors interact – upstream river flow, heavy rainfall, tides and storm surges all play a role. This research explores a new approach to compound coastal-fluvial flood forecasting using deep learning: a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The goal is to forecast water levels in an estuary as well as flood water levels over an urban floodplain with a lead time of 1 to 33 hours. The model is specifically designed to effectively combine various hydrological, coastal and meteorological data sources.
Coastal city of Cork, the second-largest city in Ireland and one that is frequently affected by compound coastal-fluvial flooding is used as a case study. In this research, we use high-resolution precipitation forecasts from the NWP model operated by Met Eireann, river flow data from the River Lee catchment, and tidal/surge information from the MSN_Flood hydrodynamic model of the Cork Harbour.
Our proposed CNN-LSTM architecture combines the strengths of these deep learning methods. The CNN component efficiently identifies important spatial patterns from the rainfall forecasts and model outputs that suggest potential flooding. The LSTM component then captures how water levels change over time, enabling the model to learn the evolution of flood conditions.
Historical flood events in Cork City form the basis for training our deep learning model. This historical data, combined with real-time data streams from NWP, river gauge records and hydrodynamic model, allows the CNN-LSTM network to learn the intricate relationships between upstream riverine conditions and downstream sea water levels. This system has the potential to significantly improve flood preparedness and response in Cork City, enabling earlier warnings and proactive measures to protect communities from flood damage. Additional data, such as soil moisture and land cover information are also used to enhance the model’s accuracy and robustness.
How to cite: Olbert, A. I., Alizadeh, M. J., and Panchanathan, A.: Enhancing Flood Forecast: A Deep Learning Approach Combining NWP, Hydrology and Hydrodynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15017, https://doi.org/10.5194/egusphere-egu26-15017, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 1a
EGU26-17453 | ECS | Posters virtual | VPS20
A Non-Stationary Multivariate Framework for Assessing Compound Coastal Hazards at Global ScalesTue, 05 May, 15:09–15:12 (CEST) vPoster spot 1a
Coastal regions are increasingly exposed to compound hazards driven by the joint occurrence of extreme sea levels, waves, river discharge, and atmospheric forcing, with risks further amplified by long-term sea-level rise. Accurately quantifying these low-probability, high-impact events requires statistical frameworks capable of representing both multivariate dependence and non-stationary behavior across space and time. Here, we present an integrated approach for global to regional coastal hazard assessment that combines non-stationary extreme value analysis with multivariate dependency modeling. The framework builds on transformed-stationary representations of evolving marginal extremes and incorporates time-varying dependence structures to capture changes in cross-hazard relationships under shifting climate conditions. Event-based sampling strategies and statistical diagnostics are used to isolate relevant extremes and assess the significance of observed trends and uncertainties. Applied to large-scale datasets of coastal and hydrometeorological variables, the methodology reveals substantial temporal and spatial variability in compound hazard characteristics, highlighting the limitations of stationary and univariate assumptions. Ongoing developments extend the framework toward a unified multihazard modeling chain that consistently integrates oceanic, atmospheric, and terrestrial drivers. By embedding diverse physical processes within a coherent statistical structure, this work advances the representation of compound coastal extremes and provides a robust foundation for next-generation hazard assessments. The proposed approach supports the development of more realistic risk scenarios, offering critical insights for adaptation planning and resilience strategies under present and future climate conditions.
How to cite: Bahmanpour, M. H., Mentaschi, L., Tilloy, A., Vousdoukas, M., Federico, I., Coppini, G., and Feyen, L.: A Non-Stationary Multivariate Framework for Assessing Compound Coastal Hazards at Global Scales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17453, https://doi.org/10.5194/egusphere-egu26-17453, 2026.
