Presentation Information
[_NIQuy3K-04]An Interactive Coastal Digital Twin of the Ocean for User-Driven What-If Scenarios and Nature-Based Coastal Resilience
*Ivan Federico1 (1. CMCC - Euro-Mediterranean Center on Climate Change (Italy))
Coastal Digital Twins of the Ocean (C-DTOs) are evolving from numerical models into interactive decision-support systems that integrate Earth observations, in situ monitoring, physics-based simulations and Artificial Intelligence. Their objective is not only to reproduce coastal dynamics but also to enable users to explore what-if scenarios, evaluate adaptation strategies and support evidence-based coastal management.
We present an integrated Coastal Digital Twin architecture for coastal resilience. The framework combines multi-source observations, deterministic numerical models and AI-based services to represent the coupled dynamics of waves, hydrodynamics, sediment transport, coastal flooding and marine vegetation. Earth observations and in situ measurements underpin model calibration and validation, while AI accelerates computational workflows and scenario exploration. The architecture is relocatable across coastal environments and supports forecasting, event reconstruction and on-demand simulations.
The Digital Twin is implemented as an interactive, user-centred platform that enables users to configure, execute and visualize customized what-if scenarios through an intuitive graphical interface. Users can delineate intervention areas, modify nature-based or engineered coastal measures, define environmental forcing conditions and compare adaptation strategies. User-defined configurations are automatically translated into model-ready simulations, with results visualized through maps, time series and impact indicators for rapid evaluation of alternative interventions.
The framework is demonstrated through two operational applications. 1) The first supports seagrass restoration as a nature-based solution for coastal protection. Users can define the location, extent and characteristics of restored seagrass meadows and evaluate their effectiveness in reducing wave energy and circulation. 2) The second application focuses on natural dune systems, enabling users to investigate how different dune configurations influence coastal flood hazards by quantifying changes in inundation extent, water levels and flood dynamics.
These applications demonstrate how Coastal Digital Twins transform coastal models into interactive decision-support platforms where observations, process-based simulations and AI are integrated into a seamless workflow, enabling planners and coastal managers to efficiently evaluate adaptation options for resilient coastal management.
We present an integrated Coastal Digital Twin architecture for coastal resilience. The framework combines multi-source observations, deterministic numerical models and AI-based services to represent the coupled dynamics of waves, hydrodynamics, sediment transport, coastal flooding and marine vegetation. Earth observations and in situ measurements underpin model calibration and validation, while AI accelerates computational workflows and scenario exploration. The architecture is relocatable across coastal environments and supports forecasting, event reconstruction and on-demand simulations.
The Digital Twin is implemented as an interactive, user-centred platform that enables users to configure, execute and visualize customized what-if scenarios through an intuitive graphical interface. Users can delineate intervention areas, modify nature-based or engineered coastal measures, define environmental forcing conditions and compare adaptation strategies. User-defined configurations are automatically translated into model-ready simulations, with results visualized through maps, time series and impact indicators for rapid evaluation of alternative interventions.
The framework is demonstrated through two operational applications. 1) The first supports seagrass restoration as a nature-based solution for coastal protection. Users can define the location, extent and characteristics of restored seagrass meadows and evaluate their effectiveness in reducing wave energy and circulation. 2) The second application focuses on natural dune systems, enabling users to investigate how different dune configurations influence coastal flood hazards by quantifying changes in inundation extent, water levels and flood dynamics.
These applications demonstrate how Coastal Digital Twins transform coastal models into interactive decision-support platforms where observations, process-based simulations and AI are integrated into a seamless workflow, enabling planners and coastal managers to efficiently evaluate adaptation options for resilient coastal management.
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