Sept. 4, 2025,
10:45 –
11:45
Room info:
Lecture Hall
Timetable
Sept. 4, 2025
Technology is revolutionizing our approach to environmental challenges. Among the most promising tools of digitalization is the Digital Twin (DT), or more specifically the Digital Twin of the Ocean (DTO). This is a virtual replica of the ocean that holds immense potential for sustainable marine development. In order to successfully confront the increasing impacts and hazards of a changing climate (such as coastal erosion and flooding), it is vital to further develop the DTO in order to be able to monitor, predict, and protect vulnerable coastal communities. DTOs are powered by AI-enhanced data that integrates ocean conditions, ecosystems, and anthropogenic influences, along with novel AI-driven predictive modeling capabilities, combining wave, hydrodynamic, and morphodynamic models. This enables unprecedented accuracy in seamless forecasting capabilities. In addition to natural phenomena, DTOs can also include socio-economic factors (e.g. ocean-use, pollution). Thus, DTOs can be used to monitor the current ocean state, but also to simulate future ‘What-if’ Scenarios (WiS) for various human interventions. In this way the DTO can guide decisions for protecting the coast and sustainable use of marine resources, while also promoting collaboration on effective solutions for ocean conservation.
In European projects such as the European Digital Twin Ocean (EDITO) ModelLab, work …
Meeting room
Lecture Hall
Digital twins of the ocean (DTO) make marine data available to support the development of the blue economy and enable a direct interaction through bi-directional components. Typical DTOs provide insufficient detail near the coast, because their resolution is too coarse and the underlying models lack processes that become relevant in shallow areas, e.g., at wetting and drying of tidal flats. As roughly 2.13 Billion of the world’s population live near a coast, downscaling ocean information to a local scale becomes necessary, as many practical applications, e.g., sediment management, require high resolution data. For this reason, we focused on the appropriate downscaling of regional and global data from existing DTOs using a high-resolution (100s of meters), unstructured, three-dimensional, process-based hindcast model in combination with in-situ observations. This high-resolution model allows the fine tidal channels, estuaries, and coastal structures like dams and flood barriers to be represented digitally. Our digital twin includes tidal dynamics, salinity, sea water temperature, waves, and suspended sediment transport. Thanks to a fast and intuitive web interface of our prototype digital twin, the model data provided enable a wide range of coastal applications and support sustainable management. Bi-directional web processing services (WPS) were implemented within the interactive web-viewer …
Meeting room
Lecture Hall
The rapid growth of offshore wind energy requires effective decision-support tools to optimize operations and manage risks. To address this, we developed iSeaPower, a web-based platform designed to support decision-making in offshore renewable energy tasks through real-time data analysis and interactive visualizations. iSeaPower integrates detailed meteorological and oceanographic data with advanced statistical methods, machine learning forecasts, and data assimilation techniques. This integration enables accurate predictions of weather windows, thorough risk assessments, and efficient operational planning for offshore wind energy stakeholders. iSeaPower is designed to optimize journey planning by considering weather conditions and travel duration. The current framework includes five methods tailored to different operational requirements. First, the forecasting method evaluates wind speed and wave height risks over short-term windows (1–3 days) using real-time weather data to quickly identify potential hazards. Second, historical database analysis calculates exceedance probabilities based on 30-day intervals from long-term historical data, revealing recurring weather risk patterns. Third, the delay time estimation method determines potential task delays across the entire year by analyzing monthly weather trends, supporting long-term operational planning and risk management. Fourth, machine learning approaches enhance the accuracy of seven-day forecasts by combining historical data with machine learning, improving short-term predictions. Finally, the updated statistics …