Presentation Information
[_NIQuy3K-03]Enabling Nature-Based Coastal Adaptation through Digital Twin-driven What-if Scenarios for coastal vegetation restoration
*Benjamin Jacob1 (1. Helmholtz-Center Hereon (Germany))
Climate hazards increasingly threaten coastal ecosystems through the intensification of storm events, coastal erosion, and ecosystem degradation. Nature-Based Solutions (NbS), such as seagrass, saltmarshes or mangrove restoration, are increasingly acknowledged as pathways to enhance coastal resilience by dampening driving mechnisms of coastal impacts, while simultaneously supporting biodiversity conservation and carbon storage.
Despite that, effective implementation requires actionable insights on how to choose design, location, and characteristics of restoration measures. These insights can help to maximize ecosystem services such as coastal protection under varying environmental conditions.
Here, we present a Digital Twin of the Ocean (DTO) system aimed at aiding in decision support by integrating observations, numerical models and artificial intelligence (AI) enabling the execution What-if Scenario (WiS) to assess coastal resilience via seagrass restoration:
The DTO system combines regional coupled hydro-wave-sediment simulations with high-resolution coastal morphodynamical modelling to evaluate erosion risks and impacts under different seagrass scenarios. Thereby, considering vegetation characteristics such as meadow location, extent, shoot density to investigate their influence on coastal ocean kinematics and sediment dynamics. An AI-based emulator further enhances computational efficiency allowing the exploration of multiple restoration pathways within the DTO.
These systems have been implemented within the European Digital Twin of the Ocean (EDITO), via downstream interactive user interfaces facilitating the exploration of different vegetation WiS.
The results demonstrate that the effectiveness of seagrass restoration is highly dependent on local environmental conditions and implementation strategies. For example, erosion mitigation potential was identified when seagrass was positioned in areas exposed to high wave roller energy. Overall, the results indicate the need for site-specific assessments rather than generalized restoration approaches.
By translating complex model outputs into actionable metric-based information, the DTO enables the visualization and comparison of different restoration scenarios to support sustainable NbS implementation.
Despite that, effective implementation requires actionable insights on how to choose design, location, and characteristics of restoration measures. These insights can help to maximize ecosystem services such as coastal protection under varying environmental conditions.
Here, we present a Digital Twin of the Ocean (DTO) system aimed at aiding in decision support by integrating observations, numerical models and artificial intelligence (AI) enabling the execution What-if Scenario (WiS) to assess coastal resilience via seagrass restoration:
The DTO system combines regional coupled hydro-wave-sediment simulations with high-resolution coastal morphodynamical modelling to evaluate erosion risks and impacts under different seagrass scenarios. Thereby, considering vegetation characteristics such as meadow location, extent, shoot density to investigate their influence on coastal ocean kinematics and sediment dynamics. An AI-based emulator further enhances computational efficiency allowing the exploration of multiple restoration pathways within the DTO.
These systems have been implemented within the European Digital Twin of the Ocean (EDITO), via downstream interactive user interfaces facilitating the exploration of different vegetation WiS.
The results demonstrate that the effectiveness of seagrass restoration is highly dependent on local environmental conditions and implementation strategies. For example, erosion mitigation potential was identified when seagrass was positioned in areas exposed to high wave roller energy. Overall, the results indicate the need for site-specific assessments rather than generalized restoration approaches.
By translating complex model outputs into actionable metric-based information, the DTO enables the visualization and comparison of different restoration scenarios to support sustainable NbS implementation.
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