ENHANCE: Exploring how climate change affects coastal cliff recession: modelling and forecasting

作为地球上最发达的地区之一，沿海地区拥有最集中的资产、丰富的发展、关键的基础设施和复杂的生态系统。然而，由于全球气候变化，海岸洪水、滑坡、海啸等自然灾害的加速发生，使海岸线受到前所未有的威胁，导致海岸侵蚀严重，向陆地后退。已有研究证实，海岸线退缩速率高度依赖于波浪作用、温度和降雨等环境驱动因素，遵循高度复杂的非线性关系，而渐进性悬崖破坏的机制尚不清楚。Enhance将利用先进的数字建模和人工智能(AI)等新兴数字技术来调查沿海山体滑坡的触发机制和缓解措施。通过数据驱动的人工智能分析，将澄清控制悬崖稳定性的关键气候因素及其相互依存关系。在此基础上，采用连续-不连续耦合模型研究了底切-缺口效应对海岸悬崖渐进破坏的影响，阐明了海岸悬崖的大变形、裂纹扩展和质量运动。提出了一种新的流固热耦合本构模型，用于研究气候变化条件下海岸边坡的多物理响应。开发的数值模拟平台旨在通过明确考虑地质地层性质的时间和空间变异性而可扩展到欧盟/世界范围内的应用。因此，将提出一个综合沿海悬崖管理框架，以便就城市规划、灾害预测和减灾作出有效的决策。这些发现将有助于联合国2030年可持续发展目标议程(9，11，13)和欧洲绿色协议，具有巨大的学术价值和工程影响。

As one of the most developed areas on this planet, the coastal regions have the greatest concentration of assets, abundant developments, critical infrastructures and complex ecosystems. However, due to the global climate change, the coastlines are unprecedentedly threatened by the accelerated occurrence of natural hazards, such as coastal flooding, landslide and tsunami, leading to serious coastal erosion and retreating landward. Some existing researches have confirmed that the coastline retreating rate depends highly on the environmental drivers such as wave action, temperature and rainfall, following a highly complex and non-linear relationship, while the mechanisms of progressive cliff failures remain unclear. ENHANCE will employ the emerging digital technologies of advanced numerical modelling and Artificial Intelligence (AI) to investigate the triggering mechanisms of coastal landslides and mitigation measures. The key climatic factors governing the cliff stability and their inter-dependency relationship will be clarified by the data-driven AI analyses. Then, a coupled continuous-discontinuous modelling will be performed to investigate the undercutting-notch effect on the progressive failure of coastal cliffs, clarifying the large deformation, crack propagation and mass movement. A novel fluid-solid-thermal coupling constitutive model will be developed to investigate the multi-physics responses of coastal slopes under changing climate conditions. The developed numerical modelling platform aims to be scalable for EU/worldwide applications by explicitly considering the temporal and spatial variabilities of geological formation properties. Consequently, an integrated coastal cliff management framework will be proposed for effective decision-makings on urban planning, hazard forecasting and mitigation. The findings will contribute to the UN 2030 Agenda for SDGs (9, 11, 13) and the European Green Deal, featuring great intellectual merits and engineering impacts.