Coastal and nearshore zones face growing pressure from storms, flooding, erosion, and sea-level rise, which threaten civil infrastructure such as ports, seawalls, bridges, pipelines, and coastal-protection systems. Traditional monitoring still relies on static design criteria or post-event response, so it rarely anticipates imminent failures. At the same time, high-resolution environmental data from satellites, in-situ sensors, and radar, together with advances in numerical modelling, now make predictive early warning systems (EWS) feasible. The open challenge is turning diverse, heterogeneous data streams and forecasting models into operational, decision-ready tools that protect specific assets. A coordinated effort is needed to bridge state-of-the-art forecasting and practical, real-time warnings for coastal infrastructure resilience.
This Research Topic aims to advance the scientific and technical foundations of early warning systems designed specifically for civil infrastructure in changing coastal and nearshore environments. Its distinctive focus is infrastructure-specific, impact-based prediction: while most existing EWS — and broader coupled monitoring-and-management frameworks — stop at hazard forecasting (for example, storm-surge warnings), this Topic targets the next step, predicting how a given asset will respond and when it may fail. We invite contributions that couple forecasting models (waves, water levels, currents) with data assimilation, machine learning, and real-time monitoring to deliver reliable, lead-time warnings of infrastructure performance and failure risk such as overtopping, scour, and structural loading. Advances in IoT sensor networks, satellite altimetry, high-frequency radar, and ensemble prediction offer new opportunities to improve predictive accuracy and uncertainty quantification. We welcome studies that demonstrate end-to-end EWS frameworks — from data acquisition and forecasting to threshold definition, alert generation, and communication with end-users.
Beyond methods, the Topic places strong emphasis on operational implementation and real-world uptake. We are particularly interested in work that moves from prototype to practice: business-as-usual deployment, integration with the workflows of port authorities, coastal-defence operators, and emergency managers, and evidence of how warnings are acted upon. By linking infrastructure-specific warnings to wider decision-making, the Topic also seeks to contribute to broader coastal-system resilience — protecting individual assets in a way that strengthens the resilience of the communities, economies, and ecosystems that depend on them. By combining hydrodynamics, geotechnics, structural engineering, data science, and implementation experience, this Topic seeks transferable, deployable methods and tools for proactive, risk-informed management of coastal assets.
We welcome Original Research, Reviews, Methods, Technology and Code, Perspectives, and case-study reports addressing the development, validation, or operational deployment of EWS for coastal and nearshore infrastructure, with particular interest in the integration of forecasting and data. Specific themes include:
• Real-time forecasting of hazardous nearshore processes (waves, storm surges, sea-level anomalies, sediment transport) relevant to infrastructure stability.
• Data assimilation and fusion methods combining heterogeneous observations (in-situ, remote sensing, crowdsourced) with predictive models.
• Machine learning and hybrid approaches for forecast accuracy, uncertainty quantification, or rapid damage prediction.
• Threshold setting and risk indicators linking environmental forcing to infrastructure-specific failure modes (overtopping, scour, slope instability).
• Operational or prototype EWS for ports, coastal defenses, bridges, or submerged pipelines, including validation and user feedback.
• Digital twins and decision-support platforms integrating real-time data, forecasting, and alert dissemination for coastal infrastructure management.
• Implementation, stakeholder uptake, and governance of EWS — including integration into operational workflows, cost-effectiveness, communication of uncertainty, and barriers to adoption.
• Linking asset-level early warning to broader coastal-system resilience outcomes, including community safety, service continuity, and adaptive long-term planning.

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Early Warning Systems for Coastal and Nearshore Infrastructure: Integrating Forecasting, Data Assimilation, and Machine Learning