Posters 1

[Poster 1-05] Citizen Science–Driven Digital Twins for Coastal Monitoring and Risk Governance

*Ronald Pelot¹ (1. Dalhousie University (Canada))

Digital twins have emerged as powerful tools for representing and understanding complex marine systems, yet their application to dynamic coastal environments remains comparatively underdeveloped, particularly when driven by citizen science observations. This paper presents an emerging initiative to establish a network of citizen science monitoring nodes across multiple coastal communities on the Eastern Shore of Nova Scotia. Volunteers collect environmental observations using standardized protocols, including meteorological conditions (e.g., air temperature, wind speed and direction, precipitation), beach morphology (e.g., sediment grain size, dune position relative to the shoreline), coastal vegetation characteristics, and bird presence. These observations are stored in structured databases and will serve as continuous and periodic input streams to a digital twin representing the monitored coastal sites.

The proposed digital twin integrates these heterogeneous datasets with geospatial information and numerical environmental models to create a dynamic virtual representation of each coastal location. The model will provide community members, researchers, and decision-makers with near real-time visualization of current environmental conditions while supporting scenario analyses of coastal evolution under storms, sea-level rise, erosion, habitat change, and other climate-driven pressures. Additional data streams from government weather stations, water-level gauges, wave buoys, satellite imagery, drone surveys, and automated cameras can further improve temporal resolution and model fidelity.

Beyond environmental monitoring, the digital twin offers a platform for enhancing coastal literacy, supporting community engagement, and strengthening social licence for adaptation initiatives by making environmental change more transparent and accessible. It also provides a common evidence base for collaborative coastal risk governance, enabling stakeholders to evaluate the potential consequences of management decisions, compare adaptation strategies, and identify emerging vulnerabilities. The paper discusses the architecture of such a citizen science-enabled coastal digital twin, the data integration challenges involved, and its potential to bridge scientific modelling with community-based environmental stewardship and climate resilience.