Digitally Twinning Coastal Resilience Via Multsensor Imagery, in Situ Sensors, and Geospatial Analysis

Increasing coastal flooding owing to sea level rise and climate-change drivers of extreme precipitation combine to threaten vulnerable communities, posing imminent as well as evolving dynamic threats given sea-level rise and climate changes. A diversity of social, economic and cultural vulnerabilities, and coping capacities exist across coastal communities, yet decision support systems for response and planning alike are disparate and siloed. Vulnerable urban communities contend with the legacy of racial segregation and discrimination, with manifest disparities leading to unmet health related social needs (HRSNs) such as access to basic resources and health care to treat higher hazard exposures. Coastal cities such as Norfolk, Virginia, USA, exhibit increased tidal, rainfall, and storm surge flooding owing to sea level and climate changes exacerbated by subsidence. Cities lack high-resolution compound flood forecasting and have disparity in exposure and inequitable outcomes, and timely response and future planning to mitigate or adapt to hazards require new techniques and data integration. This project developed the needs, framework and prototype digital architecture and foundations for a digital twin to enable analysis and predictive modeling of coastal flooding. Results outline the impediments and opportunities for open data and scientific analysis of interoperable in situ sensors (coastal flooding), Earth observation imagery, hydrodynamic models, and geospatial data and predictive impact models.