Pixels for Public Health: Analytic Collaborative Framework to Enhance Coastal Resiliency of Vulnerable Populations in Hampton Roads, Virginia

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, VA, 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. To address these hazards and proactively mitigate future vulnerability, this project proposes an innovative analytic collaborative framework (ACF) and a Digital Twin approach. We seek to more fully utilize Earth observations and computing to provide improved predictive decision support tools. We propose to design and demonstrate to an operational state a system linking an Earth Observation (EO) data source (Virginia Open Data Cube), a socio-spatial-health information "Digital Neighborhood" (DN) (Hampton Roads Biomedical Research Consortium), hydrodynamic models, and in situ flood sensor network. This Digital Twin approach will connect observational and physical environmental domains with human vulnerability. Although individual technologies are fairly mature, they remain siloed and uneven, with limited interoperability, and challenging to operationalize and innovate predictive models or ask what-if scenarios. EO data are leveraged with the Virginia Data Cube using Landsat, Sentinel, MODIS (and forthcoming NISAR) missions to improve hydrodynamic model prediction of flood events by calibration from satellite, autonomous unmanned systems, and linking to smart community IoT flood sensors. This project increases technology-readiness levels system of systems and emphasizes the catalyst role of geospatial integration of flood modeling, predictive analytics, and place-based community vulnerability. Dynamic uncertainties in sea level and flood processes are also analyzed to better plan for worsening future threats. Climate models are used to reconstruct historic flood attribution and estimate future probabilities of flooding, differentiating tidal, surge, extreme rainfall, and compound flood events. A GeoHub is developed to implement the framework and lay a foundation for adoption by flood forecasters, planners, health practitioners and emergency managers, reflecting growing recognition of the need for convergence of modeling and stakeholder engagement and participation (Baustian et al. 2020; Hemmerling et al. 2020.) The hub provides a resource of open science data, models and algorithms for fellow scientists and practitioners. We extend this portal to build stakeholder adoption in a proposed third year – drawing end-users to learn and train in a functional exercise simulation. The hub serves as a resource for forecasters, emergency managers, and hazard mitigation planners that integrates diverse vetted data, geospatial tools, and predictive spatial analytics of flood exposure with improved granularity for human health. The resulting technology will demonstrate new analytical and collaborative approaches for modeling, IoT sensor, and EO data integration, synergy between physical earth science and social science Digital Twins, and practical tools for timely and equitable flood response and planning.

Advance Earth system science knowledge through the Identification, develop, and demonstrate innovative information systems technologies

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