A digital twin to link flood models, sensors, and earth observations for coastal resilience in hampton roads, virginia, usa

A Digital Twin (DT) for coastal resilience in a low-lying coastal city necessarily entails characterizing the geophysical terrain, complex hydrologic flows, and infrastructure modifications that affect flooding. Coastal flooding in ports such as Hampton Roads, Virginia, arise from coastal storms, regular tidal estuarine circulation processes, seasonal to interannual external forcing from the ocean via the Gulf Stream and Atlantic Meridional Overturning Circulation (AMOC), and associated local influences on sea-level anomalies. We take a geospatial, hub-based approach to integrate digital linkages and threads among real-time Internet of Things (IoT) water level and flood sensors, an operational hydrodynamic model, localized probabilistic sealevel rise projections, hydro-corrected LiDAR Digital Elevation Models and GIS stormwater infrastructure, and an open data cube that ingests, hosts, and processes Application-Ready Datasets ( ARDs.) Our paper describes the structure and cloud integration of a flood sensor network, StormSense, and its augmentation with end-user identified gap sites to improve network coverage and sensitivity to vulnerable communities. The enhancement of the multiple flood sensor networks incorporates multiple APIs and Amazon Web Services to normalize and homogenize data feeds among municipal sensors, private sector sensors, NOAA and USGS gauges, and new sensors sited in partnership with emergency managers, non-profits, and the National Weather Service. We describe the development and improvements of a Delft3D model for hydrodynamic simulation, including characterization and grid improvements of shallow estuarine creeks. The model includes sea-level rise parameters for the Hampton Roads study area. The paper also outlines Earth Observing data in the Virginia Open Data Cube, including products from NASA, NOAA, ESA, and newly incorporated drone data. The data cube hosts multi-purpose data products, such as satellite derived Land Use/Land Cover, MODIS, Landsat, and Sentinel datasets, and large high-resolution, hydro-corrected LiDAR DEMs. We link the data cube to an ArcGIS Hub to provide end-products, disseminate content, and apply GIS analyses for real-time and simulated future scenario dashboard. The hub provides an end-user interface for impact modeling, what-if scenarios and decision support. The paper builds DT infrastructure to support emergency management, public health, and resilience planning.