Propagation of cost-effective water level sensors powered through the Internet of Things (IoT) has expanded the available offerings of ingestible data streams at the disposal of modern smart cities. StormSense is an IoT-enabled inundation forecasting research initiative and an active participant in the Global City Teams Challenge seeking to enhance flood preparedness in the smart cities of Hampton Roads, VA for flooding resulting from storm surge, rain, and tides. In this study, we present the results of the new StormSense water level sensors to help establish the “regional resilience monitoring network” noted as a key recommendation from the Intergovernmental Pilot Project. To accomplish this, the Commonwealth Center for Recurrent Flooding Resiliency’s Tidewatch tidal forecast system is being used as a starting point to integrate the extant (NOAA) and new (USGS and StormSense) water level sensors throughout the region, and demonstrate replicability of the solution across the cities of Newport News, Norfolk, and Virginia Beach within Hampton Roads, VA. StormSense’s network employs a mix of ultrasonic and radar remote sensing technologies to record water levels during 2017 Hurricanes Jose and Maria. These data were used to validate the inundation predictions of a street-level hydrodynamic model (5-m resolution), while the water levels from the sensors and the model were concomitantly validated by a temporary water level sensor deployed by the USGS in the Hague, and crowd-sourced GPS maximum flooding extent observations from the Sea Level Rise app, developed in Norfolk, VA.

Abstract. Remote sensing analysis is routinely used to map flooding extent either retrospectively or in near-real-time. For flood emergency response, remote sensing-based flood mapping is highly valuable as it can offer continued observational information about the flood extent over large geographical domains. Information about the floodwater depth across the inundated domain is important for damage assessment, rescue, and to prioritize relief resource allocation, but cannot be readily estimated from remote sensing analysis. The Floodwater Depth Estimation Tool (FwDET) was developed to augment remote sensing analysis by calculating water depth based solely on an inundation map with an associated Digital Elevation Model (DEM). The tool was shown to be accurate and was used in flood response activations by the Global Flood Partnership. Here we present a new version of the tool, FwDET v2.0, which enables water depth estimation for coastal flooding. FwDET v2.0 features a new flood boundary identification scheme which accounts for the lack of confinement of coastal flood domains at the shoreline. A new algorithm is used to calculate the local floodwater elevation for each cell, which improves the tool's runtime by a factor 15 and alleviates inaccurate local boundary assignment across permanent water bodies. FwDET v2.0 is evaluated against physically-based hydrodynamic simulations in both riverine and coastal case studies. The results show good correspondence, with an average difference of 0.18 m and 0.31 m for the coastal (using a 1-m DEM) and riverine (using a 10-m DEM) case studies respectively. A FwDET v2.0 application of using remote sensing derived flood maps is presented for three case studies. These case studies showcase FwDET v2.0 ability to efficiently provide a synoptic assessment of floodwater. Limitations include challenges in obtaining high-resolution DEMs and increases in uncertainty when applied for highly fragmented flood inundation domains.

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

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