Hurricane Sandy inflicted heavy damage in New York City and the New Jersey coast as the second costliest storm in history. A large-scale, unstructured grid storm tide model, Semi-implicit Eulerian Lagrangian Finite Element (SELFE), was used to hindcast water level variation during Hurricane Sandy in the mid-Atlantic portion of the U.S. East Coast. The model was forced by eight tidal constituents at the model’s open boundary, 1500 km away from the coast, and the wind and pressure fields from atmospheric model Regional Atmospheric Modeling System (RAMS) provided by Weatherflow Inc. The comparisons of the modeled storm tide with the NOAA gauge stations from Montauk, NY, Long Island Sound, encompassing New York Harbor, Atlantic City, NJ, to Duck, NC, were in good agreement, with an overall root mean square error and relative error in the order of 15–20 cm and 5%–7%, respectively. Furthermore, using large-scale model outputs as the boundary conditions, a separate sub-grid model that incorporates LIDAR data for the major portion of the New York City was also set up to investigate the detailed inundation process. The model results compared favorably with USGS’ Hurricane Sandy Mapper database in terms of its timing, local inundation area, and the depth of the flooding water. The street-level inundation with water bypassing the city building was created and the maximum extent of horizontal inundation was calculated, which was within 30 m of the data-derived estimate by USGS.

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 (United States Geological Survey [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), whereas 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.

Changes in the eustatic sea level have enhanced the impact of inundation events in the coastal zone, ranging in significance from tropical storm surges to pervasive nuisance flooding events. The increased frequency of these inundation events has stimulated the production of interactive web-map tracking tools to cope with changes in our changing coastal environment. Tidewatch Maps, developed by the Virginia Institute of Marine Science (VIMS), is an effective example of an emerging street-level inundation mapping tool. Leveraging the Semi-implicit Cross-scale Hydro-science Integrated System Model (SCHISM) as the engine, Tidewatch operationally disseminates 36-h inundation forecast maps with a 12-h update frequency. SCHISM’s storm tide forecasts provide surge guidance for the legacy VIMS Tidewatch Charts sensor-based tidal prediction platform, while simultaneously providing an interactive and operationally functional forecast mapping tool with hourly temporal resolution and a 5 m spatial resolution throughout the coastal plain of Virginia, USA. This manuscript delves into the hydrodynamic modeling and geospatial methods used at VIMS to automate the 36-h street-level flood forecasts currently available via Tidewatch Maps, and the paradigm-altering efforts involved in validating the spatial, vertical, and temporal accuracy of the model.

Sea level rise has increased the frequency of tidal flooding even without accompanying precipitation in many coastal areas worldwide. As the tide rises, inundates the landscape, and then recedes, it can transport organic and inorganic matter between terrestrial systems and adjacent aquatic environments. However, the chemical and biological effects of tidal flooding on urban estuarine systems remain poorly constrained. Here, we provide the first extensive quantification of floodwater nutrient concentrations during a tidal flooding event and estimate the nitrogen (N) loading to the Lafayette River, an urban tidal sub-tributary of the lower Chesapeake Bay (USA). To enable the scale of synoptic sampling necessary to accomplish this, we trained citizen-scientist volunteers to collect 190 flood water samples during a perigean spring tide to measure total dissolved N (TDN), dissolved inorganic N (DIN) and phosphate concentrations, and Enterococcus abundance from the retreating ebb tide while using a phone application to measure the extent of tidal inundation. Almost 95% of Enterococcus results had concentrations that exceeded the standard established for recreational waters (104 MPN 100 mL-1). Floodwater dissolved nutrient concentrations were higher than concentrations measured in natural estuarine waters, suggesting floodwater as a source of dissolved nutrients to the estuary. However, only DIN concentrations were statistically higher in floodwater samples than in the estuary. Using a hydrodynamic model to calculate the volume of water inundating the landscape, and the differences between the median DIN concentrations in floodwaters and the estuary, we estimate that 1,145 kg of DIN entered the Lafayette River during this single, blue sky, tidal flooding event. This amount exceeds the annual N load allocation for overland flow established by federal regulations for this segment of the Chesapeake Bay by 30%. Because tidal flooding is projected to increase in the future as sea levels continue to rise, it is crucial we quantify nutrient loading from tidal flooding in order to set realistic water quality restoration targets for tidally influenced water bodies.

Loftis, J.D.; Wang, H.V.; DeYoung, R.J., and Ball, W.B., 2016. Using lidar elevation data to develop a topobathymetric digital elevation model for sub-grid inundation modeling at Langley Research Center. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 134–148. Coconut Creek (Florida), ISSN 0749-0208. Technological progression in light detection and ranging permits the production of highly detailed digital elevation models, which are useful in sub-grid hydrodynamic modeling applications. Sub-grid modeling technology is capable of incorporating these high-resolution lidar-derived elevation measurements into the conventional hydrodynamic modeling framework to resolve detailed topographic features for inclusion in a hydrological transport model for runoff simulations. The horizontal resolution and vertical accuracy of the digital elevation model is...

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.