Variability in Coastal Flooding Predictions due to Forecast Errors during Hurricane Arthur

Storm surge prediction models rely on an accurate representation of the wind conditions. In this paper, we examine the sensitivity of surge predictions to forecast uncertainties in the track and strength of a storm (storm strength is quantified by the power dissipation of the associated wind field). This analysis is performed using Hurricane Arthur (2014), a Category 2 hurricane, which made landfall along the North Carolina (NC) coast in early July 2014. Hindcast simulations of a coupled hydrodynamic-wave model are performed on a large unstructured mesh to analyze the surge impact of Arthur along the NC coastline. The effects of Arthur are best represented by a post-storm data assimilated wind product with parametric vortex winds providing a close approximation. Surge predictions driven by forecast advisories issued by the National Hurricane Center (NHC) during Arthur are analyzed. The storm track predictions from the NHC improve over time. However, successive advisories predict an unrealistic increase in the storm’s strength. Due to these forecast errors, the global root mean square errors of the predicted wind speeds and water levels increase as the storm approaches landfall. The relative impacts of the track and strength errors on the surge predictions are assessed by replacing forecast storm parameters with the best known post-storm information about Arthur. In a “constant track” analysis, Arthur’s post storm determined track is used in place of the track predictions of the different advisories but each advisory retains its size and intensity predictions. In a “constant storm strength” analysis, forecast wind and pressure parameters are replaced by corresponding parameters extracted from the post storm analysis while each advisory retains its forecast storm track. We observe a strong correlation between the forecast errors and the wind speed predictions. However, the correlation between these errors and the forecast water levels is weak signifying a non-linear response of the shallow coastal waters to meteorological forcing.

R Cyriac, JC Dietrich, JG Fleming, BO Blanton, C Kaiser, CN Dawson, RA Luettich (2018). “Variability in Coastal Flooding Predictions due to Forecast Errors during Hurricane Arthur.Coastal Engineering, 137(1), 59-78. DOI: 10.1061/(ASCE)WW.1943-5460.0000419.

Improving Accuracy of Real-Time Storm Surge Inundation Predictions

Emergency managers rely on fast and accurate storm surge predictions from numerical models to make decisions and estimate damages during storm events. One of the challenges for such models is providing a high level of resolution along the coast without significantly increasing the computational time. Models with large domains, such as the ADvanced CIRCulation (ADCIRC) model used in this study, are accurate in predicting water levels and their variation in complex coastal regions, however their spatial resolution may limit their predictions of flooding at the scale of buildings, roadways, and critical infrastructure.

A new tool has been developed that uses Geographic Information System (GIS) scripts to enhance the resolution of maximum water level predictions at the boundary of predicted flooding using a high-resolution Digital Elevation Model (DEM). The water levels predicted by the lower resolution model are extrapolated outward to where the water would intersect with the higher resolution elevation dataset. The result is a highly-refined flooding boundary that represents inundation on scales smaller than the typical ADCIRC mesh resolution. This tool can process a 15-m DEM for all 32 coastal counties of the state of North Carolina in less than 15 minutes during a storm event.

Comparison of results using spatial building datasets showed that for a simulation of Hurricane Matthew, 2,353 buildings were predicted to be flooded in Carteret County, NC, prior to enhancing resolution and 3,298 post-enhancement, an increase of 40 percent. In Dare County, the increase was 22 percent. This dramatic increase in flooded buildings shows the importance of achieving high accuracy in floodplains, as a relatively small change in predicted flooding extent can have a substantial impact on the predicted number of flooded buildings. The validity of these results was tested via comparisons to results of an ADCIRC model with the same 15-m resolution as the DEM in Dare County. Dare County is a coastal region with widely-varying topography and land cover, and preliminary comparisons have shown that the GIS method is accurate in coastal regions with steeper slopes and less accurate in flatter, low-lying areas.

N Tull (2018). “Improving Accuracy of Real-Time Storm Surge Inundation Predictions,North Carolina State University.

Sensitivity of Storm Surge Predictions to Atmospheric Forcing during Hurricane Isaac

Storm surge and overland flooding can be predicted with computational models at high levels of resolution. To improve efficiency in forecasting applications, surge models often use atmospheric forcing from parametric vortex models, which represent the surface pressures and wind fields with a few storm parameters. The future of storm surge prediction could involve real-time coupling of surge and full-physics atmospheric models; thus, their accuracies must be understood in a real hurricane scenario. The authors compare predictions from a parametric vortex model (using forecast tracks from the National Hurricane Center) and a full-physics coupled atmosphere-wave-ocean model during Hurricane Isaac (2012). The predictions are then applied within a tightly coupled, wave and surge modeling system describing the northern Gulf of Mexico and the floodplains of southwest Louisiana. It is shown that, in a hindcast scenario, a parametric vortex model can outperform a data-assimilated wind product, and given reasonable forecast advisories, a parametric vortex model gives reasonable surge forecasts. However, forecasts using a full-physics coupled model outperformed the forecast advisories and improved surge forecasts. Both approaches are valuable for forecasting the coastal impacts associated with tropical cyclones

JC Dietrich, A Muhammad, M Curcic, A Fathi, CN Dawson, SS Chen, RA Luettich (2018). “Sensitivity of Storm Surge Predictions to Atmospheric Forcing during Hurricane Isaac.Journal of Waterway, Port, Coastal, and Ocean Engineering, 144(1), DOI: 10.1061/(ASCE)WW.1943-5460.0000419

Climate Change Effects on Flooding During Hurricane Sandy

Hurricane Sandy devastated the Northeast US coastline in 2012. In New York City, it caused power outages that affected nearly 2 million people, forced evacuations of 6500 patients from hospitals and nursing homes, prevented 1.1 million children from attending school for a week, and disrupted the daily travel of about 11 million commuters. Many of these impacts were related to flooding of critical infrastructure, including nearly 90,000 buildings, and more than $5 billion in damages in the mass transit system. The maximum observed water level at the tidal gauge located at the southern tip of Manhattan was 5.3 m above the station datum and 2.8 m above the expected tide. This additional water, known as storm surge, was pushed from the open sea by strong winds during the storm. Sandy was one of several recent storms to cause flooding along the US Gulf and Atlantic coasts, including Katrina and Rita (2005), Gustav and Ike (2008), Irene (2011), Isaac (2012), and Hermine and Matthew (2016). Climatic changes are causing these storms to be larger and more intense, last longer, and move farther northward. Their impacts will be more severe to communities in coastal regions in the future.

JC Dietrich (2018). “Vignette: Climate Change Effects on Flooding During Hurricane Sandy.” Disaster Epidemiology: Methods and Applications, Academic Press, JA Horney, ed., 153-156, DOI: 10.1016/B978-0-12-809318-4.00020-4.

An Earth’s Future Special Collection: Impacts of the coastal dynamics of sea level rise on low-gradient coastal landscapes

Rising sea level represents a significant threat to coastal communities and ecosystems, including altered habitats and increased vulnerability to coastal storms and recurrent inundation. This threat is exemplified in the northern Gulf of Mexico, where low topography, marshes, and a prevalence of tropical storms have resulted in extensive coastal impacts. The ability to facilitate adaptation and mitigation measures relies, in part, on the development of robust predictive capabilities that incorporate complex biological processes with physical dynamics. Initiated in 2010, the 6-year Ecological Effects of Sea Level Rise—Northern Gulf of Mexico project applied a transdisciplinary science approach to develop a suite of integrated modeling platforms informed by empirical data that are capable of evaluating a range of climate change scenarios. This special issue highlights resultant integrated models focused on tidal hydrodynamics, shoreline morphology, oyster ecology, coastal wetland vulnerability, and storm surges that demonstrate the need for dynamic models to incorporate feedbacks among physical and biological processes in assessments of sea level rise effects on coastal systems. Effects are projected to be significant, spatially variable and nonlinear relative to sea level rise rates. Scenarios of higher sea level rise rates are projected to exceed thresholds of wetland sustainability, and many regions will experience enhanced storm surges. Influenced by an extensive collaborative stakeholder engagement process, these assessments on the coastal dynamics of sea level rise provide a strong foundation for resilience measures in the northern Gulf of Mexico and a transferable approach for application to other coastal regions throughout the world.

DM Kidwell, JC Dietrich, SC Hagen, SC Medeiros (2017). “An Earth’s Future Special Collection: Impacts of the coastal dynamics of sea level rise on low-gradient coastal landscapes.Earth’s Future, 5(1), 2-9, DOI: 10.1002/2016EF000493.

Characterizing Hurricane Storm Surge Behavior in Galveston Bay using the SWAN+ADCIRC Model

CE2014The SWAN+ADCIRC shallow-water circulation model, validated for Hurricane Ike (2008), was used to develop five synthetic storm surge scenarios for the upper Texas coast in which wind speed was increased and landfall location was shifted 40 km westward. The Hurricane Ike simulation and the synthetic storms were used to study the maximum water elevations in Galveston Bay, as well as the timing and behavior of surge relative to the hurricane track. Sixteen locations indicative of surge behavior in and around Galveston Bay were chosen for analysis in this paper. Results show that water surface elevations present in Galveston Bay are dominated by the counterclockwise hurricane winds and that increasing wind speeds by 15% results in approximately 23% (+/−3%) higher surge. Furthermore, shifting the storm westward causes higher levels of surge in the more populated areas due to more intense, higher shore-normal winds. This research helps to highlight the vulnerability of the upper Texas Gulf Coast to hurricane storm surge and lends insight to storm surge and flood mitigation studies in the Houston–Galveston region.

AG Sebastian, JM Proft, JC Dietrich, W Du, PB Bedient, CN Dawson (2014). “Characterizing Storm Surge Behavior in Galveston Bay using the SWAN + ADCIRC Model.Coastal Engineering, 88, 171-181, DOI: 10.1016/ j.coastaleng.2014.03.002.