Using a Multi-Resolution Approach to Improve the Accuracy and Efficiency of Flooding Predictions

This research describes a method to improve the accuracy and efficiency of coastal flooding predictions. First, an existing model is used to explore the effect of storm forward speed and timing on tides and storm surge during Hurricane Matthew (2016). It is hypothesized that the spatial variability of Matthew’s effects on total water levels is due to the surge interacting nonlinearly with tides. If the storm occurred a few hours earlier or later, then the largest surges would have been shifted to other regions of the U.S. southeast coast. A change in forward speed of the storm also should alter its associated flooding due to differences in the duration over which the storm impacts the coastal waters. If the storm had moved faster, then the peak water levels would have increased along the coast, but the overall volume of inundation would have decreased. Then this research explores ways to increase the model’s accuracy and efficiency. To better represent Matthew’s effects, a mesh with detailed coverage of the coastal regions from Florida to North Carolina was developed by combining regional meshes originally developed for floodplain mapping. Compared to predictions using the earlier model, the new mesh allows for simulations of inundation that better match to observations especially inland.

Then, to best utilize this new mesh, a multi-resolution approach is implemented to use meshes of varying resolution when and where it is required. It is hypothesized that by `switching’ from coarse- to fine-resolution meshes, with the resolution in the fine mesh concentrated only at specific coastal regions influenced by the storm at that point in time, both accuracy and computational gains can be achieved. As the storm approaches the coastline and the landfall location becomes more certain, the simulation will switch to a fine-resolution mesh that describes the coastal features in that region. Application of the approach during Hurricanes Matthew and Florence revealed the predictions to improve in both accuracy and efficiency, as compared to that from single simulations on coarse- and fine-resolution meshes, respectively.

Finally, the efficiency of the approach is further improved in the case of Hurricane Matthew, by using multiple smaller fine-resolution meshes instead of a single high-resolution mesh for the entire U.S. southeast coast. Simulations are performed utilizing predicted values of water levels, wind speeds, and wave heights, as triggers to switch from one mesh to another. Results indicate how to achieve an optimum balance between accuracy and efficiency, by using the above-mentioned triggers, and through a careful selection of the combination meshes to be used in the approach. This research has the potential to improve the storm surge forecasting process. These gains in efficiency are directly a savings in wall-clock time, which can translate into more time to invest in better models and/or more time for the stakeholders to consider the forecast guidance.

A Thomas (2020). “Using a Multi-Resolution Approach to Improve the Accuracy and Eficiency of Flooding Predictions,” North Carolina State University.

Improving the Accuracy of a Real-Time ADCIRC Storm Surge Downscaling Model

During major storm events such as hurricanes, emergency managers rely on fast and accurate forecasting models to make important decisions concerning public safety. These models can be computationally costly and cannot quickly make predictions at the highest geospatial resolution. However, model output can be post-processed to mimic high-resolution results with minimal additional computational cost. This research proposes methods for improvement in the accuracy of downscaling (enhancing the resolution of) a real-time storm surge forecasting model. Such improvements to downscaling methods include 1) expansion in its spatial applicability, 2) adding physics using water surface slopes, and 3) adding physics using friction losses across the ground surface.

This research builds upon a process that uses maximum water elevation output from the Advanced Circulation (ADCIRC) model and downscales these results to a finer resolution by extrapolating the water levels to small-scale topography. This downscaling process is referred to as the static method. The method was originally designed for use in North Carolina (NC), where results from an ADCIRC model designed specifically for NC were downscaled to a set of NC topographical data. By joining the static method with an ADCIRC output visualization tool, the downscaling process is now able to run faster with the same level of accuracy and can run on any ADCIRC model with downscaling data from any geographical region or given resolution. This process is used to provide extra guidance to emergency managers and decision makers during hurricanes.

The downscaling process is also improved by adding physics using the slopes method and the head loss method. The slopes method incorporates the slopes of the water levels produced by ADCIRC, rather than only the value of the water level. By interpolating ADCIRC output water elevation points into a smooth surface, slopes of this surface can be used to influence the elevations of downscaled water levels. The head loss method adds friction loss due to variations in the ground surface based on land cover types and friction associated with each type. As water travels over any surface, head loss, or a loss in energy, occurs at different rates depending on the surface roughness. This rudimentary hydrologic principle is applied to increase the accuracy of the downscaling process at minimal cost. The downscaling methods are applied for results from an ADCIRC simulation used in real-time forecasting, and then compared with results from an ADCIRC simulation with 10 times more resolution in Carteret County, NC. The static method tends to over-estimate the flood extents, and the slopes method is similar. However, the head loss method generates a downscaled flooding extent that is a close match to the predictions from the higher-resolution, full-physics model.

By improving the accuracy of downscaling methods at minimal computational cost and expanding the applicability of these downscaling methods, these methods can be used by emergency managers to provide a better estimation of flooding extents while simulating storm events.

CA Rucker (2020). “Improving the Accuracy of a Real-Time ADCIRC Storm Surge Downscaling Model,” North Carolina State University.

Wind and Plume Driven Circulation in Estuarine Systems

Mechanistic models with high spatial resolution are useful tools to represent the dynamic and non-linear feedbacks between tides, winds and freshwater inflows in the nearshore and to predict future conditions. In this thesis, several aspects of the wind-and river-plume-driven hydrodynamics and transport in estuarine systems are examined through barotropic and baroclinic models.

The study begins with an application of a state-of-the-art storm surge model to examine the effects of meteorological forecast errors on coastal flooding predictions along the North Carolina (NC) coastline. As Hurricane Arthur (2014) moved over Pamlico Sound, it increased the total water levels to 2.5 m above sea level; this water pushed first into the river estuaries and against the inner banks, and then moved eastward to threaten the sound-side of the barrier islands. It is hypothesized that a combination of storm track and intensity errors caused errors in the forecast winds and water levels along the NC coast during Arthur. Model results reveal that, as the forecast storm track and intensity errors increase, the errors in forecast wind speeds also increase, but the errors in forecast water levels remain relatively the same, signifying the non-linear response of the coastal ocean to wind effects. By separating the forecast errors in storm track and storm strength, this study quantifies their effects on the coastal ocean, which provides useful guidance for designing relevant forecast ensembles.

In addition to flooding impacts, storms can also cause dramatic changes in estuarine salinities, which can negatively impact estuarine ecosystems. Baroclinic models are useful tools for predicting estuarine salinity response under changing environmental conditions. In the present work, the features of wind- and plume-driven circulation in the vicinity of Choctawhatchee Bay (CB) and Destin Inlet, Florida, are analyzed with a recently-enhanced, three-dimensional, baroclinic model. Satellite imagery showed a visible brackish surface plume at Destin during low tide. The goal of the present study is to quantify variability in the plume signature due to changes in tidal and wind forcing. Modeled tides, salinities and plume signature are validated against in-situ observations and satellite imagery and then applied to analyze plume response in two scenarios. In the first case, model plume behavior is analyzed on successive days of near-constant tidal amplitudes and changing wind directions due to passing cold fronts. In the second case, plume response is investigated during consecutive days of neap-spring variability in the tides and near-constant wind speeds. Model results reveal a larger plume during spring tides and periods of weak wind forcing. Oshore winds enhance the north-south expansion of the plume, whereas onshore winds restrict the plume to the coastline.

Finally, the validated model is applied to identify salinity and transport characteristics within CB. Based on past studies, it is hypothesized that CB is a stratified system with limited flushing and zones of distinct salinity gradients. These hypotheses are tested by analyzing bay salinities from the validated model during a period of low river flows. Model surface salinities indicate brackish conditions (20 psu) throughout the bay except for near the river mouth. Stratification (10 to 15 psu) within the bay is unaffected by the passage of cold fronts and neap-spring tidal variability. The residence time within the Choctawhatchee Bay, an important indicator of estuarine health, is computed via particle tracking and is equal to roughly 40 days.

This work advances the scientific understanding of multiple aspects of estuarine circulation including wind-driven surge and flooding, brackish plume behavior through inlets and onto the shelf, and salinity transport and stratication properties within estuaries. Research ndings lead to a better understanding of estuarine response under a wide range of atmospheric conditions, and the resulting technologies will be useful for oil spill response operations, fisheries and pollution management.

R Cyriac (2018). “Wind and Plume Driven Circulation in Estuarine Systems,” North Carolina State University.

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.

Development and Application of Coupled Hurricane Wave and Surge Models for Southern Louisiana

DissertationCoastal Louisiana and Mississippi are especially prone to large hurricanes due to their geographic location in the north-central Gulf of Mexico. Several recent hurricanes have devastated the region, creating complicated environments of waves and storm surge. Katrina (2005) and Gustav (2008) made landfall in southeastern Louisiana, and their counter-clockwise winds pushed surge onto the Louisiana-Mississippi continental shelf, into the low-lying wetlands surrounding the Mississippi River, and over and through the levee system that protects metropolitan New Orleans. Rita (2005) and Ike (2008) passed farther to the west, moved across the Texas-Louisiana continental shelf, and created surge that flooded large portions of southwestern Louisiana.

These hurricanes demand detailed hindcasts that depict the evolution of waves and surge during these storm events. These hindcasts can be used to map the likely floodplains for insurance purposes, to understand how the current protection system responded during each storm, and to design a new protection system that will resist better the waves and surge. In addition, the resulting computational model can be used to forecast the system’s response to future storm events.

The work described herein represents a significant step forward in the modeling of hurricane waves and surge in complicated nearshore environments. The system is resolved with unprecedented levels of detail, including mesh sizes of 1km on the continental shelf, less than 200m in the wave breaking zones and inland, and down to 20-30m in the fine-scale rivers and channels. The resulting hindcasts are incredibly accurate, with close matches between the modeled results and the measured high-water marks and hydrograph data. They can be trusted to provide a faithful representation of the evolution of waves and surge during all four hurricanes.

This work also describes advancements in the coupling of wave and surge models. This coupling has been implemented typically with heterogeneous meshes, which is disadvantageous because it requires intra-model interpolation at the boundaries of the nested, structured wave meshes and inter-model interpolation between the wave and circulation meshes. The recent introduction of unstructured wave models makes nesting unnecessary. The unstructured-mesh SWAN wave and ADCIRC circulation models are coupled in this work so that they run on the same unstructured mesh. This identical, homogeneous mesh allows the physics of wave-circulation interactions to be resolved correctly in both models. The unstructured mesh can be applied on a large domain to follow seamlessly all energy from deep to shallow water. There is no nesting or overlapping of structured wave meshes, and there is no inter-model interpolation. Variables and forces reside at identical, vertex-based locations. Information can be passed without interpolation, thus reducing significantly the communication costs.

The coupled SWAN+ADCIRC model is highly scalable and integrates seamlessly the physics and numerics from deep ocean to shelf to floodplain. Waves, water levels and currents are allowed to interact in complex problems and in a way that is accurate and efficient to thousands of computational cores. The coupled model is validated against extensive measurements of waves and surge during the four recent Gulf hurricanes. Furthermore, the coupling paradigm employed by SWAN+ADCIRC does not interfere with the already-excellent scalability of the component models, and the coupled model maintains its scalability to 7,168 computational cores. SWAN+ADCIRC is well-suited for the simulation of hurricane waves and surge.

JC Dietrich (2010). “Development and Application of Coupled Hurricane Wave and Surge Models for Southern Louisiana,” University of Notre Dame.

Implementation and Assessment of ADCIRC’s Wetting and Drying Algorithm

ThesisHydrodynamic models are used for a variety of purposes, such as the modeling of hurricane storm surges, the study of tidal circulation patterns, and the planning of naval fleet operations. One such hydrodynamic model is ADCIRC (ADvanced CIRCulation), which was developed more than 20 years ago and has been refined continuously by researchers across North America. ADCIRC is based on the shallow water equations and includes many of the features necessary to model complex hydrodynamic systems. However, some of these features were implemented in an attempt to solve specific problems, and their behaviors were never rigorously assessed. For instance, the model uses a wetting and drying algorithm to simulate the ebb and flow of tides in coastal regions. This behavior is important in many applications, and it must be modeled correctly. This research thesis will: (1) refute an attack on the usefulness of the finite volume method for computing mass balance errors, (2) lay the groundwork for a future study that will automate the placement of grid points based on a minimization of local mass balance error, (3) implement and assess the wetting and drying algorithm in one-, two-, and three-dimensional versions of the ADCIRC model, (4) identify a set of optimal parameters for wetting and drying simulations, (5) prove that recent updates to the wetting and drying algorithm were beneficial, and (6) show that smaller mass balance errors are obtained when they are computed for each vertical element in the water column.

JC Dietrich (2005). “Implementation and Assessment of ADCIRC’s Wetting and Drying Algorithm,University of Oklahoma.