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.
Barrier islands are susceptible to erosion, overwash, and breaching during intense storms. However, these processes are not represented typically in large-domain models for storm surge and coastal inundation. In this study, we explore the requirements for bridging the gap between dune-scale morphodynamic and region-scale flooding models. A high-resolution XBeach model is developed to represent the morphodynamics during Hurricane Isabel (2003) in the North Carolina (NC) Outer Banks. The model domain is extended to more than 30 km of Hatteras Island and is thus larger than in previous studies. The predicted dune erosion is in good agreement with post-storm observed topography, and an ‘‘excellent’’ Skill Score of 0.59 is obtained on this large domain. Sensitivity studies show the morphodynamic model accuracy is decreased as the mesh spacing is coarsened in the cross-shore direction, but the results are less sensitive to the alongshore resolution. A new metric to assess model skill, Water Overpassing Area (WOA), is introduced to account for the available flow pathway over the dune crest. Together, these findings allow for upscaled parameterizations of erosion in larger-domain models. The updated topography, obtained from XBeach prediction, is applied in a region-scale flooding model, thus allowing for enhanced flooding predictions in communities along the Outer Banks. It is found that, even using a fixed topography in region-scale model, the flooding predictions are improved significantly when post-storm topography from XBeach is implemented. These findings can be generalized to similar barrier island systems, which are common along the U.S. Gulf and Atlantic coasts.
Missing building attributes are problematic for development of data-based fragility models. Relative to other disciplines, the application of imputation techniques is limited in the field of engineering. Current imputation techniques to replace missing building attributes lack evaluations of imputation model performance, which ensure accuracy and validity of the imputed data. This paper presents two imputation approaches, along with imputation diagnostic and comparison approaches, for binary building attribute data with missing observations. Predictive mean matching (PMM) and multiple imputation (MI) are used to impute foundation type and number of stories attributes. The diagnostic approach, based on the logistic regression goodness-of-fit test, is used to evaluate the imputation model fit. The comparison approach, based on the percentage of correctly imputed observations, is used to evaluate the imputation model performance. A data set of single-family homes damaged by the 2005 Hurricane Katrina is used to demonstrate implementation of the methodology. Based on the comparison approach, PMM models showed 9% and 2% greater accuracy than MI models in imputing foundation type and number of stories, respectively.
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.
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.