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.

Storm-Driven Erosion and Inundation of Barrier Islands from Dune- to Region-Scales

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.

A Gharagozlou, JC Dietrich, A Karanci, RA Luettich, MF Overton (2020). “Storm-driven erosion and inundation of barrier islands from dune- to region-scales.” Coastal Engineering, 158, 103674, DOI: 10.1016/j.coastaleng.2020.103674

Binary Building Attribute Imputation, Evaluation, and Comparison Approaches for Hurricane Damage Data Sets

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.

CC Massarra, CL Friedland, BD Marx, JC Dietrich (2020). “Binary Building Attribute Imputation, Evaluation, and Comparison Approaches for Hurricane Damage Data Sets.” Journal of Performance of Constructed Facilities, 34(3), 04020036, DOI: 10.1061/(ASCE)CF.1943-5509.0001433.

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 Tide Effects on the Choctawhatchee Bay Plume and Implications for Surface Transport at Destin Inlet

Multiple river-dominated estuaries line the northern Gulf coast and introduce substantial density variations. Their plumes have been shown to be highly sensitive to wind and tide effects, but in studies with limited observations and idealized wind forcing. This study explores these effects with a dynamic model that can represent the full behavior from river through estuary to shelf, and for a period with extensive observations. The inner shelf adjacent to Choctawhatchee Bay, a micro tidal estuary situated along the Florida Panhandle, is subject to buoyant, brackish outflows during the ebb-phase of the tidal cycle.

In December 2013, experiments were performed in this region to study mechanisms that influence near-shore surface transport. 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. Density-driven flows near Destin Inlet are modeled with the recently-enhanced, three-dimensional, baroclinic capabilities of the ADvanced CIRCulation (ADCIRC) model. Modeled tides, salinities and plume signature are validated against in-situ observations and satellite imagery. Model results reveal substantial changes in the length, width and orientation of the plume as the wind direction varied on consecutive days due to winter cold fronts. During a period of near-constant winds and variability in tidal amplitude, the model predicted a larger plume during spring tides than during neap conditions. Coriolis effects on the plume are minimized due to its small scale nature. Therefore, when the wind forcing is weak, the plume signature spreads radially from the inlet with slight preference to the down-shelf. The Choctawhatchee Bay plume is representative of other small-scale plumes formed in river-dominated and micro-tidal environments, and this work demonstrates the sensitivity of these plumes to changing environmental conditions.

R Cyriac, JC Dietrich, CA Blain, CN Dawson, KM Dresback, A Fathi, MV Bilskie, HC Graber, SC Hagen, RL Kolar (2019). “Wind and tide effects on the Choctawhatchee Bay plume and implications for surface transport at Destin Inlet.” Regional Studies in Marine Science, 35, 101131, DOI: 10.1016/j.rsma.2020.101131.

Subgrid Theory for Storm Surge Modelling

Averaging techniques are used to generate upscaled forms of the shallow water equations for storm surge including subgrid corrections. These systems are structurally similar to the standard shallow water equations but have additional terms related to integral properties of the fine-scale bathymetry, topography, and flow. As the system only operates with coarse-scale variables (such as averaged fluid velocity) relating to flow, these fine-scale integrals require closures to relate them to the coarsened variables. Closures with different levels of complexity are identified and tested for accuracy against high resolution solutions of the standard shallow water equations. Results show that, for coarse grids in complex geometries, inclusion of subgrid closure terms greatly improves model accuracy when compared to standard solutions, and will thereby enable new classes of storm surge models.

AB Kennedy, D Wirasaet, A Begmohammadi, T Sherman, D Bolster, JC Dietrich (2019). “Subgrid Theory for Storm Surge Modelling.” Ocean Modelling, 144, 101491, DOI: 10.1016/ocemod.2019.101491.

Predictive Multi-Hazard Hurricane Data-Based Fragility Model for Residential Homes

Multi-hazard hurricane data-based fragility models are able to represent multiple predictor variables, be validated based on observed data, and consider variability in building characteristics and hazard variables. This paper develops predictive hurricane, multi-hazard, single-family building fragility models for ordered categorical damage states (DS) and binary complete failure/non-complete failure using proportional odds cumulative logit and logistic regression models, respectively. In addition to their simplicity, these models are able to represent multiple hurricane hazard variables and include variable interactions, thus improving model fitting and damage prediction. Surveys of physical damage in coastal Mississippi following Hurricane Katrina (2005) and high-resolution numerical hindcast hazard intensities from the Simulating WAves Nearshore and ADvanced CIRCulation (SWAN + ADCIRC) models are used as model input. Prediction accuracy is expressed in terms of cross-validation (CV) and evaluated using leave-one-out cross-validation (LOOCV).

Thirty-nine combinations of global damage response variables were investigated. Of these models, six DS and one complete failure model met the evaluation criteria. Maximum significant wave height was the only significant hazard variable for the DS models, while maximum 3-s gust wind speed, maximum surge depth, and maximum water speed were found to be significant predictors for the complete failure model. Model prediction external accuracy ranged from 81% to 87%.

CC Massarra, CJ Friedland, BD Marx, JC Dietrich (2019). “Predictive Multi-Hazard Hurricane Data-Based Fragility Model for Residential Homes.” Coastal Engineering, 151, 10-21, DOI: 10.1016/j.coastaleng.2019.04.008.

Influence of Storm Timing and Forward Speed on Tides and Storm Surge during Hurricane Matthew

The amount and extent of coastal flooding caused by hurricanes can be sensitive to the timing or speed of the storm. For storms moving parallel to the coast, the hazards can be stretched over a larger area. Hurricane Matthew was a powerful storm that impacted the southeastern U.S. during October 2016, moving mostly parallel to the coastline from Florida through North Carolina. In this study, three sources for atmospheric forcing are considered for a simulation of Matthew’s water levels, which are validated against extensive observations, and then the storm’s effects are explored on this long coastline. It is hypothesized that the spatial variability of Matthew’s effects on total water levels is partly due to the surge interacting nonlinearly with tides. By changing the time of occurrence of the storm, differences in storm surge are observed in different regions due to the storm coinciding with other periods in the tidal cycles. These differences are found to be as large as 1m and comparable to the tidal amplitude. 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. With respect to the forward speed, the present study contributes to established results by considering the scenario of a shore-parallel hurricane. A faster storm caused an increase in peak water levels along the coast but a decrease in the overall volume of inundation. On the other hand, a slower storm pushed more water into the estuaries and bays and flooded a larger section of the coast. Implications for short-term forecasting and long-term design studies for storms moving parallel to long coastlines are discussed herein.

A Thomas, JC Dietrich, TG Asher, M Bell, BO Blanton, JH Copeland, AT Cox, CN Dawson, JG Fleming, RA Luettich (2019). “Influence of Storm Timing and Forward Speed on Tide-Surge Interactions during Hurricane Matthew.” Ocean Modelling, 137, 1-19, DOI: 10.1016/j.ocemod.2019.03.004.