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.

Differences between SWAN v41.31 and v41.10

Updated 2020/06/23: Adjusted for new SWAN setting with NSWEEP=1.

In late May 2019, the SWAN developers released a new version. Whenever this happens, the new version needs to be implemented into the coupled SWAN+ADCIRC, thus replacing an older version in the coupled model.

Starting with the upcoming release version 55 of ADCIRC, the coupled SWAN has been upgraded to its latest release version 41.31. It replaces the older version 41.10.

This upgrade is mostly a benefit to users of SWAN+ADCIRC. It has been almost 4 years since the last upgrade, and we had skipped a new SWAN version (41.20) during that time. Thus, this upgrade is adding features and bug fixes from two newer versions (41.20 and 41.31). SWAN has added several capabilities that will be advantageous to users of SWAN+ADCIRC.

However, a few of its changes will cause differences in the wave predictions, as described below. Users will likely need to re-calibrate their input settings for SWAN.

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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.

Sustainability of Barrier Island Protection Policies under Changing Climates

This project will address methods to adapt beach and dune nourishment to improve resilience in a changing climate. As storms become more powerful and seas continue to rise, major erosion events will occur more frequently. However, coastal communities do not yet understand how to evaluate their increasing vulnerabilities and adapt their long-term planning. In this project, we will identify the climate patterns that most often trigger the need to nourish, the variability of the time interval between such nourishments, and the economic costs and sediment volumes necessary to maintain this coastal protection policy into the 21st century.

A stochastic climate emulator will first be developed to simulate 1000s of realizations of chronological climate patterns (forced by satellite and GCM products) to create future storm events coupled with sea level rise scenarios. A library of high fidelity, open source, hydrodynamic and morphodynamic simulations (ADCIRC+SWAN and XBeach) will then be used to develop a surrogate model to predict erosion and flooding for each future realization. Triggers like beach width, dune height, and community preferences will be used to identify how often communities will need to re-nourish, contingent on future climate and sea level rise scenario.

JC Dietrich, DL Anderson. “Sustainability of Barrier Island Protection Policies under Changing Climates.” U.S. Coastal Research Program, 2019 Academic Research Opportunities, 2019/10/18 to 2021/10/17, $226,624 (Dietrich: $226,624).

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.