Multi-Hazard Hurricane Vulnerability Model to Enable Resilience-Informed Decision

Hurricanes or typhoons are multi-hazard events that usually result in strong winds, storm surge, waves, and debris flow. A community-level multi-hazard hurricane risk analysis approach is proposed herein to account for the combined impacts of hazards driven by hurricanes including surge, wave, and wind. A tightly coupled ADCIRC and SWAN model is used to account for the surge and wave hazard. Community-level exposure analysis is conducted using a portfolio of building archetypes associated with each hazard. A building-level hurricane vulnerability model is developed using fragility functions to account for content, building envelope, and structural damage. These fragility functions calculate the exceedance probability of predefined damage states associated with each hazard. Then, a building damage state is calculated based on the maximum probability of being in each damage state corresponding to each hazard. The proposed hurricane risk model is then applied to Waveland, Mississippi, a community that was severely impacted by Hurricane Katrina in 2005. The main contribution of this research is modeling the community-level hurricane vulnerability in terms of damage to the building envelope and interior contents driven by surge, wave, and wind using fragility functions to provide a comprehensive model for resilience-informed decision-making.

OM Nofal, JW van de Lindt, G Yan, S Hamideh, JC Dietrich (2021). “Multi-Hazard Hurricane Vulnerability Model to Enable Resilience-Informed Decision.” Proceedings of International Structural Engineering and Construction, S El-Baradei, A Abodonya, A Singh, S Yazdani (eds.), 8(1), DOI: 10.14455/ISEC.2021.8(1).RAD-01.

Multihazard Hurricane Fragility Model for Wood Structure Homes Considering Hazard Parameters and Building Attribute Interaction

Predicting building damage as a function of hurricane hazards, building attributes, and the interaction between hazard and building attributes is a key to understanding how significant interaction reflects variation hazard intensity effect on damage based on building attribute levels. This paper develops multihazard hurricane fragility models for wood structure homes considering interaction between hazard and building attributes. Fragility models are developed for ordered categorical damage states (DS) and binary collapse / no collapse. Exterior physical damage and building attributes from rapid assessment in coastal Mississippi following Hurricane Katrina (2005), high-resolution numerical hindcast hazard intensities from the Simulating WAves Nearshore and ADvanced CIRCulation (SWAN+ADCIRC) models, and base flood elevation values are used as model input. Leave-one-out cross-validation (LOOCV) is used to evaluate model prediction accuracy. Eleven and forty-nine combinations of global damage response variables and main explanatory variables, respectively, were investigated and evaluated. Of these models, one DS and one collapse model met the rejection criteria. These models were refitted considering interaction terms. Maximum 3-s gust wind speed and maximum significant wave height were found to be factors that significantly affect damage. The interaction between maximum significant wave height and number of stories was the significant interaction term for the DS and collapse models. For every 0.3 m (0.98 ft) increase in maximum significant wave height, the estimated odds of being in a higher rather than in a lower damage state for DS model were found to be 1.95 times greater for one- rather than for two-story buildings. For every 0.3 m (0.98 ft) increase in maximum significant wave height, the estimated odds of collapse were found to be 2.23 times greater for one- rather than for two-story buildings. Model prediction accuracy was 84% and 91% for DS and collapse models, respectively. This paper does not consider the full hazard intensity experienced in Hurricane Katrina; rather, it focuses on single-family homes in a defined study area subjected to wind, wave, and storm surge hazards. Thus, the findings of this paper are not applicable for events with hazards that exceed those experienced in the study area, from which the models were derived.

CC Massarra, CJ Friedland, BD Marx, JC Dietrich (2020). “Multihazard Hurricane Fragility Model for Wood Structure Homes Considering Hazard Parameters and Building Attributes Interaction.” Frontiers in Built Environment, 6, 147, DOI: 10.3389/fbuil.2020.00147.

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).