Identifying the Drivers of Chronic Coastal Flooding: A Community-Centric Approach

This project will address the problem of recurrent, shallow flooding in low-lying coastal communities. As local sea level rise (SLR), land subsidence, and heavy rainfall events increase, so does the frequency of flooding in low-lying coastal areas. The tidal cycle now takes place on higher average sea levels, resulting in “sunny-day” flooding of roadways during high tides. Sea water also infiltrates stormwater drainage systems at low tidal levels, such that ordinary rainstorms lead to flooding. While these minor floods draw less attention than catastrophic storms, their high frequency imposes a chronic stress on coastal communities and economies by disrupting critical infrastructure services. However, information on recurrent, minor floods is scarce because they are hyper-local – often affecting a city block or intersection at a time – and relatively brief in duration. Further, prediction of chronic flooding is hindered by a poor understanding of subterranean contributions, non-tidal hydrodynamic contributions, and their interactions with tides and SLR. This problem is particularly critical for (but not unique to) communities in North Carolina (NC), many of which experience chronic flooding during non-King tide events and are seeking engineered interventions for adaptation.

The proposed work integrates outreach and research activities over the two-year project period to improve our prediction and communication of chronic flood hazards to stakeholders in the Town of Carolina Beach, NC (CB), a community plagued by chronic flooding. We will couple an existing high-resolution hydrodynamic model with a stormwater management model to hindcast and test hypotheses on the drivers of chronic, and sometimes unexpected, flood events in CB. In parallel, we will deploy a real-time flood sensor network in CB to continuously measure the stormwater network capacity and fill data gaps on the incidence and causes of chronic flooding. In response to the expressed need from local officials, we will also use the in-situ data to develop an early-warning system and engage community members to co-develop flood-mitigation design scenarios for future testing using the new model framework.

K Anarde, M Hino, A Gold, JC Dietrich. “Identifying the drivers of chronic coastal flooding: a community-centric approach.National Oceanic and Oceanic Administration, North Carolina Sea Grant, 2022/02/01 to 2024/01/31, $119,411.

Ajimon’s Paper Selected as Editor’s Choice

Our recent paper, “Effects of Model Resolution and Coverage on Storm-Driven Coastal Flooding Predictions,” was selected as the Editor’s Choice by the Journal of Waterway, Port, Coastal and Ocean Engineering. The chief editor selects a paper from the current issue. The paper is made free with registration and featured on the journal home page for two months, after which it will continue to be featured in the Editor’s Choice Collection.

Congratulations to Ajimon!

Tomás Cuevas López

Updated 2022/03/21

M.S. Student (Graduate Research Assistant)
Department of Civil, Construction and Environmental Engineering
North Carolina State University
Fitts-Woolard Hall, Room 3121
915 Partners Way
Raleigh, NC 27606

Hola! I’m a first-year Master’s student in the Coastal and Computational Hydraulics Team (CCHT) at NC State. I am from Chile, a country with a long history of natural disasters, and some of them related to the ocean. As a kid, I always wanted to study civil engineering. After the devastating 2010 tsunami that hit Chile, hydraulics engineering came into my mind.

I did my undergrad studies at the Faculty of Physical and Mathematical Sciences (FCFM) at the Universidad de Chile. During my bachelor’s, I only had one course related to coastal engineering, but after that course, I decided to do my last summer internship at PRDW where I had my first experience doing numerical modeling of coastal processes. Right after that, I started my thesis under the supervision of two PRDW engineers and a faculty professor. The goal of the work was to develop a series of python scripts to correlate and correct the results of wave hindcast models with satellite altimetry.

After my graduation, I joined the numerical modeling team of PRDW of the Chilean office, where I worked from July 2016 to December 2021. I was involved in a large range of coastal processes studies such as wave climate, wave agitation, sediment transport, hydrodynamics, moored vessel’s dynamics, CFD, etc. The other area where I developed some expertise is in Python programming, I started coding for my thesis and never stopped, I’m a data science enthusiast! My experience on this can be split into three main areas: (1) managing environmental datasets, like waves, wind, temperature, salinity, etc, (2) development of scripts and tools written in python related to coastal engineering, and (3) applying machine and deep learning models or techniques to subjects related to coastal engineering.

I am currently working on the DHS project “Improving the Efficiency of Wave and Surge Models via Adaptive Mesh Resolution.”

My main areas of interest are numerical modeling of coastal processes, applying machine learning (ML) in coastal processes to improve the time-performance and accuracy of the numerical modeling. I’m also interested in statistics and probabilistic methods since they allow handling the uncertainties of the models. And at last, in the combination of GIS tools with both ML and the outputs of coastal models, and in improving the communication of the numerical modeling results to allow better risk management and a resilient use of the coastal environment.

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Effects of Model Resolution and Coverage on Storm-Driven Coastal Flooding Predictions

Predictions of storm surge and flooding require models with higher resolution of coastal regions, to describe fine-scale bathymetric and topographic variations, natural and artificial channels, flow features, and barriers. However, models for real-time forecasting often use a lower resolution to improve efficiency. There is a need to understand how resolution of inland regions can translate to predictive accuracy, but previous studies have not considered differences between models that both represent conveyance into floodplains and are intended to be used in real time. In this study, the effects of model resolution and coverage are explored using comparisons between forecast-ready and production-grade models that both represent floodplains along the US southeast coast, but with typical resolutions in coastal regions of 400 and 50 m, respectively. For two storms that impacted the US southeast coast, it is shown that, although the overall error statistics are similar between simulations on the two meshes, the production-grade model allowed a greater conveyance into inland regions, which improved the tide and surge signals in small channels and increased the inundation volumes between 40% and 60%. Its extended coverage also removed water level errors of 20–40 cm associated with boundary effects in smaller regional models.

A Thomas, JC Dietrich, CN Dawson, RA Luettich (2022). “Effects of Model Resolution and Coverage on Storm-Driven Coastal Flooding Predictions.” Journal of Waterway, Port, Coastal, and Ocean Engineering, 148(1), 04021046, DOI: 10.1061/(ASCE)WW.1943-5460.0000687.

Impact of Storm Events on Density Stratification in the Pamlico and Albemarle Estuarine System

Tropical cyclones and other coastal storms have multiple effects on estuaries. They create storm surge, or the rise of water levels above the normal tides, which can cause flooding of coastal areas, including communities near estuaries. They can also alter ecosystems, including in estuaries with changes to nutrient loading and regeneration, abrupt changes in salinity, increases in the mixed-layer depth, decreases in sea-surface temperature, and breakdowns in water column stratification. The interactions between surge and estuarine circulation can enhance the storm effects. And with the increasing intensity of tropical cyclones, these effects will be further enhanced.

Numerical models can represent the coastal environment and its response to the combined effects of tides, river flows, and winds. It is especially challenging for numerical models to represent the response of estuaries to storms, due to the complex interactions of fresh and saline waters, and thus relatively few studies have used models to represent both storm- and density-driven circulation in estuaries. These few studies have shown that salinities and temperatures of estuaries can change significantly during storms and may require weeks to recover, depending on the amount of freshwater discharge. However, these studies have been limited in number and geographic coverage, relied on coupling to other models for baroclinic inputs, did not have the estuarine mixing and stratification as a focus, or were missing physics. Much is still uncertain about how estuarine circulation evolves during a storm event. How quickly do the horizontal salinities respond to the storm? How does the salinity transport vary through an estuary? How do freshwater discharges due to rainfall affect the mixing? Another uncertainty is the salinity response after the storm. How quickly does a system recover? Do the freshwater discharges interrupt the recovery? In this thesis, it is hypothesized that, for a large and shallow estuarine system with minimal connections to the open ocean, the storm forcing will cause large brackish and freshwater intrusions and recoveries that vary through the system.

To investigate this hypothesis, we developed a three-dimensional model of storm- and density-driven circulation in the Albemarle-Pamlico Estuarine System (APES) in North Carolina. Irene (2011) was used as the basis for storm event simulations to examine the evolution of the horizontal salinity distribution. Included in this model were hurricane-strength winds and pressures, tides, river discharges, and density circulation. Using this model, it was determined that during Irene, APES experienced movements of brackish water into the estuaries and saline water into the sounds. These movements were heavily dependent on the winds. After the stormsimulation, the large river discharges produced intrusions of fresher water into major areas of the sound, and after two weeks, the system was not fully regulated.

From this research, we have developed a better understanding of the horizontal salinity distribution of APES as well as how the system reacts to a single storm event. This research allows for future studies to consider different types of storms along with refinement of the river forcings, to understand better the full range of estuarine responses.

BA Rumbaugh (2021). “Impact of storm events on density stratification in the Pamlico and Albemarle Estuarine System,” North Carolina State University.

Improved Predictions with ST6 Physics and SWAN Version 41.31

These analyses were performed by Carter Day, an undergraduate researcher in our team.

Like other spectral wave models, SWAN uses parameterizations to represent sources (e.g. wind), sinks (e.g. whitecapping, bottom friction, depth-limited breaking), and resonance (e.g. quadruplet and triad wave-wave interactions). Each parameterization is based on laboratory and experimental data to represent the transfer of energy to, from, and between waves. It is necessary for each term to represent its physical process, but it is also necessary for the terms to be calibrated collectively to represent their combined effects on wave evolution.

SWAN release version 41.31 was modified in two main ways: derivative computation was changed to use the Green-Gauss formula, and a new ‘package’ of wave physics (the so-called ST6 physics) was introduced. This package includes new parameterizations of wind input, whitecapping, swell dissipation, wind speed scaling, and other processes. The ST6 physics have been adopted by other wave models (e.g. NOAA’s WaveWatch III), and it will likely become the preferred physics package for SWAN. However, because the ST6 physics package has changes to so many parameterizations, it is necessary to quantify its effects on wave predictions during recent storms.

In this study, we simulate two recent hurricanes, Gustav (2008) and Florence (2018), and we compare wave predictions with the new ST6 physics package. Do the ST6 physics improve the SWAN wave predictions?

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News: Jessica Gorski Featured in Lenovo Video

2021/11/15 – Intel + Lenovo
Coastal Computing

Our research into real-time erosion predictions using XBeach was featured in a recent video by Lenovo and CNN. Jessica Gorski describes how we are exploring the use of 1D transect models to predict erosion during storms.

Lenovo provides hardware and support for the HPC services at NC State. The video was produced as branded content for CNN, and it was featured on the CNN web site and social media.

The video required two days of shooting with a team of directors, photographers, audio specialists, and production assistants. Click below to see photos of the production.

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