Tag Archives: Casey Dietrich
Analytic Solution for Wind-Driven Setup
In their manuscript “Analytic Solutions for Computer Flow Model Testing,” Lynch and Gray present solutions for water levels and depth-averaged velocities, for tidal and/or wind forcing, and for Cartesian and polar domains. These solutions have been useful for model validation, especially for tides, and especially within the ADCIRC community — the first example problem in the ADCIRC documentation is based on one of their solutions. That problem, for tidal flows in a polar domain, has been used to validate several model advancements over the years.
However, we found an error in their solution for wind-driven setup on a polar domain. It appears to be a typographical error — the variables are not updated correctly at the last step, when the solution is generalized for a wind with arbitrary direction. This solution is not used frequently, and we did not find a correction to this error in the literature (although we were unable to access every subsequent manuscript that cited the Lynch and Gray solution). So we are documenting it here.
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.
News: Modeling Florence’s Storm Surge
2019/04/26 – NCSU College of Engineering
After the Storm
After the Storm
Dr. Casey Dietrich, an assistant professor in the Department of Civil, Construction, and Environmental Engineering (CCEE), leads the Coastal and Computational Hydraulics Team and develops computational models that predict storm surge and coastal flooding. Using the model ADCIRC, the team makes predictions about how high sea waters will rise, which areas will be flooded and for how long. These predictions are made for the entire coastline, and then his team visualizes the flooding at the scales of individual buildings and coastal infrastructure. During Florence, Dietrich’s team and collaborators acted as liaisons for state emergency managers to aid their decision making.
“The models are just one data point among many, but they’re helpful in understanding hazards and used to make predictions in real time — partly to make decisions about evacuation, where to deploy resources after, safe places to put emergency vehicles and water supplies,” he said.
The state emergency managers are able to use the flooding predictions to get immediate estimates on damages, which helps communities that are figuring out how much recovery will cost.
After Hurricane Matthew in 2016, Dietrich and his colleagues improved the models’ ability to forecast encroaching water along shorelines. Post-Florence, Dietrich said the research focus is to speed up the model and allow for more permutations to see what might happen if a storm slows down or shifts direction.
Team Photo 2019
Front (left to right): Carter Howe, Autumn Poisson, Alireza Gharagozlou, and Johnathan Woodruff. Back (left to right): Tucker Fulle, Ajimon Thomas, Casey Dietrich, and Carter Rucker. Not shown: Chloe Stokes.
Conference: FEF 2019
Downscaling ADCIRC Flooding Inundation Extents Using Kalpana
Updated 2020/06/24: Added documentation for --growradius none option.
Updated 2020/04/15: Added documentation for DEM vertical unit conversions.
The ADCIRC modeling system is used often to predict coastal flooding due to tropical cyclones and other storms. The model uses high resolution to represent the coastal environment, including flow pathways (inlets, man-made channels, rivers) and hydraulic controls (barrier islands, raised features). However, due to the use of large domains to represent hazards on coastlines in an entire state or multiple states, the highest resolution is typically about 20 to 50 m in coastal regions. Thus, there is a potential gap between the flooding predictions and the true flooding extents. We have developed a geospatial software to downscale the flooding extents to higher resolution.
ADCIRC vs. downscaled water levels, plan view. This image shows the difference in prediction of flooding extents, with the blue portion representing the original ADCIRC flooding extents and red representing the downscaled extents.
The following documentation is for downscaling the flooding predictions by using Kalpana. This software was created originally to view ADCIRC outputs as either ESRI shapefiles or KML files (for viewing in Google Earth). ADCIRC (the ADvanced CIRCulation model) uses finite element methods to predict water levels throughout the modeled domain. Although this model is able to provide accurate predictions in a matter of minutes, these predictions have a limited resolution and are not able to provide information at the scale of buildings, roadways, and other critical infrastructure.
Advancing the Understanding of Storm Processes and Impacts
In 2017, Hurricanes Harvey, Irma, and Maria caused more than $200 billion dollars of damage in the United States, as well as the incalculable cost of the loss of life and mental trauma associated with these disasters. In a changing climate, sea level rise and the potential for increasing tropical cyclone intensity can result in even more devastating damages. Therefore, engineers, community planners, and coastal residents need accurate, timely, and accessible forecasting of storm processes and their impact on coastal communities to bolster national resilience and reduce risk to life and property during these events. However, along with uncertainties in understanding and modeling of storm processes, there are complex challenges associated with determining and meeting the needs of end users who rely on these forecasts for emergency management decisions.
To determine needed advancements in storm forecasting, the U.S. Coastal Research Program (USCRP) hosted a Storm Processes and Impacts workshop for coastal stakeholders 16-18 April 2018, in St. Petersburg, Florida. The attendees included local coastal managers, emergency managers, state and regional agencies, federal agency scientists and engineers, academics, and private industry scientists and engineers. Workshop objectives were to synthesize present capabilities for modeling storm processes and forecasting impacts and to prioritize advancements. In addition, the workshop provided an opportunity to bridge the apparent gap between the research of coastal scientists and engineers and the information being distributed publicly and to emergency managers before, during, and after storm events.