Category Archives: Models
Use of NetCDF-Formatted Wind Fields from OWI in ADCIRC
This new OWI file format was developed by Alex Crosby and his team at Oceanweather Inc. Most of the ADCIRC code to handle these new input files was implemented by Casey Dietrich.
ADCIRC has now been updated to allow the usage of NetCDF-formatted atmospheric fields from Oceanweather Inc. (OWI). In this new format, the surface pressure and wind fields have flexibility to represent different storms with different fields, to track storms with moving fields, and to vary resolution of the fields in both space and time. These updates have been added to the latest development version of ADCIRC, and they will be available in the next release version. These new fields are read by ADCIRC using the NWS=13 parameter and a new input file.
The following animation shows the use of this new file format in an ADCIRC simulation for the wind fields due to Hurricane Charley (1999). Note that Charley is one of several storms during this period, and each storm is represented by a moving field overlaid on a coarser background field.
In the rest of this page, we describe the new input file format, how it is used in ADCIRC, and then provide a set of example files.
Johnathan wins Student Educational Award
Ph.D. student Johnathan Woodruff won the Student Educational Award from the American Shore & Beach Preservation Association. This award is given annually to an undergraduate or graduate student who, through his or her research, is furthering the state of science of coastal systems as it relates to the goals and mission of the ASBPA. The award includes a $500 cash stipend. Johnathan wil attend the ASBPA National Coastal Conference next week to present his findings to the coastal community.
CCHT Ph.D. student Johnathan Woodruff.
Presentation: SECASC Coastal Resilience Working Group
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.
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.
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.
Ajimon Thomas defends PhD Dissertation
Again because of the pandemic, we gathered virtually for a successful PhD defense by Ajimon Thomas. Most attendees joined online, and Ajimon shared his research with his fellow students and faculty committee. Congrats to Ajimon!
