Document Type
Article
Publication Date
7-2020
Abstract
The intensification of climatic changes, mainly natural geophysical hazards like hurricanes, are of great interest to the South Texas region. Scientists and engineers must protect essential resources from coastal threats, such as storm surge. This study presents the development process and improvements of a hydrodynamic finite element model that covers the South Texas coast, specifically the Lower Laguna Madre, for the aid of local emergency management teams. Four historical tropical cyclone landfalls are evaluated and used as a means of verification of the hydrodynamic model simulation results. The parameters used to improve the accuracy of the model are the tidal harmonic constituents and the surface roughness coefficient, or manning’s n value. A total of four different scenarios that use a variety of tidal constituent combinations and nodal attribute files were developed to identify the best case. Statistical evaluation, such as regressionanalysis, normalized root mean square error, and scatter index, was used to determine the significance of each hydrodynamic computational storm surge result with observed historical water surface elevations. In an effort to improving all models locally, using seven tidal constituents combinations along with a surface roughness nodal attribute grid that assigns values with respect to bathymetric data improves the accuracy of the storm surge model and should, therefore, beimplemented for future hydrodynamic studies in the South Texas region.
Recommended Citation
Sara E. Davila, Cesar Davila Hernandez, Martin Flores, Jungseok Ho. South Texas coastal area storm surge model development and improvement. AIMS Geosciences, 2020, 6(3): 271-290. doi: 10.3934/geosci.2020016
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
First Page
271
Last Page
290
Publication Title
AIM Geosciences
DOI
10.3934/geosci.2020016
Comments
© 2020 the Author(s), licensee AIMS Press. Original published version available at doi.org/10.3934/geosci.2020016