ArcWaT: a model-based cell-by-cell GIS toolbox for estimating wave transformation during storm surge events

Abstract

Quantifying the spatially varying nearshore wave characteristics and energy dissipation mechanisms is of utmost importance for several coastal management and engineering applications as well as for flood hazard assessment. This study presents the ArcGIS Wave Transformation toolbox (ArcWaT), a model-based GIS toolbox for estimating wave transformation from wave magnitude and direction model outputs. In order to assess the ArcWaT capabilities, a case study was developed using ADCIRC + SWAN model outputs from a highly-resolved numerical mesh developed for the nearshore areas of the state of Maryland and forced with wind and pressure fields from Hurricane Irene. Results show that several wave transformation processes, i.e., wave generation, shoaling, refraction, wave breaking, diffraction, and wave attenuation due to bottom friction, are more easily identified when using ArcWaT if compared to the non-processed wave magnitude and direction model outputs. Among these processes wave attenuation due to bottom friction is found to be the most significant.

Keywords:

• Significant wave height
• wave attenuation
• ADCIRC + SWAN
• Chesapeake Bay
• nearshore processes

Acknowledgements

This research is part of the “EESLR 2019 Quantifying the benefits of natural and nature-based features in Maryland’s Chesapeake and Atlantic Coastal Bays to inform and management under future sea level rise scenarios” project funded by NOAA [grant number NA19NOS4780179]. The authors would like to thank NOAA for the scholarships to the first, third, and fifth authors. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the National Oceanic and Atmospheric Administration.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are openly available in George Mason University Dataverse at https://doi.org/10.13021/orc2020/1IWPSC.

Additional information

Funding

Financial support was provided to the second author by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. This research used the computational resources from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation and the Extreme Science and Engineering Discovery Environment (XSEDE) STAMPEDE2 resources through allocation id TG-BCS130009, which is supported by the National Science Foundation [grant number ACI- 1548562].