Document Type
Article
Department/Program
Virginia Institute of Marine Science
Publication Date
7-26-2019
Journal
Journal of Marine Science and Engineering
Volume
7
Issue
8
First Page
242
Abstract
Changes in the eustatic sea level have enhanced the impact of inundation events in the coastal zone, ranging in significance from tropical storm surges to pervasive nuisance flooding events. The increased frequency of these inundation events has stimulated the production of interactive web-map tracking tools to cope with changes in our changing coastal environment. Tidewatch Maps, developed by the Virginia Institute of Marine Science (VIMS), is an effective example of an emerging street-level inundation mapping tool. Leveraging the Semi-implicit Cross-scale Hydro-science Integrated System Model (SCHISM) as the engine, Tidewatch operationally disseminates 36-h inundation forecast maps with a 12-h update frequency. SCHISM’s storm tide forecasts provide surge guidance for the legacy VIMS Tidewatch Charts sensor-based tidal prediction platform, while simultaneously providing an interactive and operationally functional forecast mapping tool with hourly temporal resolution and a 5 m spatial resolution throughout the coastal plain of Virginia, USA. This manuscript delves into the hydrodynamic modeling and geospatial methods used at VIMS to automate the 36-h street-level flood forecasts currently available via Tidewatch Maps, and the paradigm-altering efforts involved in validating the spatial, vertical, and temporal accuracy of the model.
Supplementary material: Catch the King Tide GPS data points were collected by volunteers to effectively breadcrumb their path tracing the tidal high water contour lines by pressing the 'Save Data' button in the free Sea Level Rise Mobile App every few steps along the water's edge during the high tide on the morning of November 5th, 2017. https://doi.org/10.25773/276h-2b45
DOI
doi: 10.3390/jmse7080242
Keywords
hydrodynamic; modeling; sea level rise; mobile application; app; crowdsourcing; SCHISM; Tidewatch; StormSense; Catch the King
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Loftis, Jon Derek; Mitchell, Molly; Schatt, Daniel; Forrest, David R.; Wang, Harry V.; Mayfield, David; and Stiles, William A., Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA (2019). Journal of Marine Science and Engineering, 7(8), 242.
doi: 10.3390/jmse7080242
